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Traore, Seydou. "Computational approaches toward protein design." Thesis, Toulouse, INSA, 2014. http://www.theses.fr/2014ISAT0033/document.
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Le Design computationnel de protéines, en anglais « Computational Protein Design » (CPD), est un champ derecherche récent qui vise à fournir des outils de prédiction pour compléter l'ingénierie des protéines. En effet,outre la compréhension théorique des propriétés physico-chimiques fondamentales et fonctionnelles desprotéines, l’ingénierie des protéines a d’importantes applications dans un large éventail de domaines, y comprisdans la biomédecine, la biotechnologie, la nanobiotechnologie et la conception de composés respectueux del’environnement. Le CPD cherche ainsi à accélérer le design de protéines dotées des propriétés désirées enpermettant le traitement d’espaces de séquences de large taille tout en limitant les coûts financier et humain auniveau expérimental.Pour atteindre cet objectif, le CPD requière trois ingrédients conçus de manière appropriée: 1) une modélisationréaliste du système à remodeler; 2) une définition précise des fonctions objectives permettant de caractériser lafonction biochimique ou la propriété physico-chimique cible; 3) et enfin des méthodes d'optimisation efficacespour gérer de grandes tailles de combinatoire.Dans cette thèse, nous avons abordé le CPD avec une attention particulière portée sur l’optimisationcombinatoire. Dans une première série d'études, nous avons appliqué pour la première fois les méthodesd'optimisation de réseaux de fonctions de coût à la résolution de problèmes de CPD. Nous avons constaté qu’encomparaison des autres méthodes existantes, nos approches apportent une accélération du temps de calcul parplusieurs ordres de grandeur sur un large éventail de cas réels de CPD comprenant le design de la stabilité deprotéines ainsi que de complexes protéine-protéine et protéine-ligand. Un critère pour définir l'espace demutations des résidus a également été introduit afin de biaiser les séquences vers celles attendues par uneévolution naturelle en prenant en compte des propriétés structurales des acides aminés. Les méthodesdéveloppées ont été intégrées dans un logiciel dédié au CPD afin de les rendre plus facilement accessibles à lacommunauté scientifiqueComputational Protein Design (CPD) is a very young research field which aims at providing predictive tools to complementprotein engineering. Indeed, in addition to the theoretical understanding of fundamental properties and function of proteins,protein engineering has important applications in a broad range of fields, including biomedical applications, biotechnology,nanobiotechnology and the design of green reagents. CPD seeks at accelerating the design of proteins with wanted propertiesby enabling the exploration of larger sequence space while limiting the financial and human costs at experimental level.To succeed this endeavor, CPD requires three ingredients to be appropriately conceived: 1) a realistic modeling of the designsystem; 2) an accurate definition of objective functions for the target biochemical function or physico-chemical property; 3)and finally an efficient optimization framework to handle large combinatorial sizes.In this thesis, we addressed CPD problems with a special focus on combinatorial optimization. In a first series of studies, weapplied for the first time the Cost Function Network optimization framework to solve CPD problems and found that incomparison to other existing methods, it brings several orders of magnitude speedup on a wide range of real CPD instancesthat include the stability design of proteins, protein-protein and protein-ligand complexes. A tailored criterion to define themutation space of residues was also introduced in order to constrain output sequences to those expected by natural evolutionthrough the integration of some structural properties of amino acids in the protein environment. The developed methods werefinally integrated into a CPD-dedicated software in order to facilitate its accessibility to the scientific community
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Wood, Christopher Robin Wells. "Computational design of parameterisable protein folds." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715832.
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Hong, Eun-Jong 1975. "Exact rotamer optimization for computational protein design." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44421.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (leaves 235-244).
The search for the global minimum energy conformation (GMEC) of protein side chains is an important computational challenge in protein structure prediction and design. Using rotamer models, the problem is formulated as a NP-hard optimization problem. Dead-end elimination (DEE) methods combined with systematic A* search (DEE/A*) have proven useful, but may not be strong enough as we attempt to solve protein design problems where a large number of similar rotamers is eligible and the network of interactions between residues is dense. In this thesis, we present an exact solution method, named BroMAP (branch-and-bound rotamer optimization using MAP estimation), for such protein design problems. The design goal of BroMAP is to be able to expand smaller search trees than conventional branch-and-bound methods while performing only a moderate amount of computation in each node, thereby reducing the total running time. To achieve that, BroMAP attempts reduction of the problem size within each node through DEE and elimination by energy lower bounds from approximate maximurn-a-posteriori (MAP) estimation. The lower bounds are also exploited in branching and subproblem selection for fast discovery of strong upper bounds. Our computational results show that BroMAP tends to be faster than DEE/A* for large protein design cases. BroMAP also solved cases that were not solvable by DEE/A* within the maximum allowed time, and did not incur significant disadvantage for cases where DEE/A* performed well. In the second part of the thesis, we explore several ways of improving the energy lower bounds by using Lagrangian relaxation. Through computational experiments, solving the dual problem derived from cyclic subgraphs, such as triplets, is shown to produce stronger lower bounds than using the tree-reweighted max-product algorithm.
(cont.) In the second approach, the Lagrangian relaxation is tightened through addition of violated valid inequalities. Finally, we suggest a way of computing individual lower bounds using the dual method. The preliminary results from evaluating BroMAP employing the dual bounds suggest that the use of the strengthened bounds does not in general improve the running time of BroMAP due to the longer running time of the dual method.
by Eun-Jong Hong.
Ph.D.
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Biddle, Jason Charles. "Methods and applications in computational protein design." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61792.
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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 107-111).
In this thesis, we summarize our work on applications and methods for computational protein design. First, we apply computational protein design to address the problem of degradation in stored proteins. Specifically, we target cysteine, asparagine, glutamine, and methionine amino acid residues to reduce or eliminate a protein's susceptibility to degradation via aggregation, deamidation, and oxidation. We demonstrate this technique on a subset of degradation-prone amino acids in phosphotriesterase, an enzyme that hydrolyzes toxic organophosphates including pesticides and chemical warfare agents. Second, we introduce BroMAP/A*, an exhaustive branch-and- bound search technique with enumeration. We compare performance of BroMAP/A* to DEE/A*, the current standard for conformational search with enumeration in the protein design community. When limited computational resources are available, DEE/A* sometimes fails to find the global minimum energy conformation and/or enumerate the lowest-energy conformations for large designs. Given the same computational resources, we show how BroMAP/A* is able to solve large designs by efficiently dividing the search space into small, solvable subproblems.
by Jason Charles Biddle.
S.M.
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Fuller, Jonathan Christopher. "Computational approaches for drug design at the protein-protein interface." Thesis, University of Leeds, 2010. http://etheses.whiterose.ac.uk/1699/.
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The ability to design drugs that disrupt formation of protein-protein interfaces is of particular interest to the pharmaceutical industry due to its promise for opening an entire new range of drug targets, many of which have already been well characterised in terms of their disease causing effect on the human body. Furthermore these interactions can be involved in many processes unique and essential to bacteria and viruses. We show that pockets on protein-protein interface are smaller but more numerous than those of marketed drugs using a pocket fnding algorithm (Q-SiteFinder). We investigate the similarities and differences between several candidate compounds designed to bind and disrupt protein-protein interfaces and compare to those of current marketed drugs designed to bind more traditional protein targets. We ask the further question as to whether it is possible to better identify pockets on a protein surface as likely to be drug binding. We conclude that it is possible to carefully use random forest machine learning techniques to marginally improve these predictions. However, it is extremely diffcult to use simple physical parameters to provide added information as to the maximal affnity that a small-molecule might be able to achieve in a given binding pocket. Further to the above questions we then investigate the hDM2-p53 system which when disrupted can induce apoptosis in many forms of cancer, making it a target of considerable interest to the pharmaceutical industry. Molecular docking is exploited in order to generate likely structural conformations of oligoamide hDM2-p53 inhibitors which can be used as a starting point for molecular dynamics simulations. These simulations using the AMBER/GAFF force feld are then further developed to perform replica-exchange alchemical free energy calculations using the Bennett Acceptance Ratio non-biased estimator. These simulations are in general shown to be very accurate and show promise in generating hypotheses for novel high-affnity oligoamide compounds.
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Davey, James A. "Multistate Computational Protein Design: Theories, Methods, and Applications." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35541.
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Traditional computational protein design (CPD) calculations model sequence perturbations and evaluate their stabilities using a single fixed protein backbone template in an approach referred to as single‐state design (SSD). However, certain design objectives require the explicit consideration of multiple conformational states. Cases where a multistate framework may be advantageous over the single‐state approach include the computer aided discovery of new enzyme substrates, the prediction of protein stabilities, and the design of protein dynamics. These design objectives can be tackled using multistate design (MSD). However, it is often the case
that a design objective requires the consideration of a protein state having no available structure information. For such circumstances the multistate framework cannot be applied. In this thesis I present the development of two template and ensemble preparation methodologies and their application to three projects. The purpose of which is to demonstrate the necessary ensemble modeling strategies to overcome limitations in available structure information. Particular emphasis is placed on the ability to recapitulate experimental data to guide modelling of the design space. Specifically, the use of MSD allowed for the accurate prediction of a methyltransferase recognition motif and new substrates, the prediction of mutant sequence stabilities with quantitative accuracy, and the design of dynamics into the rigid Gβ1 scaffold producing a set of dynamic variants whose tryptophan residue exchanges between two conformations on the millisecond timescale. Implementation of both the ensemble, coordinate perturbation followed by energy minimization (PertMin), and template, rotamer optimization followed by energy minimization (ROM), generation protocols developed here allow for exploration and manipulation of the structure space enabling the success of these applications.
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MARCHETTI, FILIPPO. "COMPUTATIONAL STUDIES OF PROTEIN-PROTEIN AND PROTEIN-ANTIBODY INTERACTIONS: IMPLICATION FOR MOLECULAR DESIGN." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/825462.
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High performance computing has opened the possibility to investigate complex systems by simulating their dynamics and study of equilibrium and non-equilibrium settings in realistic settings. Molecular Dynamics (MD) simulations have emerged as one of the privileged methods to disentangle the intricacies of biochemical systems but, despite the validity of Moore’s Law, the timescale of the events that can be simulated has an upper limit of the millisecond with tailor-made computers which is not enough to study some biologically relevant phenomena.
Starting from these considerations, in this thesis, I have set out to develop and validate novel methods to predict the location of potentially interacting surfaces on proteins and to predict the impact of small molecules on the activation vs. the inhibition of proteins’ functional dynamic states. To this end, I have combined physico-chemical approaches to the study of protein dynamics and generate novel approaches that may overcome the current limitations of pure brute force MD simulations.
In the first part of the thesis, I studied methods for the prediction of the residues involved in protein-protein interactions. I presented two different scores, one based on evolutionary information and one based on the energetics of the protein, on a dataset of crystal structures. Both scores have the capability to discriminate the interface region from the rest of the protein in a relevant fraction of cases. Moreover, a comparison of the scores efficacy on distinct protein classes highlights the importance of considering the biological function of the protein on the performance of the method used for the prediction of interface residues. In addition, the energetic method for interface residues prediction is used for the detection of antigenic epitopes on the spike protein of SARS-CoV-2. The regions predicted were confirmed against experimental complexes expanding our understanding of the molecular basis for interactions. In perspective, the acquired knowledge could be used for the design of novel vaccine candidates and diagnostic tools and to increase our readiness in the case of future epidemics. In the second part there, I focussed on the study of two allosteric systems. Firstly a method is presented for the integration of an ensemble docking protocol with a learning classifier for allosteric ligands of the protein Hsp90. The method reaches a good accuracy in classifying the activity of these ligands and this approach seems to reduce the dependency on the chemical similarity of the compounds used for the training. The method is tested on a limited dataset and further developments could be achieved in the future if the library of compounds is increased. In the end, I presented the initial analysis of an allosteric signal for integrinαvβ6 in complex with a pro-TGFβpeptide, with the use of molecular dynamics simulations. The data suggest that the presence of the peptide induces an increased rigidity of the legs of the structure, in particular for a specific domain.
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Grigoryan, Gevorg Ph D. Massachusetts Institute of Technology. "Computational approaches for the design and prediction of protein-protein interactions." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38997.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007.Includes bibliographical references (leaves 167-187).
There is a large class of applications in computational structural biology for which atomic-level representation is crucial for understanding the underlying biological phenomena, yet explicit atomic-level modeling is computationally prohibitive. Computational protein design, homology modeling, protein interaction prediction, docking and structure recognition are among these applications. Models that are commonly applied to these problems combine atomic-level representation with assumptions and approximations that make them computationally feasible. In this thesis I focus on several aspects of this type of modeling, analyze its limitations, propose improvements and explore applications to the design and prediction of protein-protein interactions.
by Gevorg Grigoryan.
Ph.D.
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Park, Daniel J. (Daniel John) 1979. "Computational tools for including specificity in protein design." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87286.
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Sisu, Cristina Smaranda Domnica. "Computational studies on protein similarity, specificity and design." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609407.
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Lucato, Arianna <1991>. "Computational design of novel protein-drug delivery systems." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/16136.
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Cancer is one of the leading causes of death throughout the world and the number of cases per year is reported to rise to 23.6 million by 2030. Amongst the different types of treatments available, chemotherapy represent the most common one. Despite its proven capability of tumour shrinkage and prevention from coming back after surgery, several factors limit its potential. These include poor bioavailability and biodistribution of the majority of the chemotherapeutic agents commonly used, the high dose required, their numerous adverse side effects, the development of drug resistance and non-specific targeting.
To overcome these limitations, I propose to develop novel protein-based drug delivery system capable of selectively transporting large quantities of chemotherapeutics at the tumour-site thus conferring greater therapeutic indices and efficacy. The new systems are based on proteins, natural existing polymers, that have the intrinsic property of binding small molecules with high affinity and specificity.
The project is structured in two phases. In the first phase I applied FuncLib, an automated method that uses phylogenetic analysis and Rosetta design calculations, to design mutants with higher affinity toward two selected chemotherapeutic agents. In the second phase, fifty-five designs were selected according to G Rosetta energy score and structural diversity, synthesised and assembled using a repertoire of molecular biology techniques.
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Opuu, Vaitea. "Computational design of proteins and enzymes." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX081.
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Nous proposons un ensemble de méthodes pour la conception de systèmes moléculaires. Notre stratégie consiste à utiliser comme modèle des machines naturellement optimisées, les protéines. Les protéines peuvent être des briques structurales, des transporteurs d'informations ou des catalyseurs chimiques. Nous utilisons ici des approches computationnelles, complémentaires aux voies expérimentales, pour concevoir de tels systèmes.Nous avons d'abord entièrement redessiné un domaine PDZ impliqué dans des voies métaboliques. Nous utilisons une approche physics-based basée sur la mécanique moléculaire, un modèle de solvant implicite et un échantillonnage Monte Carlo. Parmi plusieurs milliers de variants prédits pour adopter le repliement PDZ, trois ont été sélectionnés et montrent un repliement correct. Deux ont une affinité détectable pour les ligands peptidiques naturels.Nous avons ensuite re-dessiné le site actif de l'enzyme méthionyl-ARNt synthétase (MetRS). En utilisant un algorithme de type Monte Carlo adaptatif, nous avons sélectionné des variants pour l'affinité MetRS/méthionine (Met). Sur 17 variants testés expérimentalement, 17 sont actifs. La méthode a été ensuite appliquée à l'état de transition pour sélectionner des variants directement sur leur efficacité catalytique.Nous avons étudié la possibilité de modifier la MetRS pour étendre son activitéaux acides aminés β, afin d'étendre le code génétique. Ces acides aminésnon-naturels permettraient d'enrichir le répertoire structural des protéines. 20variants MetRS obtenus à partir de prédictions d'affinité MetRS/β-Met ont ététestés. Aucun n'augmente l'activité mais trois ont amélioré la sélectivité enfaveur de la β-Met. Nous avons implémenté une méthode de sélection de positionsd'intérêt et production de variants pour β-Met et β-Val. Une vingtaine deprédictions sont en cours de tests expérimentaux.Enfin, la modification de protéines in vivo pose la question de leur dérive génétique. Nous introduisons ici une méthode de conception de paires de gènes chevauchants pour des domaines PDZ. Ce codage permettrait de limiter la dérive génétique. Nous avons produit près de 2000 paires de domaines PDZ au codage chevauchant, dont une a été validées par 2 microsecondes de dynamique moléculaire. Des tests expérimentaux sont en coursWe propose a set of methods to design molecular systems. We start from naturally optimized components, namely proteins. Proteins can act as structural components, information transporters, or catalysts. We use computational methods to complement experiments and design protein systems.First, we fully redesigned a PDZ domain involved in metabolic pathways. We used a physics-based approach combining molecular mechanics, continuum electrostatics, and Monte Carlo sampling. Thousands of variants predicted to adopt the PDZ fold were selected. Three were validated experimentally. Two showed binding of the natural peptide ligand.Next, we redesigned the active site of the methionyl-tRNA synthetase enzyme (MetRS). We used an adaptive Monte Carlo method to select variants for methionine (Met) binding. Out of 17 predicted variants that were tested experimentally, 17 were found to be active. We extended the method to transition state binding to select mutants directly according to their catalytic power.We redesigned the MetRS binding site to obtain activity towards two β-amino acids, in order to expand the genetic code. These unnatural amino acids can enhance the structural repertoire of proteins. 20 predicted mutants were tested. Although none had increased β-Met activity, three had a gain in selectivity for β-Met. We then implemented a method to select optimal positions for design and applied it to β-Met and β-Val. Around 20 variants are being experimental tested.Finally, in vivo protein modifications raise the question of their eventual drift away from the original design. We introduce here a design approach for overlapping genes coding PDZ domains. This overlap would reduce genetic drift and provide bio-confinement. We computationally produced almost 2000 pairs of overlapping PDZ domains. One was validated by 2 microsecond molecular dynamic simulations. Experiments are underway
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Jiang, Lin. "Novel catalysts by computational enzyme design /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9248.
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Humphris, Elisabeth Lyn. "Computational protein design with multiple functional and structural constraints." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3390110.
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Henne, Randal Marlow. "Computational studies of G-protein coupled receptors /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8048.
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Chen, Tsan-Chou Scott. "Design of protein-protein interaction specificity using computational methods and experimental library screening." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/70386.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references.
Computational design of protein-protein interaction specificity is a powerful tool to examine and expand our understanding about how protein sequence determines interaction specificity. It also has many applications in basic bioscience and biotechnology. One of the major challenges for design is that current scoring functions relying on general physical principles do not always make reliable predictions about interaction specificity. In this thesis I described application of two approaches to address this problem. The first approach sought to improve scoring functions with experimental interaction specificity data related to the protein family of design interest. I used this approach to design inhibitor peptides against the viral bZIP protein BZLF 1. Specificity against design self-interaction was considered in the study. The second approach exploited the power of experimental library screening to characterize a large number of designed sequences at once, increasing the overall probability of identifying successful designs. I presented a novel framework for such library design approach and applied it to the design of anti-apoptotic Bcl-2 proteins with novel interaction specificity toward BH3 peptides. Finally I proposed how these two approaches can be combined together to further enhance our design capabilities.
by Tsan-Chou Scott Chen.
Ph.D.
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Dantas, Gautam. "In silico protein evolution by intelligent design : creating new and improved protein structures /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/9236.
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Maffucci, I. "OPTIMIZATION AND APPLICATION OF COMPUTATIONAL METHODS FOR THE DESIGN OF PROTEIN-PROTEIN INTERACTIONS MODULATORS." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/344181.
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In the wide field of PPIs, this PhD project has been focused on the optimization and application of computational methods for the design of PPIs modulators, with a particular interest toward peptide modulators targeting PPIs involving helical motifs.
In this contest, the first part of the project has been aimed to define the rationales behind the helical secondary structure stabilization and the helical screw sense selectivity exerted by chiral Cα-tetrasubstituted amino acids (cCTAAs) through REMD simulations and QTAIM analyses, and the mechanisms responsible of the helical screw sense inversion through PNEB simulations.
In detail, it has been found that the helical motif is stabilized by two complementary mechanisms: the first depends on the steric hindrance exerted by the cCTAA in an area parallel to the peptide helix axis and downstream of the cCTAA itself, whereas the second consists in the strengthening of the helical H-bond network thanks to peculiar C-H···O=C interactions. Analogously, P-helical screw sense selectivity is ascribable to the cCTAA steric hindrance parallel to the peptide helix axis, without particular preferences for the region downstream and upstream of the cCTAA, together with quite strong noncovalent interactions, consisting of classical N – H···O=C H-bonds and weak C – H···O=C interactions. Furthermore, PNEB simulations performed on achiral peptides of different lengths suggest that the helical screw sense inversion requires the formation of γ-turns, although a preferential screw sense inversion direction was not found.
Therefore, the knowledge gained from these studies could be helpful in designing stable helical peptides, having a preferential screw sense and that can be in principle activated in situ by inducing a conformational switch from P to M helix or vice versa.
Conversely, the second part of the project has been focused on the optimization of an MMGBSA based method, called Nwat-MMGBSA, aimed to improve the correlation between predicted binding energies of PPI complexes and experimental data. This approach, consisting in the inclusion, as part of the receptor, of hydration shells around the ligand during the MMGBSA calculations, was initially tested on classical receptor-ligand complexes and, then, automatized, optimized and tested on PPI complexes.
This approach turned out to be good for the evaluation of PPI modulators activities, from different points of view. First of all, when water played a significant role in mediating protein-ligand interactions, the application of Nwat-MMGBSA improved the correlation between predicted and experimental data. On the other hand, if the solvent does not explicitly participate to the interaction, it did not give detrimental results compared to those obtained with the standard approach. In addition, the protocol proved to be robust and reproducible, giving equivalent results by using different setups. Furthermore, although an optimal number of water molecules to include in the hydration shell could not be found, in the case of PPI interactions inhibited by small molecules the inclusion of 50 – 60 water molecules appears to be a good choice. A non-negligible advantage of this approach is represented by the possibility to automatize it, making it applicable for drug design/discovery purposes.
Therefore, although further evaluations are needed, most of all on larger datasets, the knowledge coming from the combination of both parts of the project can be exploited for the design of stable non-natural peptides targeting PPIs.
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Joughin, Brian Alan. "Novel methods in computational analysis and design of protein-protein interactions : applications to phosphoregulated interactions." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38630.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007.Includes bibliographical references (p. 107-130).
This thesis presents a number of novel computational methods for the analysis and design of protein-protein complexes, and their application to the study of the interactions of phosphopeptides with phosphopeptide-binding domain interactions. A novel protein-protein interaction type, the action-at-a-distance interaction, is described in the complex of the TEM1 P-lactamase with the 3-lactamase inhibitor protein (BLIP). New action-at-a-distance interactions were designed on the surface of BLIP and computed to enhance the affinity of that complex. A new method is described for the characterization and prediction of protein ligand-binding sites. This method was used to analyze the phosphoresidue-contacting sites of known phosphopeptide-binding domains, and to predict the sites of phosphoresidue-contact on some protein domains for which the correct site was not known. The design of a library of variant WW domains that is predicted to be enriched in domains that might have specificity for "pS/pT-Q" peptide ligands is detailed. General methods for designing libraries of degenerate oligonucleotides for expressing protein libraries as accurately as possible are given, and applied to the described WW domain variant library.
by Brian Alan Joughin.
Ph.D.
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Leaver-Fay, Andrew Snoeyink Jack. "Capturing atomic interactions with a graphical framework in computational protein design." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,613.
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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.
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Doudou, Slimane. "Computational modelling of protein-ligand binding : steps towards better drug design." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498949.
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Bastianelli, Giacomo. "Computational design of protein-based serine proteases inhibitors : tools and applications." Paris 7, 2009. http://www.theses.fr/2009PA077175.
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PfSUBl et PfSUB2 sont deux régulateurs de l'étape érythrocytaires du parasite et représentent de nouvelles cibles thérapeutiques intéressantes pour le développement de nouvelles familles de composés contre le paludisme. La limite majeure pour un tel développement rationnel de molécule sur les PFSUBs reste l'absence de structures expérimentales et des difficultés à exprimer l'enzyme recombinante active en grande quantité. L'utilisation d'un criblage à haut débit n'est donc pas envisageable à ce jour. Afin de contourner ces problèmes, nous avons mis en place une stratégie de recherche rationnelle d'inhibiteurs protéiques à l'aide d'outils in silico. Cette thèse met l'accent sur la validation et l'application d'un ensemble de d'outils bioinformatiques pour effectuer du « protein design ». Nous avons utilisé ces outils afin de modifier la spécificité d'une structure existante contre une enzyme de malaria en identifiant un mutant de EETI-II qui inhibe PvSUBl avec un Ki de 86 μM. Notre approche a aussi été appliquée au « reverse-engineer » de PcFKl, une petite protéine de venin d'araignée qui inhibe le cycle érythrocytaire de P. Falciparum. Cette hypothèse basée sur nos prédictions a été confirmée par des tests in vitro sur PfSUBlPfSUBl and PfSUB2 are two key regulators of the erythrocytic stage of the parasite and are interesting drug targets for developing new leading compounds against malaria. The major limitations to the drug discovery on PfSUBs are the absence of an experimental structure and the difficulties of expressing large quantities of the active enzymes, restricting the use of high-throughput screening of compounds. To overcome these obstacles, we set up a discovery process based on the computational design of protein-based inhibitors. The thesis focused on developing, validating and applying a series of bioinformatics tools to use in computational protein design. We used these tools to change the specificity of an existing scaffold towards a malaria enzyme, identifying a EETI-II mutant that inhibits PvSUBl with a Ki of 86 μM. Our computational protein design approach was also applied to reverse-engineer PcFKl, a spider-venom derived small protein that inhibits the erythrocytic stage of P. Falciparum. The hypothesis we made using these tools was experimentally confirmed by the in-vitro enzymatic testing on PfSUBl. Despite the challenges we faced, mostly due to the lack of a expérimental structure of PvSUBl, we successfully designed the first protein-based inhibitor of SUBI. The reverse-engineering we performed on PcFKl further confirms the reliability of thèse structural bioinformatics methods
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Durani, Venuka. "The Cycle of Protein Engineering: Bioinformatics Design of Two Dimeric Proteins and Computational Design of a Small Globular Domain." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338311626.
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Legault, Sandrine. "Investigating Different Rational Design Approaches to Increase Brightness in Red Fluorescent Proteins." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42740.
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Red fluorescent proteins (RFPs) are used extensively in biological research because their longer emission wavelengths are less phototoxic and allow deeper imaging of animal tissue. However, far-red RFPs generally display low brightness, emphasizing the need to develop brighter variants. Here, we investigate three approaches to rigidify the RFP chromophore to increase the quantum yield, and thereby brightness. We first used computational protein design on a maturation-efficient mRojo-VHSV variant previously engineered in our lab to introduce a Superdecker motif, a parallel pi-stack comprising aromatic residue side chains and the phenolate moiety of the chromophore, which we hypothesized would enhance chromophore packing and reduce non-radiative decay. The best mutants identified showed up to 1.7-fold higher quantum yield at pH 9, relative to their parent protein. We next postulated that brightness could be further increased by rigidifying the chromophore via branched aliphatic residues. Computational protein design was performed on a dim mCherry variant, mRojoA, followed by directed evolution on the brightest mutant. The combination of these methodologies yielded mSandy2, the brightest Discosoma-derived monomeric RFP with an emission maximum above 600 nm. Finally, we aimed to increase brightness by focusing on positions where residue rigidity correlated to quantum yield in mCherry-related RFPs according to NMR data that had been previously acquired in our lab. Combinatorial site-saturation mutagenesis was performed on two different surface patches of mCherry at positions 144/145/198 and 194/196/220. Our results demonstrated that surface residues may not be adequate targets for this approach. Altogether, the work herein presents unique rational design methodologies that can be used to increase brightness in RFPs.
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Vankayala, Sai Lakshmana Kumar. "Computational Approaches for Structure Based Drug Design and Protein Structure-Function Prediction." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4601.
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This dissertation thesis consists of a series of chapters that are interwoven by solving interesting biological problems, employing various computational methodologies. These techniques provide meaningful physical insights to promote the scientific fields of interest. Focus of chapter 1 concerns, the importance of computational tools like docking studies in advancing structure based drug design processes. This chapter also addresses the prime concerns like scoring functions, sampling algorithms and flexible docking studies that hamper the docking successes. Information about the different kinds of flexible dockings in terms of accuracy, time limitations and success studies are presented. Later the importance of Induced fit docking studies was explained in comparison to traditional MD simulations to predict the absolute binding modes.
Chapter 2 and 3 focuses on understanding, how sickle cell disease progresses through the production of sickled hemoglobin and its effects on sickle cell patients. And how, hydroxyurea, the only FDA approved treatment of sickle cell disease acts to subside sickle cell effects. It is believed the primary mechanism of action is associated with the pharmacological elevation of nitric oxide in the blood, however, the exact details of this mechanism is still unclear. HU interacts with oxy and deoxyHb resulting in slow NO production rates. However, this did not correlate with the observed increase of NO concentrations in patients undergoing HU therapy. The discrepancy can be attributed to the interaction of HU competing with other heme based enzymes such as catalase and peroxidases. In these two chapters, we investigate the atomic level details of this process using a combination of flexible-ligand / flexible-receptor virtual screening (i.e. induced fit docking, IFD) coupled with energetic analysis that decomposes interaction energies at the atomic level. Using these tools we were able to elucidate the previously unknown substrate binding modes of a series of hydroxyurea analogs to human hemoglobin, catalase and the concomitant structural changes of the enzymes. Our results are consistent with kinetic and EPR measurements of hydroxyurea-hemoglobin reactions and a full mechanism is proposed that offers new insights into possibly improving substrate binding and/or reactivity.
Finally in chapter 4, we have developed a 3D bioactive structure of O6-alkylguanine-DNA alkyltransferase (AGT), a DNA repair protein using Monte Carlo conformational search process. It is known that AGT prevents DNA damage, mutations and apoptosis arising from alkylated guanines. Various Benzyl guanine analouges of O6- methylguanine were tested for activity as potential inhibitors. The nature and position of the substitutions methyl and aminomethyl profoundly affected their activity. Molecular modeling of their interactions with alkyltransferase provided a molecular explanation for these results. The square of the correlation coefficient (R2 ) obtained between E-model scores (obtained from GLIDE XP/QPLD docking calculations) vs log(ED)values via a linear regression analysis was 0.96. The models indicate that the ortho-substitution causes a steric clash interfering with binding, whereas the meta-aminomethyl substitution allows an interaction of the amino group to generate an additional hydrogen bond with the protein. Using this model for virtually screening studies resulted in identification of seven lead compounds with novel scaffolds from National Cancer Institute Diversity Set2.
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Toschi, Francesca. "The computational investigation of protein/ligand complexes : implications for rational drug design." Thesis, University of Southampton, 2004. https://eprints.soton.ac.uk/378844/.
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St-Jacques, Antony D. "Engineering of Multi-Substrate Enzyme Specificity and Conformational Equilibrium Using Multistate Computational Protein Design." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38590.
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The creation of enzymes displaying desired substrate specificity is an important objective of enzyme engineering. To help achieve this goal, computational protein design (CPD) can be used to identify sequences that can fulfill interactions required to productively bind a desired substrate. Standard CPD protocols find optimal sequences in the context of a single state, for example an enzyme structure with a single substrate bound at its active site. However, many enzymes catalyze reactions requiring them to bind multiple substrates during successive steps of the catalytic cycle. The design of multi-substrate enzyme specificity requires the ability to evaluate sequences in the context of multiple substrate-bound states because mutations designed to enhance activity for one substrate may be detrimental to the binding of a second substrate. Additionally, many enzymes undergo conformational changes throughout their catalytic cycle and the equilibrium between these conformations can have an impact on their substrate specificity. In this thesis, I present the development and implementation of two multistate computational protein design methodologies for the redesign of multi-substrate enzyme specificity and the modulation of enzyme conformational equilibrium. Overall, our approaches open the door to the design of multi-substrate enzymes displaying tailored specificity for any biocatalytic application.
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Gagnon, Olivier. "Development and Validation of a Structure-Based Computational Method for the Prediction of Protein Specificity Profiles." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39643.
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Post-translational modification (PTM) of proteins by enzymes such as methyltransferases, kinases and deacetylases play a crucial role in the regulation of many metabolic pathways. Determining the substrate scope of these enzymes is essential when studying their biological role. However, the combinatorial nature of possible protein substrate sequences makes experimental screening assays intractable. To predict new substrates for proteins, various computational approaches have been developed. Our method relies on crystallographic data and a novel multistate computational protein design algorithm. We previously used our method to successfully predict four new substrates for SMYD2 (Lanouette S & Davey J.A., 2015), doubling the number of known targets for this PTM enzyme that has been difficult to characterize using other methods. This was possible by first extracting a specificity profile of Smyd2 using our algorithm and subsequently screening a peptide library for matching sequences. However, our method did not yield successful results when attempting to reproduce specificity profiles of other proteins (64% accuracy on average). Different protein environments have demonstrated limitations in the methodology and lead us to further develop the algorithm on a more thorough dataset. Using our new optimized method, specificity profile predictions increase by roughly 20% (84% accuracy on average), independent of the structural template used. The algorithm was then used to blindly predict a specificity profile for the methyltransferase Smyd3, an enzyme for which limited data is currently available. A library of 2550 peptides was screened with the predicted profile, yielding 123 matching sequences. We randomly chose 64 for experimental validation (SPOT peptide array) of methylation by Smyd3 and found 45 methylated and 19 non-methylated peptides (70% success rate). Finally, we released to the community a web version of the algorithm, which can be accessed as http://viper.science.uottawa.ca.
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Shah, Premal S. Rees Douglas C. "Advances in force field development and sequence optimization methods for computational protein design /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-04042005-142719.
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Stemm, Mina Catherine. "Computational and combinatorial design of protein-based inhibitors of human tyrosyl-DNA phosphodiesterase /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3166399.
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Mignon, David. "Computational protein design : un outil pour l'ingénierie des protéines et la biologie synthétique." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX089/document.
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Le « Computational protein design » ou CPD est la recherche des séquences d’acides aminés compatibles avec une structure protéique ciblée. L’objectif est de concevoir une fonction nouvelle et/ou d’ajouter un nouveau comportement. Le CPD est en développement dans de notre laboratoire depuis plusieurs années, avec le logiciel Proteus qui a plusieurs succès à son actif.Notre approche utilise un modèle énergétique basé sur la physique et s’appuie sur la différence d’énergie entre l’état plié et l’état déplié de la protéine. Au cours de cette thèse, nous avons enrichi Proteus sur plusieurs points, avec notamment l’ajout d’une méthode d’exploration Monte Carlo avec échange de répliques ou REMC. Nous avons comparé trois méthodes stochastiques pour l’exploration de l’espace de la séquence : le REMC, le Monte Carlo simple et une heuristique conçue pour le CPD, le «Multistart Steepest Descent » ou MSD. Ces comparaisons portent sur neuf protéines de trois familles de structures : SH2, SH3 et PDZ. En utilisant les techniques d’exploration ci-dessus, nous avons été en mesure d’identifier la conformation du minimum global d’énergie ou GMEC pour presque tous les tests dans lesquels jusqu’à 10 positions de la chaîne polypeptidique étaient libres de muter (les autres conservant leurs types natifs). Pour les tests avec 20 positions libres de muter, le GMEC a été identifié dans 2/3 des cas. Globalement, le REMC et le MSD donnent de très bonnes séquences en termes d’énergie, souvent identiques ou très proches du GMEC. Le MSD a obtenu les meilleurs résultats sur les tests à 30 positions mutables. Le REMC avec huit répliques et des paramètres optimisés a donné le plus souvent le meilleur résultat lorsque toutes les positions peuvent muter. De plus, comparé à une énumération exacte des séquences de faible énergie, le REMC fournit un échantillon de séquences de grande diversité.Dans la seconde partie de ce travail, nous avons testé notre modèle pour la conception de domaines PDZ. Pour l’état plié,nous avons utilisé deux variantes d’un modèle de solvant GB. La première utilise une frontière diélectrique protéine/solvant effective moyenne ; la seconde, plus rigoureuse, utilise une frontière exacte qui fluctue le long de la trajectoire MC. Pour caractériser l’état déplié, nous utilisons un ensemble de potentiels chimiques d’acide aminé ou énergies de références. Ces énergies de références sont déterminées par maximisation d’une fonction de vraisemblance afin de reproduire les fréquences d’acides aminés des domaines PDZ naturels. Les séquences conçues par Proteus ont été comparées aux séquences naturelles. Nos séquences sont globalement similaires aux séquences Pfam, au sens des scoresBLOSUM40, avec des scores particulièrement élevés pour les résidus au cœur de la protéine. La variante de GB la plus rigoureuse donne toujours des séquences similaires à des homologues naturels modérément éloignés et l’outil de reconnaissance de plis Super family appliqué à ces séquences donne une reconnaissance parfaite. Nos séquences ont également été comparées à celles du logiciel Rosetta. La qualité, selon les mêmes critères que précédemment, est très comparable, mais les séquences Rosetta présentent moins de mutations que les séquences ProteusComputational Protein Design, or CPD is the search for the amino acid sequences compatible with a targeted protein structure. The goal is to design a new function and/or add a new behavior. CPD has been developed in our laboratory for several years, with the software Proteus which has several successes to its credit. Our approach uses a physics-based energy model, and relies on the energy difference between the folded and unfolded states of the protein. During this thesis, we enriched Proteus on several points, including the addition of a Monte Carlo exploration method with Replica Exchange or REMC. We compared extensively three stochastic methods for the exploration of sequence space: REMC, plain Monte Carlo and a heuristic designed for CPD: Multistart Steepest Descent or MSD.These comparisons concerned nine proteins from three structural families: SH2, SH3 and PDZ. Using the exploration techniques above, we were able to identify the Global Minimum EnergyConformation, or GMEC for nearly all the test cases where up to10 positions of the polypeptide chain were free to mutate (the others retaining their native types). For the tests where 20positions were free to mutate, the GMEC was identified in 2/3 of the cases. Overall, REMC and MSD give very good sequences in terms of energy, often identical or very close to the GMEC. MSDperformed best in the tests with 30 mutating positions. REMCwith eight replicas and optimized parameters often gave the best result when all positions could mutate. Moreover, compared to an exact enumeration of the low energy sequences, REMC provided a sample of sequences with a high sequence diversity.In the second part of this work, we tested our CPD model forPDZ domain design. For the folded state, we used two variants ofa GB solvent model. The first used a mean, effective protein/solvent dielectric boundary; the second one, more rigorous, used an exact boundary that flucutated over the MCtrajectory. To characterize the unfolded state, we used a set of amino acid chemical potentials or reference energies. These reference energies were determined by maximizing a likelihoodfunction so as to reproduce the amino acid frequencies in naturalPDZ domains. The sequences designed by Proteus were compared to the natural sequences. Our sequences are globally similar to the Pfam sequences, in the sense of the BLOSUM40scores, with especially high scores for the residues in the core ofthe protein. The more rigorous GB variant always gives sequences similar to moderately distant natural homologues and perfect recognition by the the Super family fold recognition tool.Our sequences were also compared to those produced by the Rosetta software. The quality, according to the same criteria as before, was very similar, but the Rosetta sequences exhibit fewer mutations than the Proteus sequences
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Hom, Geoffrey Deshaies Raymond Joseph. "Advances in computational protein design : development of more efficient search algorithms and their application to the full-sequence design of larger proteins /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-05302005-223153.
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GULOTTA, Maria Rita. "Computational methodologies applied to Protein-Protein Interactions for molecular insights in Medicinal Chemistry." Doctoral thesis, Università degli Studi di Palermo, 2021. http://hdl.handle.net/10447/479127.
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In living systems, proteins usually team up into “molecular machinery” implementing several protein-to-protein physical contacts – or protein-protein interactions (PPIs) – to exert biological effects at both cellular and systems levels. Deregulations of protein-protein contacts have been associated with a huge number of diseases in a wide range of medical areas, such as oncology, cancer immunotherapy, infectious diseases, neurological disorders, heart failure, inflammation and oxidative stress.
PPIs are very complex and usually characterised by specific shape, size and complementarity. The protein interfaces are generally large, broad and shallow, and frequently protein-protein contacts are established between non-continuous epitopes, that conversely are dislocated across the protein interfaces. For this reason, in the past two decades, PPIs were thought to be “undruggable” targets by the scientific research community with scarce or no chance of success. However, in recent years the Medicinal Chemistry frontiers have been changing and PPIs have gained popularity amongst the research groups due to their key roles in such a huge number of diseases.
Until recently, PPIs were determined by experimental evidence through techniques specifically developed to target a small group of interactions. However, these methods present several limitations in terms of high costs and labour- and time-wasting. Nowadays, a large number of computational methods have been successfully applied to evaluate, validate, and deeply analyse the experimentally determined protein interactomes. In this context, a high number of computational tools and techniques have been developed, such as methods designed to construct interaction databases, quantum mechanics and molecular mechanics (QM/MM) to study the electronic properties, simulate chemical reactions, and calculate spectra, and all-atom molecular dynamics simulations to simulate temporal and spatial scales of inter- and intramolecular interactions. These techniques have allowed to explore PPI networks and predict the related functional features.
In this PhD work, an extensive use of computational techniques has been reported as valuable tool to explore protein-protein interfaces, identify their hot spot residues, select small molecules and design peptides with the aim of inhibiting six different studied PPIs. Indeed, in this thesis, a success story of in silico approaches to PPI study has been described, where MD simulations, docking and pharmacophore screenings led to the identification of a set of PPI modulators. Among these, two molecules, RIM430 and RIM442, registered good inhibitory activity with IC50 values even within the nanomolar range against the interaction between MUC1 and CIN85 proteins in cancer disease.
Furthermore, computational alanine scanning, all-atom molecular dynamics simulations, docking and pharmacophore screening were exploited to (1) rationally predict three potential interaction models of NLRP3PYD-ASCPYD complex involved in inflammatory and autoimmune diseases; (2) identify a potentially druggable region on the surface of SARS-CoV-2 Spike protein interface and select putative inhibitors of the interaction between Spike protein and the host ACE2 receptor against COVID-19 (CoronaVIrus Disease 2019); (3) investigate intramolecular modifications as a consequence of a point mutation on C3b protein (R102G) associated with the age-related macular degeneration (AMD) disease; (4) design non-standard peptides to inhibit transcriptional events associated with HOX-PBX complex involved in cancer diseases; and (5) to optimise a patented peptide sequence by designing helix-shaped peptides embedded with the hydrogen bond surrogate approach to tackle cocaine abuse relapses associated with Ras-RasGRF1 interaction.
Although all the herein exploited techniques are based on predictive calculations and need experimental evidence to confirm the findings, the results and molecular insights retrieved and collected show the potential of the computer-aided drug design applied to the Medicinal Chemistry, guaranteeing labour- and time-saving to the research groups. On the other hand, computing ability, improved algorithms and fast-growing data sets are rapidly fostering advances in multiscale molecular modelling, providing a powerful emerging paradigm for drug discovery. It means that more and more research efforts will be done to invest in novel and more precise computational techniques and fine-tune the currently employed methodologies.
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Feldmeier, Kaspar Konrad [Verfasser], and Birte [Akademischer Betreuer] Höcker. "Form and Function : Two computational protein design studies / Kaspar Konrad Feldmeier ; Betreuer: Birte Höcker." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1164169718/34.
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Ross, Gregory A. "Improving rapid affinity calculations for drug-protein interactions." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:62ccfb5e-10f1-40ec-9a2b-936277944d87.
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The rationalisation of drug potency using three-dimensional structures of protein-ligand complexes is a central paradigm in medicinal research. For over two decades, a major goal has been to find the rules that accurately relate the structure of any protein-ligand complex to its affinity. Addressing this problem is of great concern to the pharmaceutical industry, which uses virtual screens to computationally assay up to many millions of compounds against a protein target. A fast and trustworthy affinity estimator could potentially streamline the drug discovery process, reducing reliance on expensive wet lab experiments, speeding up the discovery of new hits and aiding lead optimization. Water plays a critical role in drug-protein interactions. To address the often ambiguous nature of water in binding sites, a water placement method was developed and found to be in good agreement with X-ray crystallography, neutron diffraction data and molecular dynamics simulations. The method is fast and has facilitated a large scale study of the statistics of water in ligand binding sites, as well as the creation of models pertaining to water binding free energies and displacement propensities, which are of particular interest to medicinal chemistry. Structure-based scoring functions employing the explicit water models were developed. Surprisingly, these attempts were no more accurate than the current state of the art, and the models suffered from the same inadequacies which have plagued all previous scoring functions. This suggests a unifying cause behind scoring function inaccuracy. Accordingly, mathematical analyses on the fundamental uncertainties in structure-based modelling were conducted. Using statistical learning theory and information theory, the existence of inherent errors in empirical scoring functions was proven. Among other results, it was found that even the very best generalised structure-based model is significantly limited in its accuracy, and protein-specific models are always likely to be better. The theoretical framework developed herein hints at modelling strategies that operate at the leading edge of achievable accuracy.
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Denarie, Laurent. "Robotics-inspired methods to enhance protein design." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/18677/1/Denarie.pdf.
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The ability to design proteins with specific properties would yield great progress in pharmacology and bio-technologies. Methods to design proteins have been developed since a few decades and some relevant achievements have been made including de novo protein design. Yet, current approaches suffer some serious limitations. By not taking protein’s backbone motions into account, they fail at capturing some of the properties of the candidate design and cannot guarantee that the solution will in fact be stable for the goal conformation. Besides, although multi-states design methods have been proposed, they do not guarantee that a feasible trajectory between those states exists, which means that design problem involving state transitions are out of reach of the current methods. This thesis investigates how robotics-inspired algorithms can be used to efficiently explore the conformational landscape of a protein aiming to enhance protein design methods by introducing additional backbone flexibility. This work also provides first milestones towards protein motion design.
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Capelli, R. "COMPUTATIONAL MODELING OF PROTEINS: FROM STATISTICAL MECHANICS TO IMMUNOLOGY." Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/527950.
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One of the biggest revolutions occurred during the second half of the 20th century in physics was the introduction of computers in research. In particular, the use of fast computing machines opened the possibility to study complex systems by simulating their dynamics, without the need to pursue analytical solutions, otherwise impossible to tackle. The consequences of this breakthrough were huge both in the study of equilibrium and non-equilibrium many-body problems, with the strong limitation given by the number of atoms involved in the calculation.
The first technique used in biology-related problems was the Monte Carlo Method, and some years later Molecular Dynamics (MD) was formalized. In MD, for each atom of the system one can solve its Newton equations of motion, obtaining a trajectory in the phase space for the entire system, and study its behavior in equilibrium and non-equilibrium conditions. The constant rise in computational power gave the possibility to scientists to study larger and larger systems, while the advances in experimental techniques enhanced the possibility for direct comparisons between wet and in silico data at similar levels of resolution. Despite the validity of Moore’s Law (i.e., the exponential growth of the computing power due to transistors miniaturization) until now, the timescale of the events that can be simulated has an upper limit of the millisecond with tailor-made computers, which is not enough to study all the biologically-relevant phenomena. Since the birth of computational chemistry, a huge number of different statistical mechanics-based methods has been implemented to permit, given the computing power limit, an effective reliable use of MD simulations in biochemistry.
One of the most relevant problems tackled by MD is the calculation of free energy differences, both in conformational changes and in sequence mutations of a protein. The main reason of this difficulty is represented by the frustrated nature of interactions in proteins and the size of these systems: this leads to a complex energy landscape which in principle needs very long sampling times to overcome all possible energy barriers. In the present thesis, we studied and improved a path-independent and system-independent free energy calculation technique, called Simplified Confinement Method. We describe this work in Chapter 1.
Although MD has been successful in most of its applications, there are still many open problems: as mentioned before, the available parametrizations of interaction potentials (called force fields) are not completely reliable. In particular, the choice of force field parameters is performed comparing experimental data on a fixed set of (usually small) molecules with computed data on the same molecules. This raises a significant problem: large molecules can have a more complex behavior, and using these potentials can lead to a systematic error; furthermore, the timescale in which the force field is tested needs to be limited. Another strong limitation of MD depends on the equilibrium experiments used for parametrization: the kinetic properties of a system are not considered. Given the impossibility to reparametrize a general force field with non-equilibrium experimental data, we implemented a technique that uses equilibrium-based force fields, adding a potential term based on time series resulting from kinetic experiments. This approach, based on the principle of Maximum Caliber, restrains the system with an experimental-based bias, returning a more realistic behavior of the simulation in condition where the usual force fields show their limitations. We describe this work in Chapter 2.
The application of computational methods in the study of proteins confirms its efficacy in other fields of life sciences: an actual and emerging topic is represented by vaccinology. With techniques developed by Louis Pasteur at the end of the 19 th century (isolation of the pathogen, its inactivation and subsequent inoculation in the host), various scientists developed vaccines for deadly diseases like poliomyelitis, diphterite and measles. None of the mentioned was developed with molecular biology-based approaches. Almost 50 years after the birth of molecular biology, the Human Genome Project decoded human DNA and, at the same time, the genome of the most dangerous pathogen was screened. This has laid the foundation of Reverse Vaccinology (RV), where the proteins responsible for immune reaction can be identified from the pathogen DNA and tested directly on animal models, obtaining a new vaccine candidate with little or no risk for the host, having removed the pathogen itself. At the beginning of the 21st century the first vaccine against Meningococcus B, responsible for the 50% of the meningococcal meningitis, was developed using this protocol. Since then, crystallographic data was inserted in RV workflow to exploit conformational data, creating the so-called Structural Vaccinology (SV). To enhance its efficacy, SV exploits all the aspects of molecular modeling like computer-aided drug/protein design and MD to integrate information that come from experimental sources. One of the most promising technique in this field is the grafting of an immunogenic sequence (i.e., a portion of a protein recognized by the immune system) on a foreign protein; this approach could lead to a new vaccine component which have no risk for the patient. To date, the grafting technique has been carried out by human-driven workflows. Motivated by this reason, we studied immunogenic peptides from a family of pathogens involved in respiratory diseases, exploiting Structural Vaccinology principles with both computational and experimental approach. Furthermore, we developed and implemented an unsupervisionated automated tool to design grafted protein sequences. We describe this work in Chapter 3.
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Badieyan, Somayesadat. "Molecular Design and Mechanistic Characterization of Glycoside Hydrolases using Computational and Experimental Techniques." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77989.
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Cellulase activity is due to the activity of multiple enzymes, including endoglucanases, cellobiohydrolases and glucosidases that work synergistically to solubilize crystalline cellulose efficiently. The dependence of hydrolysis reaction rate on temperature predicts that large increases in performance and decreased enzyme cost would be achieved if the enzymatic degradation could be operated at elevated temperatures. However there is always a tradeoff between the activity and stability of enzymes. So obtaining cellulases with high thermostability and simultaneously enhanced activity is a great challenge in the field of bioethanol production. In the studies presented in this dissertation, different computational techniques, such as Molecular Dynamics (MD), Molecular Docking, Quantum Mechanics (QM) and hybrid Quantum Mechanics and Molecular Mechanics (QM/MM), along with several site-directed mutagenesis and in vitro assays have been applied to the study and design of the activity and stability of cellulases. Using molecular dynamics to investigate the thermal unfolding of endoglucanases of family 5 of glycoside hydrolases (GH5), a good correlation between the optimum activity temperatures of cellulases and their structural fluctuations was revealed. These data led us to hypothesize that cellulase stability could be enhanced by redesign of enzyme dynamics through altering the amino acid composition in the highly flexible regions of an endoglucanase that would increase its local or global rigidity. Cellulase C, a GH5 member, was stabilized thermally and chemically by cross linking its highly flexible subdomain.
Family 1 of glycoside hydrolases were investigated by QM and hybrid QM/MM methods to analyze the role of non-catalytic polar residues at the active site of GH1 glucosidases that make hydrogen bonds to the glucose moiety at subsite -1. A tyrosine residue in simultaneous interaction with O5 of the glucose ring and the carboxylate group of the nucleophilic glutamate was found to play a significant role in the energy profile along the hydrolysis reaction coordinates. It was shown to reduce the energy barrier of the deglycosylation step by ~12 Kcal/mol. Exclusion of this tyrosine from QM calculation substantially influenced the preactivated structure of the glucose moiety in the enzyme-substrate complex and affected the structural distortion and charge distribution in transition states.Ph. D.
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Correia, Bruno Emanuel Ferreira de Sousa. "Computational design with flexible backbone sampling for protein remodeling and scaffolding of complex binding sites." Doctoral thesis, Universidade Nova de Lisboa. Instituto de Tecnologia Química e Biológica, 2010. http://hdl.handle.net/10362/5791.
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Dissertation presented to obtain the Doutoramento (Ph.D.) degree in Biochemistry at the
Instituto de Tecnologia Qu mica e Biol ogica da Universidade Nova de LisboaComputational protein design has achieved several milestones, including the design of a new protein fold, the design of enzymes for reactions that lack natural catalysts, and the re-engineering of protein-protein and protein-DNA binding speci city. These achievements have spurred demand to apply protein design methods to a wider array of research problems. However, the existing computational methods have largely relied on xed-backbone approaches that may limit the scope of problems that can be tackled. Here, we describe four computational protocols - side chain grafting, exible backbone remodeling, backbone grafting, and de novo sca old design - that expand the methodological protein design repertoire, three of which incorporate backbone exibility. Brie y, in the side chain grafting method, side chains of a structural motif are transplanted to a protein with a similar backbone conformation; in exible backbone remodeling, de novo segments of backbone are built and designed; in backbone grafting, structural motifs are explicitly grafted onto other proteins; and in de novo sca olding, a protein is folded and designed around a structural motif. We developed these new methods for the design of epitope-sca old vaccines in which viral neutralization epitopes of known three-dimensional structure were transplanted onto nonviral sca old proteins for conformational stabilization and immune presentation.(...)
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Druart, Karen. "Défis algorithmiques pour les simulations biomoléculaires et la conception de protéines." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLX080/document.
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Le dessin computationnel de protéine, ou CPD, est une technique qui permet de modifier les protéines pour leur conférer de nouvelles propriétés, en exploitant leurs structures 3D et une modélisation moléculaire. Pour rendre la méthode de plus en plus prédictive, les modèles employés doivent constamment progresser. Dans cette thèse, nous avons abordé le problème de la représentation explicite de la flexibilité du squelette protéique. Nous avons développé une méthode de dessin "multi-états", qui se base sur une bibliothèque discrète de conformations du squelette, établie à l'avance. Dans un contexte de simulation Monte Carlo, le paysage énergétique d'une protéine étant rugueux, les changements de squelettes ne peuvent etre acceptés que moyennant certaines précautions. Aussi, pour explorer ces conformations, en même temps que des mutations et des mouvements de chaînes latérales, nous avons introduit un nouveau type de déplacement dans une méthode Monte Carlo existante. Il s'agit d'un déplacement "hybride", où un changement de squelette est suivi d'une courte relaxation Monte Carlo des chaînes latérales seules, après laquelle un test d'acceptation est effectué. Pour respecter une distribution de Boltzmann des états, la probabilité doit avoir une forme précise, qui contient une intégrale de chemin, difficile à calculer en pratique. Deux approximations sont explorées en détail: une basée sur un seul chemin de relaxation, ou chemin "générateur" (Single Path Approximation, ou SPA), et une plus complexe basée sur un ensemble de chemins, obtenus en permutant les étapes élémentaires du chemin générateur (Permuted Path Approximation, ou PPA). Ces deux approximations sont étudiées et comparées sur deux protéines. En particulier, nous calculons les énergies relatives des conformations du squelette en utilisant trois méthodes différentes, qui passent réversiblement d'une conformation à l'autre en empruntent des chemins très différents. Le bon accord entre les méthodes, obtenu avec de nombreuses paramétrisations différentes, montre que l'énergie libre se comporte bien comme une fonction d'état, suggérant que les états sont bien échantillonnés selon la distribution de Boltzmann. La méthode d'échantillonnage est ensuite appliquée à une boucle dans le site actif de la tyrosyl-ARNt synthétase, permettant d'identifier des séquences qui favorisent une conformation, soit ouverte, soit fermée de la boucle, permettant en principe de contrôler ou redessiner sa conformation. Nous décrivons enfin un travail préliminaire visant à augmenter encore la flexibilité du squelette, en explorant un espace de conformations continu et non plus discret. Ce changement d'espace oblige à restructurer complètement le calcul des énergies et le déroulement des simulations, augmente considérable le coût des calculs, et nécessite une parallélisation beaucoup plus agressive du logiciel de simulationComputational protein design is a method to modify proteins and obtain new properties, using their 3D structure and molecular modelling. To make the method more predictive, the models need continued improvement. In this thesis, we addressed the problem of explicitly representing the flexibility of the protein backbone. We developed a "multi-state" design approach, based on a small library of backbone conformations, defined ahead of time. In a Monte Carlo framework, given the rugged protein energy landscape, large backbone motions can only be accepted if precautions are taken. Thus, to explore these conformations, along with sidechain mutations and motions, we have introduced a new type of Monte Carlo move. The move is a "hybrid" one, where the backbone changes its conformation, then a short Monte Carlo relaxation of the sidechains is done, followed by an acceptation test. To obtain a Boltzmann sampling of states, the acceptation probability should have a specific form, which involves a path integral that is difficult to calculate. Two approximate forms are explored: the first is based on a single relaxation path, or "generating path" (Single Path Approximation or SPA). The second is more complex and relies on a collection of paths, obtained by shuffling the elementary steps of the generating path (Permuted Path Approximation or PPA). These approximations are tested in depth and compared on two proteins. Free energy differences between the backbone conformations are computed using three different approaches, which move the system reversibly from one conformation to another, but follow very different routes. Good agreement is obtained between the methods and a wide range of parameterizations, indicating that the free energy behaves as a state function, as it should, and strongly suggesting that Boltzmann sampling is verified. The sampling method is applied to the tyrosyl-tRNA synthetase enzyme, allowing us to identify sequences that prefer either an open or a closed conformation of an active site loop, so that in principle we can control, or design the loop conformation. Finally, we describe preliminary work to make the protein backbone fully flexible, moving within a continuous and not a discrete space. This new conformational space requires a complete reorganization of the energy calculation and Monte Carlo simulation scheme, increases simulation cost substantially, and requires a much more aggressive parallelization of our software
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Fang, Lei. "COMPUTATIONAL MODELING, DESIGN, AND CHARACTERIZATION OF COCAINE-METABOLIZING ENZYMES FOR ANTI-COCAINE MEDICATION." UKnowledge, 2013. http://uknowledge.uky.edu/pharmacy_etds/39.
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Cocaine is a widely abused and addictive drug, resulting in serious medical and social problems in modern society. Currently, there is no FDA-approved medication specific for cocaine abuse treatment. The disastrous medical and social consequences of cocaine abuse have made the development of an anti-cocaine medication a high priority. However, despite decades of efforts, traditional pharmacodynamic approach has failed to yield a truly useful small-molecule drug due to the difficulties inherent in blocking a blocker like cocaine without affecting the normal functions of the transporters or receptors. An alternative approach, i.e. pharmacokinetic approach, is to interfere with the delivery of cocaine to its receptors/transporters and/or accelerate its metabolism in the body. It would be an ideal anti-cocaine medication to accelerate cocaine metabolism producing biologically inactive metabolites.
Two natural enzymes may catalyze hydrolysis of cocaine: human butyrylcholinesterase (BChE) and bacterial cocaine esterase (CocE). However, the wild-type enzymes are not suitable as anti-cocaine therapeutics, due to the low catalytic activity, thermoinstability, or short biological half-life. In this investigation, we performed integrated computational-experimental studies to rationally design and discover mutants of these enzymes in order to improve the catalytic activity, thermostability, and/or biological half-life. To rationally design desirable mutants of the enzymes, we have successfully developed computational models, including those for BChE gating, glycosylated BChE structure, BChE-substrate complex structures, BChE dimer/tetramer structures, CocE monomer/dimer structures, and CocE-substrate complex structures. Development of the computational models enabled us to rationally design new amino-acid mutations that may improve the catalytic activity, thermostability, and/or prolonged biological half-life. The computational design was followed by wet experimental tests, including both in vitro and in vivo experiments, leading to discovery of new enzyme forms with not only a high catalytic efficiency against cocaine, but also an improved thermostability and/or prolonged biological half-life. The identified new mutants of BChE and CocE are expected to be valuable candidates for development of a more efficient enzyme therapy for cocaine abuse. The encouraging outcomes of the present study also suggest that the structure-and-mechanism-based design and integrated computational-experimental approach is promising for rational drug design and discovery.
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Li, Weiyi. "Protein Engineering Hydrophobic Core Residues of Computationally Designed Protein G and Single-Chain Rop: Investigating the Relationship between Protein Primary structure and Protein Stability through High-Throughput Approaches." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398956266.
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Domin, Gesine, Sven Findeiß, Manja Wachsmuth, Sebastian Will, Peter F. Stadler, and Mario Mörl. "Applicability of a computational design approach for synthetic riboswitches." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-218007.
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Riboswitches have gained attention as tools for synthetic biology, since they enable researchers to reprogram cells to sense and respond to exogenous molecules. In vitro evolutionary approaches produced numerous RNA aptamers that bind such small ligands, but their conversion into functional riboswitches remains difficult. We previously developed a computational approach for the design of synthetic theophylline riboswitches based on secondary structure prediction. These riboswitches have been constructed to regulate ligand dependent transcription termination in Escherichia coli. Here, we test the usability of this design strategy by applying the approach to tetracycline and streptomycin aptamers. The resulting tetracycline riboswitches exhibit robust regulatory properties in vivo. Tandem fusions of these riboswitches with theophylline riboswitches represent logic gates responding to two different input signals. In contrast, the conversion of the streptomycin aptamer into functional riboswitches appears to be difficult. Investigations of the underlying aptamer secondary structure revealed differences between in silico prediction and structure probing. We conclude that only aptamers adopting the minimal free energy (MFE) structure are suitable targets for construction of synthetic riboswitches with design approaches based on equilibrium thermodynamics of RNA structures. Further improvements in the design strategy are required to implement aptamer
structures not corresponding to the calculated MFE state.
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Domin, Gesine, Sven Findeiß, Manja Wachsmuth, Sebastian Will, Peter F. Stadler, and Mario Mörl. "Applicability of a computational design approach for synthetic riboswitches." Oxford University Press, 2016. https://ul.qucosa.de/id/qucosa%3A15259.
Full textAbstract:
Riboswitches have gained attention as tools for synthetic biology, since they enable researchers to reprogram cells to sense and respond to exogenous molecules. In vitro evolutionary approaches produced numerous RNA aptamers that bind such small ligands, but their conversion into functional riboswitches remains difficult. We previously developed a computational approach for the design of synthetic theophylline riboswitches based on secondary structure prediction. These riboswitches have been constructed to regulate ligand dependent transcription termination in Escherichia coli. Here, we test the usability of this design strategy by applying the approach to tetracycline and streptomycin aptamers. The resulting tetracycline riboswitches exhibit robust regulatory properties in vivo. Tandem fusions of these riboswitches with theophylline riboswitches represent logic gates responding to two different input signals. In contrast, the conversion of the streptomycin aptamer into functional riboswitches appears to be difficult. Investigations of the underlying aptamer secondary structure revealed differences between in silico prediction and structure probing. We conclude that only aptamers adopting the minimal free energy (MFE) structure are suitable targets for construction of synthetic riboswitches with design approaches based on equilibrium thermodynamics of RNA structures. Further improvements in the design strategy are required to implement aptamer
structures not corresponding to the calculated MFE state.
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Mahasenan, Kiran V. "Discovery of novel small molecule enzyme inhibitors and receptor modulators through structure-based computational design." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1332367560.
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Altman, Michael Darren. "Computational ligand design and analysis in protein complexes using inverse methods, combinatorial search, and accurate solvation modeling." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36258.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2006.Vita.
Includes bibliographical references (p. 207-230).
This thesis presents the development and application of several computational techniques to aid in the design and analysis of small molecules and peptides that bind to protein targets. First, an inverse small-molecule design algorithm is presented that can explore the space of ligands compatible with binding to a target protein using fast combinatorial search methods. The inverse design method was applied to design inhibitors of HIV-1 protease that should be less likely to induce resistance mutations because they fit inside a consensus substrate envelope. Fifteen designed inhibitors were chemically synthesized, and four of the tightest binding compounds to the wild-type protease exhibited broad specificity against a panel of drug resistance mutant proteases in experimental tests. Inverse protein design methods and charge optimization were also applied to improve the binding affinity of a substrate peptide for an inactivated mutant of HIV-1 protease, in an effort to learn more about the thermodynamics and mechanisms of peptide binding. A single mutant peptide calculated to have improved binding electrostatics exhibited greater than 10-fold improved affinity experimentally.
(cont.) The second half of this thesis presents an accurate method for evaluating the electrostatic component of solvation and binding in molecular systems, based on curved boundary-element method solutions of the linearized Poisson-Boltzmann equation. Using the presented FFTSVD matrix compression algorithm and other techniques, a full linearized Poisson-Boltzmann equation solver is described that is capable of solving multi-region problems in molecular continuum electrostatics to high precision.
Michael Darren Altman.
Ph.D.
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Lanouette, Sylvain. "Characterization of the Protein Lysine Methyltransferase SMYD2." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32467.
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Our understanding of protein lysine methyltransferases and their substrates remains limited despite their importance as regulators of the proteome. The SMYD (SET and MYND domain) methyltransferase family plays pivotal roles in various cellular processes, including transcriptional regulation and embryonic development. Among them, SMYD2 is associated with oesophageal squamous cell carcinoma, bladder cancer and leukemia as well as with embryonic development. Initially identified as a histone methyltransferase, SMYD2 was later reported to methylate p53, the retinoblastoma protein pRb and the estrogen receptor ERalpha and to regulate their activity. Our proteomic and biochemical analyses demonstrated that SMYD2 also methylates the molecular chaperone HSP90 on K209 and K615. We also showed that HSP90 methylation is regulated by HSP90 co-chaperones, pH, and the demethylase LSD1. Further methyltransferase assays demonstrated that SMYD2 methylates lysine K* in proteins which include the sequence [LFM]-₁-K*-[AFYMSHRK]+₁-[LYK]+₂. This motif allowed us to show that SMYD2 methylates the transcriptional co-repressor SIN3B, the RNA helicase DHX15 and the myogenic transcription factors SIX1 and SIX2. Finally, muscle cell models suggest that SMYD2 methyltransferase activity plays a role in preventing premature myogenic differentiation of proliferating myoblasts by repressing muscle-specific genes. Our work thus shows that SMYD2 methyltransferase activity targets a broad array of substrates in vitro and in situ and is regulated by intricate mechanisms.
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Radoux, Christopher John. "The automatic detection of small molecule binding hotspots on proteins : applying hotspots to structure-based drug design." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/275133.
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Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods typically assess the pocket as a whole, doing little to suggest which regions and interactions are the most important for binding. This thesis introduces Fragment Hotspot Maps, a grid-based method that samples atomic propensities derived from interactions in the Cambridge Structural Database (CSD) with simple molecular probes. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores, offering more precision over other binding site prediction methods. The method is validated by scoring the positions of 21 fragment and lead pairs. Fragment atoms are found in the highest scoring parts of the map corresponding to their atom type, with a median percentage rank of 98%. This is reduced to 72% for lead atoms, showing that the method can differentiate between the hotspots, and the warm spots later used during fragment elaboration. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. These calculations are easily accessible through a simple to use web application, which only requires an input PDB structure or code. High scoring specific interactions predicted by the Fragment Hotspot Maps can be used to guide existing computer aided drug discovery methods. The Hotspots Python API has been created to allow these work flows to be executed programmatically through a single Python script. Two of the functions use scores from the Fragment Hotspot Maps to guide virtual screening methods, docking and field-based ligand screening. Docking virtual screening performance is improved by using a constraint selected from the highest scoring polar interaction. The field-based ligand screener uses modified versions of the Fragment Hotspot Maps directly to predict and score the binding pose. This workflow gave comparable results to docking, and for one target, Glucocorticoid receptor (GCR), showed much better results, highlighting its potential as an orthogonal approach. Fragment Hotspot Maps can be used at multiple stages of the drug discovery process, and research into these applications is ongoing. Their utility in the following areas are currently being explored: to assess ligandability for both individual structures and across proteomes, to aid in library design, to assess pockets throughout a molecular dynamics trajectory, to prioritise crystallographic fragment hits and to guide hit-to-lead development.
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Almlöf, Martin. "Computational Methods for Calculation of Ligand-Receptor Binding Affinities Involving Protein and Nucleic Acid Complexes." Doctoral thesis, Uppsala University, Department of Cell and Molecular Biology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7421.
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The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented.
For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions.
A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands.
A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding.
The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.
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Park, In-Hee. "Computational Simulations of Protein-Ligand Molecular Recognition via Enhanced Samplings, Free Energy Calculations and Applications to Structure-Based Drug Design." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276745410.
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