Relevant bibliographies by topics / Computational protein design

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computational protein design'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Computational protein design"

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Kraemer-Pecore, Christina M., Andrew M. Wollacott, and John R. Desjarlais. "Computational protein design." Current Opinion in Chemical Biology 5, no. 6 (December 2001): 690–95. http://dx.doi.org/10.1016/s1367-5931(01)00267-8.

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Street, Arthur G., and Stephen L. Mayo. "Computational protein design." Structure 7, no. 5 (May 1999): R105—R109. http://dx.doi.org/10.1016/s0969-2126(99)80062-8.

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MacDonald, James T., and Paul S. Freemont. "Computational protein design with backbone plasticity." Biochemical Society Transactions 44, no. 5 (October 15, 2016): 1523–29. http://dx.doi.org/10.1042/bst20160155.

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The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.
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Schreiber, Gideon, and Sarel J. Fleishman. "Computational design of protein–protein interactions." Current Opinion in Structural Biology 23, no. 6 (December 2013): 903–10. http://dx.doi.org/10.1016/j.sbi.2013.08.003.

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Kortemme, Tanja, and David Baker. "Computational design of protein–protein interactions." Current Opinion in Chemical Biology 8, no. 1 (February 2004): 91–97. http://dx.doi.org/10.1016/j.cbpa.2003.12.008.

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Kundert, Kale, and Tanja Kortemme. "Computational design of structured loops for new protein functions." Biological Chemistry 400, no. 3 (February 25, 2019): 275–88. http://dx.doi.org/10.1515/hsz-2018-0348.

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Abstract The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.
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Frappier, Vincent, and Amy E. Keating. "Data-driven computational protein design." Current Opinion in Structural Biology 69 (August 2021): 63–69. http://dx.doi.org/10.1016/j.sbi.2021.03.009.

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Samish, Ilan, Christopher M. MacDermaid, Jose Manuel Perez-Aguilar, and Jeffery G. Saven. "Theoretical and Computational Protein Design." Annual Review of Physical Chemistry 62, no. 1 (May 5, 2011): 129–49. http://dx.doi.org/10.1146/annurev-physchem-032210-103509.

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Coluzza, Ivan. "Computational protein design: a review." Journal of Physics: Condensed Matter 29, no. 14 (February 27, 2017): 143001. http://dx.doi.org/10.1088/1361-648x/aa5c76.

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Desjarlais, John R., and Stephen L. Mayo. "Editorial overview: Computational protein design." Current Opinion in Structural Biology 12, no. 4 (August 2002): 429–30. http://dx.doi.org/10.1016/s0959-440x(02)00343-3.

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Dissertations / Theses on the topic "Computational protein design"

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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 scientifique
Computational 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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Books on the topic "Computational protein design"

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Samish, Ilan, ed. Computational Protein Design. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6637-0.

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Torsten, Schwede, and Peitsch Manuel C, eds. Computational structural biology: Methods and applications. Hackensack, N.J: World Scientific, 2008.

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Hans-Joachim, Böhm, and Schneider Gisbert 1965-, eds. Protein-ligand interactions from molecular recognition to drug design. Weinheim: Wiley-VCH, 2003.

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Moreira, Irina S., Miguel Machuqueiro, and Joana Mourão, eds. Computational Design of Membrane Proteins. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1468-6.

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Stoddard, Barry L., ed. Computational Design of Ligand Binding Proteins. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3569-7.

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Mather A. R. Sadiq Al-Baghdadi. CFD models for analysis and design of PEM fuel cells CFD models for analysis & design of PEM fuel cells. New York: Nova Science Publishers, 2008.

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Maher A. R. Sadiq Al-Baghdadi. CFD modeling and analysis of different novel designs of air-breathing PEM fuel cells. Hauppauge, N.Y: Nova Science Publishers, 2009.

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Maher A. R. Sadiq Al-Baghdadi. CFD modeling and analysis of different novel designs of air-breathing PEM fuel cells. New York: Nova Science Publishers, 2010.

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Harren, Jhoti, and Leach Andrew R, eds. Structure-based drug discovery. Dordrecht: Springer, 2007.

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Takao, Kumazawa, Kruger Lawrence, and Mizumura Kazue, eds. The polymodal receptor: A gateway to pathological pain. Amsterdam: Elsevier, 1996.

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Book chapters on the topic "Computational protein design"

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Saven, Jeffery G. "Computational Protein Design." In Protein Engineering Handbook, 325–42. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527634026.ch12.

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Shifman, Julia, and Anamika Singh. "Computational Protein Design." In Encyclopedia of Biophysics, 1–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-35943-9_10084-1.

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Zhou, Yichao, Bruce R. Donald, and Jianyang Zeng. "Parallel Computational Protein Design." In Methods in Molecular Biology, 265–77. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6637-0_13.

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Kuhlman, Brian, Tim Jacobs, and Tom Linskey. "Computational Design of Protein Linkers." In Methods in Molecular Biology, 341–51. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3569-7_20.

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Kiss, Gert, Scott A. Johnson, Geoffrey Nosrati, Nihan Çelebi-Ölçüm, Seonah Kim, Robert Paton, and Kendal N. Houk. "Computational Design of New Protein Catalysts." In Modeling of Molecular Properties, 241–66. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527636402.ch16.

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Samish, Ilan. "The Framework of Computational Protein Design." In Methods in Molecular Biology, 3–19. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6637-0_1.

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O’Mara, Megan L., and Evelyne Deplazes. "Polypeptide and Protein Modeling for Drug Design." In Encyclopedia of Computational Neuroscience, 2439–47. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_732.

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O’Mara, Megan L., and Evelyne Deplazes. "Polypeptide and Protein Modeling for Drug Design." In Encyclopedia of Computational Neuroscience, 1–9. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7320-6_732-1.

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Johnson, Lucas B., Thaddaus R. Huber, and Christopher D. Snow. "Methods for Library-Scale Computational Protein Design." In Methods in Molecular Biology, 129–59. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1486-9_7.

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Carbonell, Pablo, and Jean-Yves Trosset. "Computational Protein Design Methods for Synthetic Biology." In Methods in Molecular Biology, 3–21. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1878-2_1.

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Conference papers on the topic "Computational protein design"

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Arikawa, Keisuke. "A Computational Framework for Predicting the Motions of a Protein System From a Robot Kinematics Viewpoint." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12527.

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There is an analogy between the kinematic structures of proteins and robotic mechanisms. On the basis of this analogy, we have so far developed some methods for predicting the internal motions of proteins from their three-dimensional structural data in protein data bank (PDB). However, these methods are basically applicable to a single protein molecule. In this study, we extended these methods to apply them to systems that consist of multiple molecules including proteins (protein systems), and developed a computational framework for predicting the motions of the molecules. The model used in this method is a type of elastic network model. In particular, proteins are modeled as a robot manipulator constrained by the springs (the dihedral angles on the main chains correspond to the joint angles). The interactions between molecules are also modeled as springs. The basic concept for predicting the motions is based on the analysis of structural compliance. By applying statically balanced forces to the model in various directions, we extracted those motions with larger structural compliance. To reduce the computational time, we formulated the method with the prospect of efficient computation including parallel computation. In addition, we developed a preparatory computer program implementing the proposed algorithms, and analyzed some protein systems. The results showed that the proposed computational framework can efficiently analyze large protein systems.
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Tortosa, Pablo. "Active Sites by Computational Protein Design." In FROM PHYSICS TO BIOLOGY: The Interface between Experiment and Computation - BIFI 2006 II International Congress. AIP, 2006. http://dx.doi.org/10.1063/1.2345625.

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LI, XIANG, and JIE LIANG. "COMPUTATIONAL DESIGN OF COMBINATORIAL PEPTIDE LIBRARY FOR MODULATING PROTEIN-PROTEIN INTERACTIONS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702456_0004.

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SMADBECK, JAMES, GEORGE A. KHOURY, MEGHAN B. PETERSON, and CHRISTODOULOS A. FLOUDAS. "ADVANCES IN DE NOVO PROTEIN DESIGN FOR MONOMERIC, MULTIMERIC, AND CONFORMATIONAL SWITCH PROTEINS." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814520829_0010.

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Shahbazi, Zahra, Horea T. Ilies¸, and Kazem Kazerounian. "On Hydrogen Bonds and Mobility of Protein Molecules." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87470.

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Modeling protein molecules as kinematic chains provides the foundation for developing powerful approaches to the design, manipulation and fabrication of peptide based molecules and devices. Nevertheless, these models possess a high number of degrees of freedom (DOF) with considerable computational implications. On the other hand, real protein molecules appear to exhibits a much lower mobility during the folding process than what is suggested by existing kinematic models. The key contributor to the lower mobility of real proteins is the formation of Hydrogen bonds during the folding process. In this paper we explore the pivotal role of Hydrogen bonds in determining the structure and function of the proteins from the point of view of mechanical mobility. The existing geometric criteria on the formation of Hydrogen bonds are reviewed and a new set of geometric criteria are proposed. We show that the new criteria better correlate the number of predicted Hydrogen bonds with those established by biological principles than other existing criteria. Furthermore, we employ established tools in kinematics mobility analysis to evaluate the internal mobility of protein molecules, and to identify the rigid and flexible segments of the proteins. Our results show that the developed procedure significantly reduces the DOF of the protein models, with an average reduction of 94%. Such a dramatic reduction in the number of DOF can have has enormous computational implications in protein folding simulations.
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Simoncini, David, Sophie Barbe, Thomas Schiex, and Sébastien Verel. "Fitness landscape analysis around the optimum in computational protein design." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205455.3205626.

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Albuquerque, Vitória, Ernesto Caffarena, and João Silva. "Computational design of neutralizing scfv for gastric cancer protein cldn6." In International Symposium on Immunobiologicals. Instituto de Tecnologia em Imunobiológicos, 2022. http://dx.doi.org/10.35259/isi.2022_52286.

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Lillian, Todd D., N. C. Perkins, and S. Goyal. "Computational Elastic Rod Model Applied to DNA Looping." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34956.

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DNA is a long flexible biopolymer containing genetic information. Proteins often take advantage of DNA’s inherent flexibility to perform their cellular functions. Here we present selected results from our computational studies of the mechanical looping of DNA by the Lactose repressor protein. The Lactose repressor resides in the bacterium E. coli and deforms DNA into a loop as a means of controlling the production of enzymes necessary for digesting lactose. We examine this looping process using a computational rod model [1–3] to understand the strain energy and geometry for the resultant DNA loops. Our model captures the multiple looped conformations of the molecule arising from both multiple boundary conditions and geometric nonlinearities. In addition, the model captures the periodic variation of strain energy with base-pair length as suggested by repression experiments (see, for example, [4, 5]).
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Chen, Mao, Chao Yu, and Jiagang Ouyang. "A Tabu Search Algorithm for the Protein Folding Problem." In 2009 Second International Symposium on Computational Intelligence and Design. IEEE, 2009. http://dx.doi.org/10.1109/iscid.2009.66.

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Koh, Sung K., and G. K. Ananthasuresh. "Design of HP Models of Proteins by Energy Gap Criterion Using Continuous Modeling and Optimization." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57598.

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The sequence of 20 types of amino acid residues in a heteropolymer chain of a protein is believed to be the basis for the 3-D conformation (folded structure) that a protein assumes to serve its functions. We present a deterministic optimization method to design the sequence of a simplified model of proteins for a desired conformation. A design methodology developed for the topology optimization of compliant mechanisms is adapted here by converting the discrete combinatorial problem of protein sequence design to a continuous optimization problem. It builds upon our recent work which used a minimum energy criterion on a deterministic approach to protein design using continuous models. This paper focuses on the energy gap criterion, which is argued to be one of the most important characteristics determining the stable folding of a protein chain. The concepts, methodology, and illustrative examples are presented using HP models of proteins where only two types (H: hydrophobic and P: polar) of monomers are considered instead of 20. The highlight of the method presented in this paper is the drastic reduction in computational costs.
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Reports on the topic "Computational protein design"

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Sapiro, Guillermo. New Forcefields and Algorithms for Computational Protein Design. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada428012.

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Avdjieva, Irena, Ivan Terziyski, Gergana Zahmanova, Anelia Nisheva, and Dimitar Vassilev. Fusion Protein Design with Computational Homologybased Structure Prediction. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, July 2021. http://dx.doi.org/10.7546/crabs.2021.07.07.

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Gershoni, Jonathan M., David E. Swayne, Tal Pupko, Shimon Perk, Alexander Panshin, Avishai Lublin, and Natalia Golander. Discovery and reconstitution of cross-reactive vaccine targets for H5 and H9 avian influenza. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7699854.bard.

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Research objectives: Identification of highly conserved B-cell epitopes common to either H5 or H9 subtypes of AI Reconstruction of conserved epitopes from (1) as recombinantimmunogens, and testing their suitability to be used as universal vaccine components by measuring their binding to Influenza vaccinated sera of birds Vaccination of chickens with reconstituted epitopes and evaluation of successful vaccination, clinical protection and viral replication Development of a platform to investigate the dynamics of immune response towards infection or an epitope based vaccine Estimate our ability to focus the immune response towards an epitope-based vaccine using the tool we have developed in (D) Summary: This study is a multi-disciplinary study of four-way collaboration; The SERPL, USDA, Kimron-Israel, and two groups at TAU with the purpose of evaluating the production and implementation of epitope based vaccines against avian influenza (AI). Systematic analysis of the influenza viral spike led to the production of a highly conserved epitope situated at the hinge of the HA antigen designated “cluster 300” (c300). This epitope consists of a total of 31 residues and was initially expressed as a fusion protein of the Protein 8 major protein of the bacteriophagefd. Two versions of the c300 were produced to correspond to the H5 and H9 antigens respectively as well as scrambled versions that were identical with regard to amino acid composition yet with varied linear sequence (these served as negative controls). The recombinantimmunogens were produced first as phage fusions and then subsequently as fusions with maltose binding protein (MBP) or glutathioneS-transferase (GST). The latter were used to immunize and boost chickens at SERPL and Kimron. Furthermore, vaccinated and control chickens were challenged with concordant influenza strains at Kimron and SEPRL. Polyclonal sera were obtained for further analyses at TAU and computational bioinformatics analyses in collaboration with Prof. Pupko. Moreover, the degree of protection afforded by the vaccination was determined. Unfortunately, no protection could be demonstrated. In parallel to the main theme of the study, the TAU team (Gershoni and Pupko) designed and developed a novel methodology for the systematic analysis of the antibody composition of polyclonal sera (Deep Panning) which is essential for the analyses of the humoral response towards vaccination and challenge. Deep Panning is currently being used to monitor the polyclonal sera derived from the vaccination studies conducted at the SEPRL and Kimron.
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Fernando, P. U. Ashvin Iresh, Gilbert Kosgei, Matthew Glasscott, Garrett George, Erik Alberts, and Lee Moores. Boronic acid functionalized ferrocene derivatives towards fluoride sensing. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44762.

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In this technical report (TR), a robust, readily synthesized molecule with a ferrocene core appended with one or two boronic acid moieties was designed, synthesized, and used toward F- (free fluoride) detection. Through Lewis acid-base interactions, the boronic acid derivatives are capable of binding with F- in an aqueous solution via ligand exchange reaction and is specific to fluoride ion. Fluoride binding to ferrocene causes significant changes in fluorescence or electrochemical responses that can be monitored with field-portable instrumentation at concentrations below the WHO recommended limit. The F- binding interaction was further monitored via proton nuclear magnetic resonance spectroscopy (1H-NMR). In addition, fluorescent spectroscopy of the boronic acid moiety and electrochemical monitoring of the ferrocene moiety will allow detection and estimation of F- concentration precisely in a solution matrix. The current work shows lower detection limit (LOD) of ~15 μM (285 μg/L) which is below the WHO standards. Preliminary computational calculations showed the boronic acid moieties attached to the ferrocene core interacted with the fluoride ion. Also, the ionization diagrams indicate the amides and the boronic acid groups can be ionized forming strong ionic interactions with fluoride ions in addition to hydrogen bonding interactions.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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