Thèses sur le sujet « Computer-based drug design »

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.

The overall aim of the research is to develop a computational platform based on HINT paradigm for manipulating, predicting and analyzing biomacromolecular-ligand structure. A second synergistic goal is to apply the above methodology to design novel and potent anti-cancer agents. The crucial role of the microtubule in cell division has identified tubulin as an interesting target for the development of therapeutics for cancer. Pyrrole-containing molecules derived from nature have proven to be particularly useful as lead compounds for drug development. We have designed and developed a series of substituted pyrroles that inhibit growth and promote death of breast tumor cells at nM and μM concentrations in human breast tumor cell lines. In another project, stilbene analogs were designed and developed as microtubule depolymerizing agents that showed anti-leukemic activity. A molecular modeling study was carried out to accurately represent the complex structure and the binding mode of a new class of tubulin inhibitors that bind at the αβ-tubulin colchicine site. These studies coupled with HINT interaction analyses were able to describe the complex structure and the binding modes of inhibitors. Qualitative analyses of the results showed general agreement with the experimental in vitro biological activity for these derivatives. Consequently, we have been designing new analogs that can be synthesized and tested; we believe that these molecules will be highly selective against cancer cells with minimal toxicity to the host tissue. Another goal of our research is to develop computational tools for drug design. The development and implementation of a novel cavity detection algorithm is also reported and discussed. The algorithm named VICE (Vectorial Identification of Cavity Extents) utilizes HINT toolkit functions to identify and delineate a binding pocket in a protein. The program is based on geometric criteria and applies simple integer grid maps to delineate binding sites. The algorithm was extensively tested on a diverse set of proteins and detects binding pockets of different shapes and sizes. The study also implemented the computational titration algorithm to understand the complexity of ligand binding and protonation state in the active site of HIV-1 protease. The Computational titration algorithm is a powerful tool for understanding ligand binding in a complex biochemical environment and allows generating hypothesis on the best model for binding.

A complete understanding of complex formation between proteins and ligands, a crucial matter for pharmacology and, more in general, in biomedicine, requires a detailed knowledge of their static and dynamic atomic interactions. The main objective of this thesis is to test recent developments in conformational sampling techniques in providing such a dynamical view. We aim at developing new protocols and methods for such a study. Moreover, we want to show how its application can aid in addressing existing problems in the biophysics of protein ligand interactions. Moreover, we apply and refine novel computational approaches aiming at a comprehensive description of the protein and protein-ligand energy landscape, progressing into the rational design of new inhibitors for particular targets. We provide here a summary of the main results. PELE was used for induce fit docking in protein kinases, mammalian target of rapamycin (mTOR) and BCL-2 family protein, particularly MCL-1 protein. Results produced a detailed atomic description of the binding modes of ligand/drug to the selected target. Overall, these results provide new data to understand the mechanism of action of these molecules, and provide new structural data that will allow the development of more Specific inhibitors for cancer treatments. Importantly, we demonstrate the critical role of sampling the protein-ligand dynamics in order to improve the docking score. Moreover, the findings reported here clearly shown the capabilities of PG (and its derivatives) for use in particular apoptotic targets. Following the previous goal, we aim at the implementation of the atomic detailed knowledge into the rational design of new inhibitors, aiming to enhance specificity and binding strength. Motivated by our success with validation studies (applied to several systems for protein-ligand interaction and induce fit procedure) we attempted to design a new inhibitor for a specific target. For doing so, we used the system from our second study: Molecular interactions of prodiginines with the BH3 domain of BCL-2 family members. We have shown how PELE can be used in effectively design improved compounds with significant better docking results. The PELE was applied to steroid Nuclear Receptors to unbiased simulations, where substrate/ligands were placed in the active site, to freely move through the protein and finding the channels, or outside the receptors allowed ligand to freely explore the protein surface. In this study, we demonstrated the applicability of the PELE method in solving relevant biophysical problems. In particular, using PELE we introduced a new structural and dynamic paradigm for ligand binding in steroid nuclear receptors. Using PELE, we create a protocol involving sequence comparison and all­atom protein-ligand induced fit simulations to predict PR resistance at the molecular level. We introduced a significant advance in predicting the affinity of different drugs against HIV-1 protease with several mutations. This study shows how computational techniques are capable of quantitatively discriminating resistance variants of HIV-1 protease. This application is fully automated and installed on PELE web server. Beside these main objectives based on methods application, we aim to add methodological improvements derived from the application and validation studies. We performed method development and studied PELE protocols to model long-time protein dynamics by means of normal mode perturbation and constrained minimization. New backbone perturbation combined with normal modes increased the capability of PELE method to explore local dynamics and large conformational changes.

The work of this thesis was focused on the development of high-performance algorithms for a new generation of molecular descriptors, with many advantages with respect to its predecessors, suitable for diverse applications in the field of drug design, as well as its implementation in commercial grade scientific software (Pentacle). As a first step, we developed a new algorithm (AMANDA) for discretizing molecular interaction fields which allows extracting from them the most interesting regions in an efficient way. This algorithm was incorporated into a new generation of alignmentindependent molecular descriptors, named GRIND-2. The computing speed and efficiency of the new algorithm allow the application of these descriptors in virtual screening. In addition, we developed a new alignment-independent encoding algorithm (CLACC) producing quantitative structure-activity relationship models which have better predictive ability and are easier to interpret than those obtained with other methods.<br>El trabajo que se presenta en esta tesis se ha centrado en el desarrollo de algoritmos de altas prestaciones para la obtención de una nueva generación de descriptores moleculares, con numerosas ventajas con respecto a sus predecesores, adecuados para diversas aplicaciones en el área del diseño de fármacos, y en su implementación en un programa científico de calidad comercial (Pentacle). Inicialmente se desarrolló un nuevo algoritmo de discretización de campos de interacción molecular (AMANDA) que permite extraer eficientemente las regiones de máximo interés. Este algoritmo fue incorporado en una nueva generación de descriptores moleculares independientes del alineamiento, denominados GRIND-2. La rapidez y eficiencia del nuevo algoritmo permitieron aplicar estos descriptores en cribados virtuales. Por último, se puso a punto un nuevo algoritmo de codificación independiente de alineamiento (CLACC) que permite obtener modelos cuantitativos de relación estructura-actividad con mejor capacidad predictiva y mucho más fáciles de interpretar que los obtenidos con otros métodos.

<p>Proteins are involved in all of life's processes and are also responsible for many diseases. Thus, engineering proteins to perform new tasks could revolutionize many areas of biomedical research. One promising technique for protein engineering is computational structure-based protein design (CSPD). CSPD algorithms search large protein conformational spaces to approximate biophysical quantities. In this dissertation we present new algorithms to realistically and accurately model how amino acid mutations change protein structure. These algorithms model continuous flexibility, protein ensembles and positive/negative design, while providing guarantees on the output. Using these algorithms and the OSPREY protein design program we design and apply protocols for three biomedically-relevant problems: (i) prediction of new drug resistance mutations in bacteria to a new preclinical antibiotic, (ii) the redesign of llama antibodies to potentially reduce their immunogenicity for use in preclinical monkey studies, and (iii) scaffold-based anti-HIV antibody design. Experimental validation performed by our collaborators confirmed the importance of the algorithms and protocols.</p><br>Dissertation

Tse, Ching Man.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (p. 188-200).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.vi<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Obtain information on drug targets --- p.3<br>Chapter 1.2 --- Drug Design --- p.5<br>Chapter 1.3 --- Interface for interaction --- p.9<br>Chapter 1.4 --- Summary --- p.10<br>Chapter 2 --- Background Study --- p.12<br>Chapter 2.1 --- Protein Function Prediction --- p.16<br>Chapter 2.2 --- Drug Design --- p.37<br>Chapter 2.3 --- Visualisation and Interaction in Biomedic --- p.44<br>Chapter 3 --- Overview --- p.48<br>Chapter 3.1 --- Protein prediction using secondary structure analysis --- p.52<br>Chapter 3.2 --- Knowledge-driven ligand design --- p.55<br>Chapter 3.3 --- Interactive interface in virtual reality --- p.57<br>Chapter 4 --- Protein Function Prediction --- p.60<br>Chapter 4.1 --- Introduction --- p.61<br>Chapter 4.1.1 --- Motivation --- p.61<br>Chapter 4.1.2 --- Objective --- p.62<br>Chapter 4.1.3 --- Overview --- p.63<br>Chapter 4.2 --- Methods and Design --- p.66<br>Chapter 4.2.1 --- Feature Cell --- p.68<br>Chapter 4.2.2 --- Heterogeneous Vector --- p.71<br>Chapter 4.2.3 --- Feature Cell Similarity --- p.75<br>Chapter 4.2.4 --- Heterogeneous Vector Similarity --- p.79<br>Chapter 4.3 --- Experiments --- p.85<br>Chapter 4.3.1 --- Data Preparation --- p.85<br>Chapter 4.3.2 --- Experimental Methods --- p.87<br>Chapter 4.4 --- Results --- p.97<br>Chapter 4.4.1 --- Scalability --- p.97<br>Chapter 4.4.2 --- Cluster Quality --- p.99<br>Chapter 4.4.3 --- Classification Quality --- p.102<br>Chapter 4.5 --- Discussion --- p.103<br>Chapter 4.6 --- Conclusion --- p.104<br>Chapter 5 --- Drug Design --- p.106<br>Chapter 5.1 --- Introduction --- p.107<br>Chapter 5.1.1 --- Motivation --- p.107<br>Chapter 5.1.2 --- Objective --- p.109<br>Chapter 5.1.3 --- Overview --- p.109<br>Chapter 5.2 --- Methods --- p.111<br>Chapter 5.2.1 --- Fragment Joining --- p.115<br>Chapter 5.2.2 --- Genetic Operators --- p.116<br>Chapter 5.2.3 --- Post-Processing --- p.124<br>Chapter 5.3 --- Experiments --- p.128<br>Chapter 5.3.1 --- Data Preparation --- p.129<br>Chapter 5.3.2 --- Experimental Methods --- p.132<br>Chapter 5.4 --- Results --- p.134<br>Chapter 5.4.1 --- Binding Pose --- p.134<br>Chapter 5.4.2 --- Free Energy and Molecular Weight --- p.137<br>Chapter 5.4.3 --- Execution Time --- p.138<br>Chapter 5.4.4 --- Handling Phosphorus --- p.138<br>Chapter 5.5 --- Discussions --- p.139<br>Chapter 5.6 --- Conclusion --- p.140<br>Chapter 6 --- Interface in Virtual Reality --- p.142<br>Chapter 6.1 --- Introduction --- p.143<br>Chapter 6.1.1 --- Motivation --- p.143<br>Chapter 6.1.2 --- Objective --- p.145<br>Chapter 6.1.3 --- Overview --- p.145<br>Chapter 6.2 --- Methods and Design --- p.146<br>Chapter 6.2.1 --- Hybrid Drug Synthesis --- p.147<br>Chapter 6.2.2 --- Interactive Interface in Virtual Reality --- p.154<br>Chapter 6.3 --- Experiments and Results --- p.171<br>Chapter 6.3.1 --- Data Preparation --- p.171<br>Chapter 6.3.2 --- Experimental Settings --- p.172<br>Chapter 6.3.3 --- Results --- p.173<br>Chapter 6.4 --- Discussions --- p.176<br>Chapter 6.5 --- Conclusions --- p.179<br>Chapter 7 --- Conclusion --- p.180<br>A Glossary --- p.184<br>Bibliography --- p.188

Li, Hongjian.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (p. 132-156).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Abstract in Chinese --- p.iii<br>Acknowledgement --- p.iv<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Motivation --- p.2<br>Chapter 1.2 --- Objective --- p.2<br>Chapter 1.3 --- Method --- p.3<br>Chapter 1.4 --- Outline --- p.4<br>Chapter 2 --- Background --- p.7<br>Chapter 2.1 --- Overview of the Pharmaceutical Industry --- p.7<br>Chapter 2.2 --- The Process of Modern Drug Discovery --- p.10<br>Chapter 2.2.1 --- Development of an Innovative Idea --- p.10<br>Chapter 2.2.2 --- Establishment of a Project Team --- p.11<br>Chapter 2.2.3 --- Target Identification --- p.11<br>Chapter 2.2.4 --- Hit Identification --- p.12<br>Chapter 2.2.5 --- Lead Identification --- p.13<br>Chapter 2.2.6 --- Lead Optimization --- p.14<br>Chapter 2.2.7 --- Clinical Trials --- p.14<br>Chapter 2.3 --- Drug Discovery via Computational Means --- p.15<br>Chapter 2.3.1 --- Structure-Based Virtual Screening --- p.16<br>Chapter 2.3.2 --- Computational Synthesis of Potent Ligands --- p.20<br>Chapter 2.3.3 --- General-Purpose Computing on GPU --- p.23<br>Chapter 3 --- Approximate Matching of DNA Patterns --- p.26<br>Chapter 3.1 --- Problem Definition --- p.27<br>Chapter 3.2 --- Motivation --- p.28<br>Chapter 3.3 --- Background --- p.30<br>Chapter 3.4 --- Method --- p.32<br>Chapter 3.4.1 --- Binary Representation --- p.32<br>Chapter 3.4.2 --- Agrep Algorithm --- p.32<br>Chapter 3.4.3 --- CUDA Implementation --- p.34<br>Chapter 3.5 --- Experiments and Results --- p.39<br>Chapter 3.6 --- Discussion --- p.44<br>Chapter 3.7 --- Availability --- p.45<br>Chapter 3.8 --- Conclusion --- p.47<br>Chapter 4 --- Structure-Based Virtual Screening --- p.50<br>Chapter 4.1 --- Problem Definition --- p.51<br>Chapter 4.2 --- Motivation --- p.52<br>Chapter 4.3 --- Medicinal Background --- p.52<br>Chapter 4.4 --- Computational Background --- p.59<br>Chapter 4.4.1 --- Scoring Function --- p.59<br>Chapter 4.4.2 --- Optimization Algorithm --- p.65<br>Chapter 4.5 --- Method --- p.68<br>Chapter 4.5.1 --- Scoring Function --- p.69<br>Chapter 4.5.2 --- Inactive Torsions --- p.72<br>Chapter 4.5.3 --- Optimization Algorithm --- p.73<br>Chapter 4.5.4 --- C++ Implementation Tricks --- p.74<br>Chapter 4.6 --- Data --- p.75<br>Chapter 4.6.1 --- Proteins --- p.75<br>Chapter 4.6.2 --- Ligands --- p.76<br>Chapter 4.7 --- Experiments and Results --- p.77<br>Chapter 4.7.1 --- Program Validation --- p.77<br>Chapter 4.7.2 --- Virtual Screening --- p.81<br>Chapter 4.8 --- Discussion --- p.89<br>Chapter 4.9 --- Availability --- p.90<br>Chapter 4.10 --- Conclusion --- p.91<br>Chapter 5 --- Computational Synthesis of Ligands --- p.92<br>Chapter 5.1 --- Problem Definition --- p.93<br>Chapter 5.2 --- Motivation --- p.93<br>Chapter 5.3 --- Background --- p.94<br>Chapter 5.4 --- Method --- p.97<br>Chapter 5.4.1 --- Selection --- p.99<br>Chapter 5.4.2 --- Mutation --- p.102<br>Chapter 5.4.3 --- Crossover --- p.102<br>Chapter 5.4.4 --- Split --- p.103<br>Chapter 5.4.5 --- Merging --- p.104<br>Chapter 5.4.6 --- Drug Likeness Testing --- p.104<br>Chapter 5.5 --- Data --- p.105<br>Chapter 5.5.1 --- Proteins --- p.105<br>Chapter 5.5.2 --- Initial Ligands --- p.107<br>Chapter 5.5.3 --- Fragments --- p.107<br>Chapter 5.6 --- Experiments and Results --- p.109<br>Chapter 5.6.1 --- Binding Conformation --- p.112<br>Chapter 5.6.2 --- Free Energy and Molecule Weight --- p.115<br>Chapter 5.6.3 --- Execution Time --- p.116<br>Chapter 5.6.4 --- Support for Phosphorus --- p.116<br>Chapter 5.7 --- Discussion --- p.120<br>Chapter 5.8 --- Availability --- p.123<br>Chapter 5.9 --- Conclusion --- p.123<br>Chapter 5.10 --- Personal Contribution --- p.124<br>Chapter 6 --- Conclusion --- p.125<br>Chapter 6.1 --- Conclusion --- p.125<br>Chapter 6.2 --- Future Work --- p.128<br>Chapter A --- Publications --- p.130<br>Chapter A.1 --- Conference Papers --- p.130<br>Chapter A.2 --- Journal Papers --- p.131<br>Bibliography --- p.132

Computer-aided drug design is an exceptionally useful tool for screening a large number of potential drug molecules to evaluate their therapeutic potential. This technique is both effective and economical. Approximately 120 imidazole-containing molecules were computationally designed and evaluated using gas-phase and solution-phase simulations to assess their propensity for acting as a chelating agent with twenty-six biologically relevant cations. Of particular interest was their ability to chelate Zn2+ and Ca2+, which play a key role in the degradation of neurons following an ischemic stroke. The ultimate goal was to design a small molecule that could be administered before a medical procedure that featured stroke as a possible side effect. In the event that a stroke occurred, the destruction of neurons caused by release of excess Ca2+ and Zn2+ would be diminished and the patient would maintain motor and cognitive function. Promising in silico results were obtained.