Conceptual Framework and Methodology for Analysing Previous Molecular Docking Results

Modern drug discovery relies on in-silico computational simulations such as molecular docking. Molecular docking models biochemical interactions to predict where and how two molecules would bind. The results of large-scale molecular docking simulations can provide valuable insight into the relationship between two molecules. This is useful to a biomedical scientist before conducting in-vitro or in-vivo wet-lab experiments. Although this ˝eld has seen great advancements, feedback from biomedical scientists shows that there is a need for storage and further analysis of molecular docking results. To meet this need, biomedical scientists need to have access to computing, data, and network resources, and require speci˝c knowledge or skills they might lack. Therefore, a conceptual framework speci˝cally tailored to enable biomedical scientists to reuse molecular docking results, and a methodology which uses regular input from scientists, has been proposed. The framework is composed of 5 types of elements and 13 interfaces. The methodology is light and relies on frequent communication between biomedical sciences and computer science experts, speci˝ed by particular roles. It shows how developers can bene˝t from using the framework which allows them to determine whether a scenario ˝ts the framework, whether an already implemented element can be reused, or whether a newly proposed tool can be used as an element. Three scenarios that show the versatility of this new framework and the methodology based on it, have been identi˝ed and implemented. A methodical planning and design approach was used and it was shown that the implementations are at least as usable as existing solutions. To eliminate the need for access to expensive computing infrastructure, state-of-the-art cloud computing techniques are used. The implementations enable faster identi˝cation of new molecules for use in docking, direct querying of existing databases, and simpler learning of good molecular docking practice without the need to manually run multiple tools. Thus, the framework and methodol-ogy enable more user-friendly implementations, and less error-prone use of computational methods in drug discovery. Their use could lead to more e˙ective discovery of new drugs

Similarités et divergences, globales et locales, entre structures protéiques

This thesis focusses on local and global similarities and divergences inside protein structures. First, structures are scored, with criteria of similarity and distance in order to provide a supervised classification. This structural domain classification inside existing hierarchical databases is possible by using dominances and learning. These methods allow to assign new domains with accuracy and exactly. Second we focusses on local similarities and proposed a method of protein comparison modelisation inside graphs. Graph traversal allows to find protein similar substructures. This method is based on compatibility between elements and criterion of distances. We can use it and detect events such that circular permutations, hinges and structural motif repeats. Finally we propose a new approach of accurate protein structure analysis that focused on divergences between similar structures.Cette thèse s'articule autour de la détection de similarités globales et locales dans les structures protéiques. Premièrement les structures sont comparées, mesurées en termes de distance métrique dans un but de classification supervisée. Cette classification des domaines structuraux au sein de classifications hiérarchiques se fait par le biais de dominances et d'apprentissages permettant d'assigner plus rapidement et de manière exacte de nouveaux domaines. Deuxièmement, nous proposons une méthode de manière de traduire un problème biologique dans les formalisme des graphes. Puis nous résolvons ce problème via le parcours de ces graphes pour extraire les différentes sous-structures similaires. Cette méthode repose sur des notions de compatibilités entre éléments des structures ainsi que des critères de distances entre éléments. Ces techniques sont capables de détecter des événements tels que des permutations circulaires, des charnières (flexibilité) et des répétitions de motifs structuraux. Finalement nous proposons une nouvelle approche dans l'analyse fine de structures afin de faciliter la recherche de régions divergentes entre structures 3D fortement similaires

Meta-analysis of protein structural alignment

The three-dimensional structure of a protein molecule provides significant insight into its biological function. Structural alignment of proteins is an important and widely performed task in the analysis of protein structures, whereby functionally and evolutionarily important segments are identified. However, structural alignment is a computationally difficult problem and a large number of heuristics introduced to solve it do not agree on their results. Consequently, there is no widely accepted solution to the structure alignment problem. In this study, we present a meta-analysis approach to generate a re-optimized, best-of-all result using the alignments generated from several popular methods. Evaluations of the methods on a large set of benchmark pairwise alignments indicate that TM-align provides superior alignments (except for RMSD), compared to other methods we have surveyed. Smolign provides smaller cores than other methods with best RMSD values. The re-optimization of the alignments using TM-align’s optimization method does not alter the relative performance of the methods. We also provide visualization approaches to delineate the relationships of the alignment methods.M.S., Biomedical Engineering -- Drexel University, 201