Development of a normal mode-based geometric simulation approach for investigating the intrinsic mobility of proteins

Specific functions of biological systems often require conformational transitions of macromolecules. Thus, being able to describe and predict conformational changes of biological macromolecules is not only important for understanding their impact on biological function, but will also have implications for the modelling of (macro)molecular complex formation and in structure-based drug design approaches. The “conformational selection model” provides the foundation for computational investigations of conformational fluctuations of the unbound protein state. These fluctuations may reveal conformational states adopted by the bound proteins. The aim of this work is to incorporate directional information in a geometry-based approach, in order to sample biologically relevant conformational space extensively. Interestingly, coarse-grained normal mode (CGNM) approaches, e.g., the elastic network model (ENM) and rigid cluster normal mode analysis (RCNMA), have emerged recently and provide directions of intrinsic motions in terms of harmonic modes (also called normal modes). In my previous work and in other studies it has been shown that conformational changes upon ligand binding occur along a few low-energy modes of unbound proteins and can be efficiently calculated by CGNM approaches. In order to explore the validity and the applicability of CGNM approaches, a large-scale comparison of essential dynamics (ED) modes from molecular dynamics (MD) simulations and normal modes from CGNM was performed over a dataset of 335 proteins. Despite high coarse-graining, low frequency normal modes from CGNM correlate very well with ED modes in terms of directions of motions (average maximal overlap is 0.65) and relative amplitudes of motions (average maximal overlap is 0.73). In order to exploit the potential of CGNM approaches, I have developed a three-step approach for efficient exploration of intrinsic motions of proteins. The first two steps are based on recent developments in rigidity and elastic network theory. Initially, static properties of the protein are determined by decomposing the protein into rigid clusters using the graph-theoretical approach FIRST at an all-atom representation of the protein. In a second step, dynamic properties of the molecule are revealed by the rotations-translations of blocks approach (RTB) using an elastic network model representation of the coarse-grained protein. In the final step, the recently introduced idea of constrained geometric simulations of diffusive motions in proteins is extended for efficient sampling of conformational space. Here, the low-energy (frequency) normal modes provided by the RCNMA approach are used to guide the backbone motions. The NMSim approach was validated on hen egg white lysozyme by comparing it to previously mentioned simulation methods in terms of residue fluctuations, conformational space explorations, essential dynamics, sampling of side-chain rotamers, and structural quality. Residue fluctuations in NMSim generated ensemble is found to be in good agreement with MD fluctuations with a correlation coefficient of around 0.79. A comparison of different geometry-based simulation approaches shows that FRODA is restricted in sampling the backbone conformational space. CONCOORD is restricted in sampling the side-chain conformational space. NMSim sufficiently samples both the backbone and the side-chain conformations taking experimental structures and conformations from the state of the art MD simulation as reference. The NMSim approach is also applied to a dataset of proteins where conformational changes have been observed experimentally, either in domain or functionally important loop regions. The NMSim simulations starting from the unbound structures are able to reach conformations similar to ligand bound conformations (RMSD 0.7) between the RMS fluctuations derived from NMSim generated structures and two experimental structures are observed. Furthermore, intrinsic fluctuations in NMSim simulation correlate with the region of loop conformational changes observed upon ligand binding in 2 out of 3 cases. The NMSim generated pathway of conformational change from the unbound structure to the ligand bound structure of adenylate kinase is validated by a comparison to experimental structures reflecting different states of the pathway as proposed by previous studies. Interestingly, the generated pathway confirms that the LID domain closure precedes the closing of the NMPbind domain, even if no target conformation is provided in NMSim. Hence, the results in this study show that, incorporating directional information in the geometry-based approach NMSim improves the sampling of biologically relevant conformational space and provides a computationally efficient alternative to state of the art MD simulations.Konformationsänderungen von Proteinen sind häufig eine grundlegende Voraussetzung für deren biologische Funktion. Die genaue Charakterisierung und Vorhersage dieser Konformationsänderungen ist für das Verständnis ihres Einflusses auf die Funktion erforderlich. Eines der dafür am häufigsten verwendeten und genauesten computergestützten Verfahren ist die Molekulardynamik-Simulationen (MD Simulationen). Diese sind jedoch nach wie vor sehr rechenintensiv und durchmustern den Konformationsraum nur in begrenztem Maße. Daher wurden Anstrengungen unternommen, alternative geometriebasierte Methoden (wie etwa CONCOORD oder FRODA) zu entwickeln, die auf einer reduzierten Darstellung von Proteinen beruhen. Das Ziel dieser Arbeit ist es, Richtungsinformationen in einen geometriebasierten Ansatz zu integrieren, und so den biologisch relevanten Konformationsraum erschöpfend zu durchmustern. Diese Idee führte kürzlich zur Entwicklung von „coarse-grained normal mode“ (CGNM) Methoden, wie zum Beispiel dem „elastic network model“ (ENM) und der von mir in vorangegangenen Arbeiten entwickelte „rigid cluster normal mode analysis“ (RCNMA). Beide Methoden liefern die gewünschte Richtungsinformation der intrinsischen Bewegungen eines Proteins in Form von harmonischen Moden (auch Normalmoden). Um die Aussagekraft, Robustheit und breite Anwendbarkeit solcher CGNM Verfahren zu untersuchen, wurde im Rahmen dieser Dissertation ein umfangreicher Vergleich zwischen „essential dynamics“ (ED) Moden aus MD Simulationen und Normalmoden aus CGNM Berechnungen durchgeführt. Der zugrundeliegende Datensatz enthielt 335 Proteine. Obwohl die CGNM Verfahren eine stark vereinfachte Darstellung für Proteine verwenden, korrelieren die niederfrequenten Moden dieser Verfahren bezüglich ihrer Bewegungs-Richtung (durchschnittliche maximale Überschneidung: 0,65) und -Amplitude (durchschnittliche maximale Überschneidung: 0,73) sehr gut mit ED Moden. Im Durchschnitt beschreibt das erste Viertel der Normalmoden 85 % des Raumes, der durch die ersten fünf ED Moden aufgespannt wird. Um die Leistungsfähigkeit von CGNM Verfahren genauer zu bestimmen, wurde im Rahmen der vorliegenden Studie eine dreistufige Methode zur Untersuchung der intrinsischen Dynamik von Proteinen entwickelt. Die ersten beiden Stufen basieren auf neusten Entwicklungen in der Rigiditäts-Theorie und der Beschreibung von elastischen Netzwerken. Diese sind im RCNMA Ansatz verwirklich und ermöglichen die Bestimmung der Normalmoden. Im letzten Schritt werden die Bewegungen des Proteinrückgrates entlang der mittels RCNMA erzeugten niederenergetischen Normalmoden ausgerichtet. Die Seitenkettenkonformrationen werden dabei durch Diffusionsbewegungen hin zu energetisch günstigen Rotameren erzeugt. Dies ist ein iterativer Prozess, bestehend aus mehreren kleineren Schritten, in denen jeweils intermediäre Konformationen erzeugt werden. Zur Validierung des NMSim Ansatzes wurde dieser mit den anderen zuvor genannten Simulationsmethoden am Beispiel von Lysozym verglichen. Die Fluktuationen der Aminosäurereste aus dem mit NMSim erzeugten Ensemble stimmen mit berechneten Fluktuationen aus der MD Simulation gut überein (Korrelationskoeffizient R = 0,79). Ein Vergleich der unterschiedlichen geometriebasierten Simulationsansätze zeigt, dass bei FRODA die Durchmusterung des Konformationsraumes des Proteinrückrates unzureichend ist. Bei CONCOORD ist hingegen die Durchmusterung des Konformationsraumes der Seitenketten unzureichend. NMSim hingegen durchmustert sowohl den Konformationsraum des Proteinrückrates als auch den der Seitenketten angemessen, wenn man die experimentell und mittels MD Simulationen erzeugten Konformationen als Referenz verwendet. Der NMSim Ansatz wurde ebenfalls auf einen Datensatz von Proteinen angewendet, für die Konformationsänderungen in Domänen oder in funktionell wichtigen Schleifenregionen experimentell beobacht wurden. In Übereinstimmung mit dem Konformations-Selektions-Modell ist der NMSim Ansatz bei vier von fünf Proteinen, die eine Domänenbewegung aufweisen, in der Lage, ausgehend von der ungebundenen Struktur neue Konformationen zu erzeugen, die der ligandgebundenen Konformation entsprechen (RMSD 0,7) zwischen der RMS Fluktuation der durch NMSim erzeugten Konformationen und jeweils zwei experimentellen Strukturen erreicht. Hingegen korrelieren die intrinischen Fluktuationen der NMSim Simulation in zwei von drei Fällen mit dem Bereich der ligandinduzierten Konformationsänderung in den Schleifen. Der mit NMSim generierte Pfad für die Konformationsänderungen von der ungebundenen Struktur zur ligandgebundenen Struktur der Adenylat-Kinase wurde durch den Vergleich zu experimentellen Strukturen validiert, die verschiedene Zustände des Pfades widerspiegeln. Die unterschiedlichen Kristallstrukturen, die entlang der Konformationsänderungen von der ungebundenen zur ligandgebundenen Struktur liegen, werden auf dem von NMSim erzeugten Pfad durchmustert. Interessanterweise bestätigt der generierte Pfad, dass die Schließbewegung der LID Domäne derjenigen der NMPbind Domäne vorangeht, sogar wenn keine Zielkonformation für die NMSim Simulation verwendet wurde

Open Boundary Simulations of Proteins and Their Hydration Shells by Hamiltonian Adaptive Resolution Scheme

The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS) allows to perform molecular simulations in an open boundary framework. It allows to change on the fly the resolution of specific subset of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Towards this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations.Comment: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright \c{opyright} American Chemical Society after peer review and technical editing by the publishe

Predicting biomolecular function from 3D dynamics : sequence-sensitive coarse-grained elastic network model coupled to machine learning

La dynamique structurelle des biomolécules est intimement liée à leur fonction, mais très coûteuse à étudier expériementalement. Pour cette raison, de nombreuses méthodologies computationnelles ont été développées afin de simuler la dynamique structurelle biomoléculaire. Toutefois, lorsque l'on s'intéresse à la modélisation des effects de milliers de mutations, les méthodes de simulations classiques comme la dynamique moléculaire, que ce soit à l'échelle atomique ou gros-grain, sont trop coûteuses pour la majorité des applications. D'autre part, les méthodes d'analyse de modes normaux de modèles de réseaux élastiques gros-grain (ENM pour "elastic network model") sont très rapides et procurent des solutions analytiques comprenant toutes les échelles de temps. Par contre, la majorité des ENMs considèrent seulement la géométrie du squelette biomoléculaire, ce qui en fait de mauvais choix pour étudier les effets de mutations qui ne changeraient pas cette géométrie. Le "Elastic Network Contact Model" (ENCoM) est le premier ENM sensible à la séquence de la biomolécule à l'étude, ce qui rend possible son utilisation pour l'exploration efficace d'espaces conformationnels complets de variants de séquence. La présente thèse introduit le pipeline computationel ENCoM-DynaSig-ML, qui réduit les espaces conformationnels prédits par ENCoM à des Signatures Dynamiques qui sont ensuite utilisées pour entraîner des modèles d'apprentissage machine simples. ENCoM-DynaSig-ML est capable de prédire la fonction de variants de séquence avec une précision significative, est complémentaire à toutes les méthodes existantes, et peut générer de nouvelles hypothèses à propos des éléments importants de dynamique structurelle pour une fonction moléculaire donnée. Nous présentons trois exemples d'étude de relations séquence-dynamique-fonction: la maturation des microARN, le potentiel d'activation de ligands du récepteur mu-opioïde et l'efficacité enzymatique de l'enzyme VIM-2 lactamase. Cette application novatrice de l'analyse des modes normaux est rapide, demandant seulement quelques secondes de temps de calcul par variant de séquence, et est généralisable à toute biomolécule pour laquelle des données expérimentale de mutagénèse sont disponibles.The dynamics of biomolecules are intimately tied to their functions but experimentally elusive, making their computational study attractive. When modelling the effects of thousands of mutations, time-stepping methods such as classical or enhanced sampling molecular dynamics are too costly for most applications. On the other hand, normal mode analysis of coarse-grained elastic network models (ENMs) provides fast analytical dynamics spanning all timescales. However, the vast majority of ENMs consider backbone geometry alone, making them a poor choice to study point mutations which do not affect the equilibrium structure. The Elastic Network Contact Model (ENCoM) is the first sequence-sensitive ENM, enabling its use for the efficient exploration of full conformational spaces from sequence variants. The present work introduces the ENCoM-DynaSig-ML computational pipeline, in which the ENCoM conformational spaces are reduced to Dynamical Signatures and coupled to simple machine learning algorithms. ENCoM-DynaSig-ML predicts the function of sequence variants with significant accuracy, is complementary to all existing methods, and can generate new hypotheses about which dynamical features are important for the studied biomolecule's function. Examples given are the maturation efficiency of microRNA variants, the activation potential of mu-opioid receptor ligands and the effect of point mutations on VIM-2 lactamase's enzymatic efficiency. This novel application of normal mode analysis is very fast, taking a few seconds CPU time per variant, and is generalizable to any biomolecule on which experimental mutagenesis data exist

Theoretical-experimental study on protein-ligand interactions based on thermodynamics methods, molecular docking and perturbation models

The current doctoral thesis focuses on understanding the thermodynamic events of protein-ligand interactions which have been of paramount importance from traditional Medicinal Chemistry to Nanobiotechnology. Particular attention has been made on the application of state-of-the-art methodologies to address thermodynamic studies of the protein-ligand interactions by integrating structure-based molecular docking techniques, classical fractal approaches to solve protein-ligand complementarity problems, perturbation models to study allosteric signal propagation, predictive nano-quantitative structure-toxicity relationship models coupled with powerful experimental validation techniques. The contributions provided by this work could open an unlimited horizon to the fields of Drug-Discovery, Materials Sciences, Molecular Diagnosis, and Environmental Health Sciences

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening

The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds

Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest

Quantum mechanics in complex systems

This document should be considered in its separation; there are three distinct topics contained within and three distinct chapters within the body of works. In a similar fashion, this abstract should be considered in three parts. Firstly, we explored the existence of multiply-charged atomic ions by having developed a new set of dimensional scaling equations as well as a series of relativistic augmentations to the standard dimensional scaling procedure and to the self-consistent field calculations. Secondly, we propose a novel method of predicting drug efficacy in hopes to facilitate the discovery of new small molecule therapeutics by modeling the agonist-protein system as being similar to the process of Inelastic Electron Tunneling Spectroscopy. Finally, we facilitate the instruction in basic quantum mechanical topics through the use of quantum games; this method of approach allows for the generation of exercises with the intent of conveying the fundamental concepts within a first year quantum mechanics classroom. Furthermore, no to be mentioned within the body of the text, yet presented in appendix form, certain works modeling the proliferation of cells types within the confines of man-made lattices for the purpose of facilitating artificial vascular transplants. ^ In Chapter 2, we present a theoretical framework which describes multiply-charged atomic ions, their stability within super-intense laser fields, also lay corrections to the systems due to relativistic effects. Dimensional scaling calculations with relativistic corrections for systems: H, H-, H 2-, He, He-, He2-, He3- within super-intense laser fields were completed. Also completed were three-dimensional self consistent field calculations to verify the dimensionally scaled quantities. With the aforementioned methods the system\u27s ability to stably bind \u27additional\u27 electrons through the development of multiple isolated regions of high potential energy leading to nodes of high electron density is shown. These nodes are spaced far enough from each other to minimized the electronic repulsion of the electrons, while still providing adequate enough attraction so as to bind the excess elections into orbitals. We have found that even with relativistic considerations these species are stably bound within the field. It was also found that performing the dimensional scaling calculations for systems within the confines of laser fields to be a much simpler and more cost-effective method than the supporting D=3 SCF method. The dimensional scaling method is general and can be extended to include relativistic corrections to describe the stability of simple molecular systems in super-intense laser fields.^ Chapter 3, we delineate the model, and aspects therein, of inelastic electron tunneling and map this model to the protein environment. G protein-coupled receptors (GPCRs) constitute a large family of receptors that sense molecules outside of a cell and activate signal transduction pathways inside the cell. Modeling how an agonist activates such a receptor is important for understanding a wide variety of physiological processes and it is of tremendous value for pharmacology and drug design. Inelastic electron tunneling spectroscopy (IETS) has been proposed as the mechanism by which olfactory GPCRs are activated by an encapsulated agonist. In this note we apply this notion to GPCRs within the mammalian nervous system using ab initio quantum chemical modeling. We found that non-endogenous agonists of the serotonin receptor share a singular IET spectral aspect both amongst each other and with the serotonin molecule: a peak that scales in intensity with the known agonist activities. We propose an experiential validation of this model by utilizing lysergic acid dimethylamide (DAM-57), an ergot derivative, and its isotopologues in which hydrogen atoms are replaced by deuterium. If validated our theory may provide new avenues for guided drug design and better in silico prediction of efficacies. ^ Our final chapter, explores methods which may be explored to assist in the early instruction in quantum mechanics. The learning of quantum mechanics is contingent upon an understanding of the physical significance of the mathematics that one must perform. Concepts such as normalization, superposition, interference, probability amplitude and entanglement can prove challenging for the beginning student. This paper outlines several class exercises that use a non-classical version of tic-tac-toe to instruct several topics in an undergraduate quantum mechanics course. Quantum tic-tac-toe (QTTT) is a quantum analogue of classical tic-tac-toe (CTTT) benefiting from the use of superposition in movement, qualitative (and later quantitative) displays of entanglement and state collapse due to observation. QTTT can be used for the benefit of the students understanding in several other topics with the aid of proper discussion

Robotics-Inspired Methods for the Simulation of Conformational Changes in Proteins

Cette thèse présente une approche de modélisation inspirée par la robotique pour l'étude des changements conformationnels des protéines. Cette approche est basée sur une représentation mécanistique des protéines permettant l'application de méthodes efficaces provenant du domaine de la robotique. Elle fournit également une méthode appropriée pour le traitement gros-grains des protéines sans perte de détail au niveau atomique. L'approche présentée dans cette thèse est appliquée à deux types de problèmes de simulation moléculaire. Dans le premier, cette approche est utilisée pour améliorer l'échantillonnage de l'espace conformationnel des protéines. Plus précisément, cette approche de modélisation est utilisée pour implémenter des classes de mouvements pour l'échantillonnage, aussi bien connues que nouvelles, ainsi qu'une stratégie d'échantillonnage mixte, dans le contexte de la méthode de Monte Carlo. Les résultats des simulations effectuées sur des protéines ayant des topologies différentes montrent que cette stratégie améliore l'échantillonnage, sans toutefois nécessiter de ressources de calcul supplémentaires. Dans le deuxième type de problèmes abordés ici, l'approche de modélisation mécanistique est utilisée pour implémenter une méthode inspirée par la robotique et appliquée à la simulation de mouvements de grande amplitude dans les protéines. Cette méthode est basée sur la combinaison de l'algorithme RRT (Rapidly-exploring Random Tree) avec l'analyse en modes normaux, qui permet une exploration efficace des espaces de dimension élevée tels les espaces conformationnels des protéines. Les résultats de simulations effectuées sur un ensemble de protéines montrent l'efficacité de la méthode proposée pour l'étude des transitions conformationnellesProteins are biological macromolecules that play essential roles in living organisms. Un- derstanding the relationship between protein structure, dynamics and function is indis- pensable for advances in fields such as biology, pharmacology and biotechnology. Study- ing this relationship requires a combination of experimental and computational methods, whose development is the object of very active interdisciplinary research. In such a context, this thesis presents a robotics-inspired modeling approach for studying confor- mational changes in proteins. This approach is based on a mechanistic representation of proteins that enables the application of efficient methods originating from the field of robotics. It also provides an accurate method for coarse-grained treatment of proteins without loosing full-atom details.The presented approach is applied in this thesis to two different molecular simulation problems. First, the approach is used to enhance sampling of the conformational space of proteins using the Monte Carlo method. The modeling approach is used to implement new and known Monte Carlo trial move classes as well as a mixed sampling strategy. Results of simulations performed on proteins with different topologies show that this strategy enhances sampling without demanding higher computational resources. In the second problem tackled in this thesis, the mechanistic modeling approach is used to implement a robotics-inspired method for simulating large amplitude motions in proteins. This method is based on the combination of the Rapidly-exploring Random Tree (RRT) algorithm with Normal Mode Analysis (NMA), which allows efficient exploration of the high dimensional conformational spaces of proteins. Results of simulations performed on ten different proteins of different sizes and topologies show the effectiveness of the proposed method for studying conformational transitionsTOULOUSE-INSA-Bib. electronique (315559905) / SudocSudocFranceF