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Molecular dynamics (MD) simulation is a powerful tool enabling researchers to gain insight into biological processes at the atomic level. There have been many advancements in both hardware and software in the last decade to both accelerate MD simulations and increase their predictive accuracy; however, MD simulations are typically limited to the microsecond timescale, whereas biological motions can take seconds or longer. Because of this, it remains extremely challenging to restrain simulations using ensemble-averaged experimental observables. Among various approaches to elucidate the kinetics of molecular simulations, Markov State Models (MSMs) have proven their ability to extract both kinetic and thermodynamic properties of long-timescale motions using ensembles of shorter MD simulation trajectories. In this dissertation, we have implemented an MSM path-entropy method, based on the idea of maximum-caliber, to efficiently predict the changes in protein folding behavior upon mutation. Next, we explore the accuracy of different MSM estimators applied to trajectory data obtained by adaptive seeding, in which new rounds of short MD simulations are collected from states of interest, and propose a simple method to build accurate models by population re-weighting of the transition count matrix. Finally, we explore ways to reconcile simulated ensembles with Hydrogen/Deuterium exchange (HDX) protection measurements, by constructing multi-ensemble Markov State Models (MEMMs) from biased MD simulations, and reconciling these predictions against the experimental data using the BICePs (Bayesian Inference of Conformational Populations) algorithm. We apply this approach to model the native-state conformational ensemble of apomyoglobin at neutral pH. / Chemistry

Computational studies have become an essential tool in biochemistry, providing detailed insight into biological systems alongside experimental studies. Molecular simulation can predict protein conformational dynamics and the impact of mutations, enabling rapid and low-cost investigation of potential therapeutic targets and better understanding of biological systems. Molecular dynamics (MD) is a computational method able to model ensembles of biomolecular conformations in solution by simulating atomic motion at high temporal resolution. The principle limitation of MD is the ability to collect sufficient data for equilibrium sampling. However, with the progression of high-performance computing (HPC) clusters and distributed computing platforms, timescales previously inaccessible to MD can be reached and relevant protein parameters can be extracted using modeling. From these simulations, Markov state models (MSMs) are used extract system-relevant kinetic and thermodynamic information. An MSM represents a series of memoryless, probabilistic transitions between discrete states in a kinetically meaningful way. The obtained information is used to understand the relationships between relevant protein conformations, thus enabling a comprehensive understanding of the modelled system in a human-readable format. Recent advancements in model scoring and hyper-parameterization moved MSM construction away from anecdotal, case-by-case basis to a highly systematic approach that focuses on optimization and validity. Thus, modern MSMs are employed to investigate protein properties, and predict experimental observables using system-representative ensembles of conformations. Additionally, a comprehensive MSM can be combined with sparse experimental data to generate an improved interpretation of the system. My work focuses on performing all-atom massively-parallel MD simulation using the Folding@home distributed computing platform in order to build comprehensive MSMs that are used in improving simulation accuracy and protein design. This work results in the development of an unbiased framework for MSM building that is used to lend insight into simulation parameters, extract novel system behavior and enable clear comprehension of a target function, such as impact of mutations or emphasis of rare events. / Chemistry

In recent years, advancements in computational methodologies have shed light on the complex process that makes proteins fold into their three-dimensional shapes. These new tools have helped us understand the steps proteins take to achieve these structures, revealing the presence of metastable intermediates along the folding pathways. This newfound understanding has led to the development of a novel drug discovery strategy known as Pharmacological Protein Inactivation by Folding Intermediate Targeting (PPI-FIT). This approach specifically targets folding intermediates to modulate protein expression levels, thus opening new opportunities for pharmacological intervention. This approach could be particularly relevant for diseases linked to targets that were previously considered "undruggable." A promising outcome of the PPI-FIT strategy is the identification of SM875, a compound that has been shown to lower prion protein (PrP) levels, positioning it as a potential therapeutic candidate for prion diseases. This study describes the initial phase of optimization of the SM875 scaffold. It encompasses the chemical diversification of SM875, followed by systematic evaluations of its biological activity and toxicity, with the aim of establishing structure-activity relationships (SAR). This knowledge is instrumental in guiding the synthesis of analogs with enhanced properties, advancing them through the development pipeline toward clinical application. Furthermore, this work investigates the potential regulatory function of folding intermediates in physiological processes, hypothesizing that they may serve as substrates for post translational modifications (PTMs). This hypothesis proposes an expansion of the current paradigm, suggesting that folding intermediates could constitute an additional layer of regulation within the complex network of proteostasis.

No / This chapter gives an introduction to protein simulation methodology aimed at experimentalists and graduate students new to in silico investigations. More emphasis is placed on the knowledge needed to select appropriate simulation protocols, leaving theoretical and mathematical depth for other texts to take care of. The chapter explains some of the more practical considerations of performing simulations of proteins, in particular, the additional considerations required when studying protein folding where nonnative environments are modeled. Forced unfolding simulations are highly relevant and invaluable in characterizing proteins naturally exposed to mechanical stress as a component of their biological function. The chapter illustrates this utility by discussing research that has been done primarily on the giant muscle protein titin. Using Molecular dynamics (MD) simulations to investigate protein folding faces two main challenges. The most obvious relates to the timescale of protein folding and the computational expense required for adequate sampling. / NIH

O entendimento dos princípios básicos do enovelamento protéico pode conduzir a muitas aplicações importantes. Embora não se conheçam todos os aspectos significativos envolvidos neste problema, experimentos e aproximações teóricas têm produzido avanços relevantes na sua compreensão. Um fato experimental importante tem sido a descoberta de que o logaritmo da taxa de enovelamento log kf se correlaciona linearmente com parâmetros estruturais globais, como a ordem de contato relativa c. Com o propósito de contribuir para o entendimento do processo de enovelamento, o objetivo primordial deste trabalho consiste em explicar o porquê de certas proteínas não seguirem o comportamento linear entre log kf e c, verificado para outras proteínas da mesma classe (usualmente proteínas pequenas e com termodinâmica descrita pela aproximação de dois estados). Para isso foi necessário identificar os parâmetros topológicos da estrutura nativa que constituíssem importantes determinantes da cinética do enovelamento de proteínas globulares. Também se estudou como as especificidades estéricas dos aminoácidos afetam o processo do enovelamento de proteínas, assim como influenciam na correlação entre a ordem de contato relativo e a taxa de enovelamento. Empregou-se neste estudo um modelo simplificado em rede cúbica, que foi tratado por meio de simulações Monte Carlo. Um conjunto de 52 estruturas maximamente compactas, correspondendo a cadeias de tamanho L = 27 monômeros, foi usado para representar estados nativos; estas estruturas foram escolhidas de forma a representar uma variedade significativa de padrões estruturais, independentemente de c. Através de uma análise detalhada da influência de parâmetros topológicos das configurações nativas na cinética do enovelamento, conclui-se que a taxa de enovelamento é fortemente dependente daquilo que denominamos aqui como “conteúdo de estruturas tipo-secundárias" da estrutura nativa. Adicionalmente, observou-se que aquela (taxa), independentemente do valor da ordem de contato relativo, é fortemente influenciada pelos padrões confíguracionais e suas combinações presentes na nativa. Por meio dessa premissa, foi então possível explicar de forma consistente os casos que não obedecem a pretensa relação linear entre log kf e c, levando a concluir que o logaritmo da taxa de enovelamento e a ordem de contato relativo são linearmente dependentes somente para aquelas configurações em que há uma certa quantidade equilibrada (que depende de c) de padrões estruturais, mesclando contatos efetivos de curto alcance (alto conteúdo de estruturas tipo-secundárias), com outros de longo alcance (baixo conteúdo de estruturas tipo-secundárias). Estruturas nativas que quebram este equilíbrio têm sua cinética de enovelamento afetada com respeito à reta de regressão linear ajustada para o conjunto de todas as configurações consideradas. Dessa forma, verificou-se que o mecanismo físico básico que relaciona o conteúdo de estruturas tipo-secundárias e a taxa de enovelamento, envolve o conceito de cooperatividade: se a estrutura nativa é rica em combinações de padrões estruturais ricos em contatos efetivos de curto alcance, o processo de enovelamento é mais rápido porque contatos locais são naturalmente estimulados por flutuações térmicas. / The understanding of basic principles of the protein folding problem can lead to many important applications. Although not all the involved significant aspects of this problem are known, experiments and theoretical approaches have produced important advances in its understanding. An important experimental fact has been the discovery that the logarithm of the folding rate log kf correlates linearly with global structural parameters, like the relative contact order c. In order to contribute for the understanding of folding process, the primordial goal of this work consists in to explain why certain proteins do not follow the linear behavior between log kf and c, as verified to other proteins from the same class (usually small two states proteins). For this, it was necessary to identify those topological parameters of the native structure that are important to the folding kinetic of globular protein. It was also studied how steric specificities of the aminoacids affect the protein folding process, as well how they influence the correlation between the relative contact order and the folding rate. It was employed in this study a simplified cubic lattice model, treated by Monte Carlo simulation. A set of 52 maximum compact structures, corresponding to chains of size L = 27 monomers, was used to represent the native states; these structures were chosen in such a way to represent a significant diversity of structural patterns, independently of c. Through a detailed analysis of the influence of topological parameters of the native configurations on the folding kinetic, it was concluded that the folding rate is strongly dependent of what we call here as “content of type-secondary" of the native. Additionally, it was observed that log kf is, independently of c, strongly influenced by the configurational patterns and its combinations in the native. Through this premise it was possible to consistently explain the cases that do not obey the pretense linear relation between log kf and c, leading to conclude that the logarithm of the folding rate and the relative contact order are linearly related only for those configurations in that there is a certain balanced amount of structural patterns (which depend on c) mixing short-range effective contacts (high contents of secondary-type structures) and long-range contacts (low contents of secondary-type structures). Structures that break this balance have its folding kinetic affected with respect to the linear fitting adjusted for the set of all the considered configurations. Of this form, it was verified that basic physical mechanism that relates the content of type-secondary structures and the folding rate involves the cooperativety concept: if the native structure presents combinations of structural standards rich in effective contacts of short-range, the folding process is faster because local contacts are naturally stimulated by thermal fluctuations.

Les protéines sont des objets biologiques conçus pour résister aux perturbations et, àen même temps, s'adapter à des nouveaux environnements et des nouveaux besoins. Que sont lespropriétés structurelles des protéines permettant une telle plasticité? Pour taclercette question, nous modélisons d'abord la structure des protéines comme un réseau d'acides aminés et atomes en interaction. Compte tenu de la conformation structurelle 3Dd'une mutation obtenue In Silico, une approche réseaupermet la quantification de son changement structurel. En utilisant des grands ensemblesde mutations, nous avons conclu que le changement structurel est indépendant du type d'acide aminé remplacé ou du remplacement après mutation. En regardantà la composition des voisinages d'acides aminés, nous avons remarqué que lela localisation d'un type d'acide aminé dans la structure 3D est arbitraire:ce qui signifie que les contraintes d'interactions d'acides aminés dans une protéinemontre être indépendantes de la position de l'acide aminé en question. Menant à laobservation que la position de l'acide aminé dans la séquence est lapropriété unique modulant la plasticité structurelle.Le fait que les acides aminés peuvent se remplacer les uns les autres danstoutes les positions parce que la contrainte d'interaction ne dépend pas dutype d'acide aminé,est basé sur la personnalisation des voisins viamutations altérnatives compensatoires. Même s'il y a une grandetolérance pour les mutations basée sur la robustesse structurelle, les mutations peuvent avoir un impact surla plasticité structurelle en raison de la modification de la force des interactions êntre acides aminéset la distribution des atomes et des voisins entourant les résidus.La conséquence directe d'une telle variabilité de l'emballage atomique,est dû à une différence de vide (espace vide,pas d'atomes) sur la surface des résidus identifiés par certaines de mes données / résultats.Cela soulève la possibilité que la plasticité structurelle n'est pas seulementrégulée par les acides aminés et les contacts atomiques, mais aussi en sculptantdes vides locales dans la structure de la protéine pour permettre des mouvements atomiquesnécessaires pour la fonction de la protéine. Enfin, pour tester cette hypothèse, nous avonsmis en œuvre trois algorithmes pour mesurer l'espace vide autour desacides aminés pour regarder la relation entre cet espace vide et la plasticité structurelle. / Proteins are biological objects made to resist perturbations and, atthe same time, adapt to new environments and new needs. What are thestructural properties of proteins allowing such plasticity? To tacklethis question we first model protein structure as a network of aminoacids and atoms in interaction. Given the 3D structural conformationof a mutation obtained In Silico, a network approachallows the quantification of its structural change. Using large setsof mutations, we concluded that structural change is independent fromthe type of amino acid replaced, or replacing after mutation. Lookingat the composition of amino acid neighborhoods, we noticed that thelocation of a type of amino acid in the 3D structure is arbitrary:meaning that constraints of amino acid interactions in a proteinshow to be position independent. Leading to theobservation that the position of the amino acid in the sequence is thesingle property modulating structural plasticity.The fact that amino acids can replace each other atany position because the interaction constraint is not dependent on thetype of amino acid,is based on the customization of neighbors via alternative amino acidmutations or compensatory mutations. Even if there is a large mutationtolerance based on structural robustness, mutations can have an impact onthe structural plasticity because of the change in strength of pairwsie interactionsand the distribution of atoms and neihgbors surrounding residues.The direct consequence of such a variable atomic packingdistribution, is a difference of void (empty space,no atoms) on the surface of residues as identified by some of my data/results.This raises the possibility that structural plasticity is not onlyregulated by amino acid and atomic contacts but also by carving localvoids within the protein structure to allow atomic motionsrequired for the function of the protein. Finally, to test this hypothesis, we haveimplemented three algorithms to measure the empty space around aminoacids to look at the relation between this empty space and structural plasticity.