Characterization of the influence of external stimulus on protein-nucleic acid complex through multiscale computations

The concomitant detection, monitoring and analysis of biomolecules have assumed utmost importance in the field of medical diagnostics as well as in different spheres of biotechnology research such as drug development, environmental hazard detection and biodefense. There is an increased demand for the modulation of the biological response for such detection / sensing schemes which will be facilitated by the sensitive and controllable transmission of external stimuli. Electrostatic actuation for the controlled release/capture of biomolecules through conformational transformations of bioreceptors provides an efficient and feasible mechanism to modulate biological response. In addition, electrostatic actuation mechanism has the advantage of allowing massively parallel schemes and measurement capabilities that could ultimately be essential for biomedical applications.Experiments have previously demonstrated the unbinding of thrombin from its aptamer in presence of small positive electrode potential whereas the complex remained associated in presence of small negative potentials / zero potential. However, the nanoscale physics/chemistry involved in this process is not clearly understood. In this thesis a combination of continuum mechanics based modeling and a variety of atomistic simulation techniques have been utilized to corroborate the aforementioned experimental observations. It is found that the computational approach can satisfactorily predict the dynamics of the electrically excited aptamer-thrombin complex as well as provide an analytical model to characterize the forced binding of the complex

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.Peer ReviewedPostprint (author's final draft

Molecular modeling of drug delivery systems based on carbon nanostructures: structure, function, and potential applications for anticancer complexes of Pt(II)

The medication with Pt(II) drugs (cisplatin, carboplatin, and oxaliplatin) has been an effective alternative for treating cancers due to their notable inhibition of cancer cells growth and the prevention of metastasis. Nevertheless, the low selectivity of these metallodrugs for malignant cells produces severe side effects, which limit this chemotherapy. In this context, carbon nanohorns (CNHs) have been considered potential nanovectors for drugs, since they present low toxicity, drug-loading capacity, biodegradation routes, and biocompatibility when oxidized. However, there is still a lack of studies regarding the molecular behavior of these nanocarriers on cell membranes. The present work aims to characterize the interactions between inclusion complexes drug@CNH, which are formed by platinum drugs encapsulated in CNHs, and plasma membranes by using molecular dynamics simulations. The results demonstrated that the van der Waals contribution played a primary role (∼74%) for the complex stability, which explain the confined dynamics of drugs inside the CNHs. The free energy profiles revealed an endergonic character of the drug release processes from CNHs, in which the energy barrier for oxaliplatin release (~24 kcal mol–1 ) was ~30% larger than those for carboplatin and cisplatin (~18 kcal mol-1 ). The simulations also showed four stages of the interaction mechanism CNH--membrane: approach, insertion, permeation, and internalization. Despite the low structural disturbance of the membranes, the free energy barrier of ∼55 kcal mol-1 for the CNHs translocation indicated that this transport is kinetically unfavorable by passive process. The in silico experiments evidenced that the most likely mechanism of cisplatin delivery from CNHs involve the approach and insertion stages, where the nanovector adheres on the surface of cancer cells, as reported in in vitro studies. After this retention, the drug load may be slowly released in the tumor site. Finally, simulations of the cellular uptake of Pt(II) drugs also pointed out significant energy barriers (~30 kcal mol-1 ) for this process, which reflects their low permeability in membranes as discussed in experimental studies. In addition to reinforcing the potential of CNH as nanovector of Pt(II) drugs, the results presented in this thesis may assist and drive new experimental studies with CNHs, focusing on the development of less aggressive formulations for cancer treatments.A medicação com fármacos a base de Pt(II) (cisplatina, carboplatina e oxaliplatina) tem sido uma alternativa efetiva para tratar cânceres devido à sua notável inibição do crescimento de células cancerosas e a prevenção de metástases. No entanto, a baixa seletividade dessas metalodrogas por células cancerosas gera severos efeitos colaterais. Nesse contexto, nanohorns de carbono (CNHs) têm sido considerados potenciais nanovetores de fármacos, devido a baixa toxicidade, capacidade de carreamento de fármacos, rotas de biodegradação, e biocompatibilidade quando oxidados. Porém, existe uma carência de estudos tratando o comportamento desses nanocarreadores em biomembranas. Esse trabalho tem como objetivo caracterizar as interações entre complexos de inclusão fármaco@CNH, formados por fármacos de Pt(II) encapsulados em CNHs, e membranas usando simulações por dinâmica molecular. Os resultados demonstraram que a contribuição de van der Waals teve um papel primário (∼74%) na estabilidade dos complexos, o que explica a dinâmica confinada dos fármacos dentro dos CNHs. Os perfis de energia livre revelaram o caráter endergônico da liberação dos fármacos a partir de CNHs, nos quais a barreira de energia para a liberação da oxaliplatina (~24 kcal mol– 1 ) é ~30% maior do que aquelas para carboplatina e cisplatina. As simulações mostraram quatro estágios do mecanismo de interação CNH-membrana: aproximação, inserção, permeação e internalização. Apesar do baixo distúrbio estrutural das membranas, a barreira de energia livre de ∼55 kcal mol-1 para a translocação de CNHs indicou que esse transporte é desfavorável cineticamente via o processo passivo. Os experimentos in silico evidenciam que o mecanismo mais provável de entrega de cisplatina a partir de CNHs envolve a aproximação e inserção, onde o nanovetor adere na superfície de células cancerosas, como reportado em estudos in vitro. Após essa retenção, a carga de fármaco deve ser ligeiramente liberada no tumor. As simulações de captação celular de fármacos de Pt(II) também apontaram barreiras de energia significativas (∼30 kcal mol-1 ) para esse processo, o que reflete a baixa permeabilidade deles em membranas como discutido em estudos experimentais. Além de reforçar o potencial de CNHs como nanovetores de fármacos de Pt(II), os resultados apresentados nessa tese podem auxiliar e impulsionar novos estudos com CNHs, focando no desenvolvimento de formulações menos agressivas para tratamentos de câncer.FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerai

Molecular Dynamics Simulation

Condensed matter systems, ranging from simple fluids and solids to complex multicomponent materials and even biological matter, are governed by well understood laws of physics, within the formal theoretical framework of quantum theory and statistical mechanics. On the relevant scales of length and time, the appropriate ‘first-principles’ description needs only the Schroedinger equation together with Gibbs averaging over the relevant statistical ensemble. However, this program cannot be carried out straightforwardly—dealing with electron correlations is still a challenge for the methods of quantum chemistry. Similarly, standard statistical mechanics makes precise explicit statements only on the properties of systems for which the many-body problem can be effectively reduced to one of independent particles or quasi-particles. [...

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Biomolecular simulation is increasingly central to understanding and designing biological molecules and their interactions. Detailed, physics‐based simulation methods are demonstrating rapidly growing impact in areas as diverse as biocatalysis, drug delivery, biomaterials, biotechnology, and drug design. Simulations offer the potential of uniquely detailed, atomic‐level insight into mechanisms, dynamics, and processes, as well as increasingly accurate predictions of molecular properties. Simulations can now be used as computational assays of biological activity, for example, in predictions of drug resistance. Methodological and algorithmic developments, combined with advances in computational hardware, are transforming the scope and range of calculations. Different types of methods are required for different types of problem. Accurate methods and extensive simulations promise quantitative comparison with experiments across biochemistry. Atomistic simulations can now access experimentally relevant timescales for large systems, leading to a fertile interplay of experiment and theory and offering unprecedented opportunities for validating and developing models. Coarse‐grained methods allow studies on larger length‐ and timescales, and theoretical developments are bringing electronic structure calculations into new regimes. Multiscale methods are another key focus for development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Method

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Mapping biophysics through enhanced Monte Carlo techniques

This thesis is focused on the study of molecular interactions at the atomistic detail and is divided into one introductory chapter and four chapters referencing different problems and methodological approaches. All of them are focused on the development and improvement of computational Monte Carlo algorithms to study, in an efficient manner, the behavior of these systems at a classical molecular mechanics level. The four biophysical problems studied in this thesis are: induced fit docking between protein-ligand and between DNA-ligand to understand the binding mechanism, protein stretching response, and generation/ scoring of protein-protein docking poses. The thesis is organized as follows: First chapter corresponds to the state of the art in computational methods to study biophysical interactions, which is the starting point of this thesis. Our in-house PELE algorithm and the main standard methods such as molecular dynamics will be explained in detail. Chapter two is focused on the main PELE modifications to add new features, such as the addition of a new force field, implicit solvent and an anisotropic network specific for DNA simulation studies. We study, compare and validate the conformations generated by six representative DNA fragments with the new PELE features using molecular dynamics as a reference. Chapter three is devoted to applying the new methods implemented and tested in PELE to study protein-ligand interactions and DNA-ligand interactions using four systems. First, we study the porphyrin binding to Gun4 protein combining PELE and molecular dynamics simulations. Besides, we provide a docking pose that has been corroborated by a new crystal structure published during the revision process of the submitted study showing the accuracy of our predictions. In the second project, we use our improved version of PELE to generate the first structural model of an alpha glucose 1,6-bisphosphate substrate bound to the human Phosphomannomutase 2 demonstrating that this ligand can adopt two low-energy orientations. The third project is the study of DNA-ligand interactions for three cisplatin drugs where we evaluate the binding free energy using Markov state models. We show excellent results respect another free energy methods studied with molecular dynamics. The last project is the study of the daunomycin DNA intercalator where we simulate and study the binding process with PELE. Chapter four is focused on the computational study of force extension profiles during the protein unfolding. We added a dynamic harmonic constraint following a similar procedure applied in steered molecular dynamics to our Monte Carlo approach to fix or pull some selected atoms forcing the protein unfolding in a defined direction. We implement and compare with steered molecular dynamics this technique with Ubiquitin and Azurin proteins. Moreover, we add this feature to a well-known algorithm called MCPRO from William Jorgensen¿s group at YALE University to evaluate the free energy associated to the unfolding of the deca-alanine system. Chapter five corresponds to the introduction of a multiscale approach to study protein-protein docking. A coarse-grained model will be combined with a Monte Carlo exploration reducing the degrees of freedom to generate thousands of protein-protein poses in a quick way. Poses produced by this procedure will be refined and ranked through a protonation, hydrogen bond optimization, and minimization protocol at the all-atom representation to identify the best poses. I present two test cases where this procedure has been applied showing a good accuracy in the predictions: tryptogalinin and ferredoxin/flavodoxin systems.Aquesta tesi es centra en l'estudi de les interaccions moleculars amb detall atomic i es divideix en un capítol d'introducció i quatre capítols que fan referència a diferents problemes i enfocaments metodològics. Tots ells se centren en el desenvolupament i millora dels algoritmes computacionals de Monte Carlo per estudiar, de manera eficient, el comportament d'aquests sistemes a un nivell mecànica molecular clàssica. Els quatre problemes biofísics estudiats en aquesta tesi són: acoblament induït entre la proteïna-lligand i entre DNA-lligant per comprendre el mecanisme d'unió, resposta de les proteïnes a l'estirament, i la generació/puntuació d'acoblament entre poses proteïna-proteïna. La tesi s'organitza de la següent manera: El primer capítol correspon a l'estat de l'art en mètodes computacionals per estudiar les interaccions biofísiques, que és el punt de partida d'aquesta tesi. El nostre PELE algoritme i els principals mètodes estàndard com ara la dinàmica molecular s'explicaran en detall. El capítol dos es centra en les principals modificacions PELE per afegir noves característiques, com ara l'addició d'un nou camp de força, solvent implícit i modes normals per aquests estudis de simulació d'ADN. Es fa un estudi, comparació i validació de les conformacions generades per sis fragments d'ADN representatius amb PELE utilitzant dinàmica molecular com a referència. El tercer capítol està dedicat a l'aplicació dels nous mètodes implementats i provats en PELE per estudiar les interaccions proteïna-lligand i la interacció lligand-DNA utilitzant quatre sistemes. En primer lloc, se estudia la unió a proteïnes GUN4 combinant PELE i simulacions de dinàmica molecular. A més, es proposa un acoblament que ha sigut corroborat per una nova estructura cristal·lina publicada durant el procés de revisió de l'estudi mostrant l'exactitud de les nostres prediccions. En el segon projecte, hem utilitzat la nostra versió millorada de PELE per generar el primer model estructural d'una glucosa alfa substrat 1,6-bisfosfat unit a la fosfomanomutasa humana 2, que demostra que aquest lligant pot adoptar dues orientacions de baiza energia. El tercer projecte és l'estudi de les interaccions d'ADN lligant per tres medicaments cisplatí on se avalua l'energia lliure d'unió utilitzant Markov States Models. Es mostren excel·lents resultats respecte d'altres mètodes d'energia lliure estudiats amb dinàmica molecular. L'últim projecte és l'estudi de l'intercalador d'ADN anomenat daunomicina on es simula i estudia el procés d'unió amb PELE. El capítol 4 es centra en l'estudi computacional dels perfils d'extensió de la força durant el desplegament de la proteïna. Hem afegit una restricció harmònica dinàmica seguint un procediment similar al aplicat en dinàmica molecular en el nostre algoritme Monte Carlo per fixar o moure alguns àtoms seleccionats obligant a desplegar la proteïna en una direcció definida. Aquesta tècnica s'ha implementat i comparat amb dinàmica molecular per les proteïnes ubiquitina i azurin. D'altra banda, hem afegit aquesta modificació a un algoritme ben conegut anomenat MCPRO del grup de William Jorgensen a la Universitat de Yale per avaluar l'energia lliure associada al desplegament del sistema deca alanina. El capítol cinc correspon a la introducció d'un enfocament multiescala per estudiar l'acoblament proteïna-proteïna. Un model de gra gruixut es combinat amb una exploració Monte Carlo per reduir els graus de llibertat i generar milers de poses proteïna-proteïna d'una manera ràpida. Les poses produides per aquest procediment es perfeccionan i evaluan a través d'una protonació, optimització d'enllaços d'hidrogen, i minimització a escala atòmica per identificar les millors poses. Es presenten dos casos de prova on s'ha aplicat aquest procediment que mostra una bona precisió en les prediccions: tryptogalinin i ferredoxina / flavodoxina systems