Studying protein-ligand interactions using a Monte Carlo procedure

[eng] Biomolecular simulations have been widely used in the study of protein-ligand interactions; comprehending the mechanisms involved in the prediction of binding affinities would have a significant repercussion in the pharmaceutical industry. Notwithstanding the intrinsic difficulty of sampling the phase space, hardware and methodological developments make computer simulations a promising candidate in the resolution of biophysically relevant problems. In this context, the objective of the thesis is the development of a protocol that permits studying protein-ligand interactions, in view to be applied in drug discovery pipelines. The author contributed to the rewriting PELE, our Monte Carlo sampling procedure, using good practices of software development. These involved testing, improving the readability, modularity, encapsulation, maintenance and version control, just to name a few. Importantly, the recoding resulted in a competitive cutting-edge software that is able to integrate new algorithms and platforms, such as new force fields or a graphical user interface, while being reliable and efficient. The rest of the thesis is built upon this development. At this point, we established a protocol of unbiased all-atom simulations using PELE, often combined with Markov (state) Models (MSM) to characterize the energy landscape exploration. In the thesis, we have shown that PELE is a suitable tool to map complex mechanisms in an accurate and efficient manner. For example, we successfully conducted studies of ligand migration in prolyl oligopeptidases and nuclear hormone receptors (NHRs). Using PELE, we could map the ligand migration and binding pathway in such complex systems in less than 48 hours. On the other hand, with this technique we often run batches of 100s of simulations to reduce the wall-clock time. MSM is a useful technique to join these independent simulations in a unique statistical model, as individual trajectories only need to characterize the energy landscape locally, and the global characterization can be extracted from the model. We successfully applied the combination of these two methodologies to quantify binding mechanisms and estimate the binding free energy in systems involving NHRs and tyorsinases. However, this technique represents a significant computational effort. To reduce the computational load, we developed a new methodology to overcome the sampling limitations caused by the ruggedness of the energy landscape. In particular, we used a procedure of iterative simulations with adaptive spawning points based on reinforcement learning ideas. This permits sampling binding mechanisms at a fraction of the cost, and represents a speedup of an order of magnitude in complex systems. Importantly, we show in a proof-of-concept that it can be used to estimate absolute binding free energies. Overall, we hope that the methodologies presented herein help streamline the drug design process.[spa] Las simulaciones biomoleculares se han usado ampliamente en el estudio de interacciones proteína-ligando. Comprender los mecanismos involucrados en la predicción de afinidades de unión tiene una gran repercusión en la industria farmacéutica. A pesar de las dificultades intrínsecas en el muestreo del espacio de fases, mejoras de hardware y metodológicas hacen de las simulaciones por ordenador un candidato prometedor en la resolución de problemas biofísicos con alta relevancia. En este contexto, el objetivo de la tesis es el desarrollo de un protocolo que introduce un estudio más eficiente de las interacciones proteína-ligando, con vistas a diseminar PELE, un procedimiento de muestreo de Monte Carlo, en el diseño de fármacos. Nuestro principal foco ha sido sobrepasar las limitaciones de muestreo causadas por la rugosidad del paisaje de energías, aplicando nuestro protocolo para hacer analsis detallados a nivel atomístico en receptores nucleares de hormonas, receptores acoplados a proteínas G, tirosinasas y prolil oligopeptidasas, en colaboración con una compañía farmacéutica y de varios laboratorios experimentales. Con todo ello, esperamos que las metodologías presentadas en esta tesis ayuden a mejorar el diseño de fármacos

Computational Studies of the Energetics and Dynamics of Protein-Protein Binding

Protein-protein binding is crucial to various processes in living organisms including signal transduction and cell regulation and also plays a central role in various diseases. Therefore, detailed understanding of protein binding is of great importance and is an active area of research in many fields including chemistry, molecular biology and biophysics. In this dissertation, a series of five computational studies were completed to provide molecular details of the energetics and dynamics of model protein-protein complexes. The first three studies focus on the role of solvent in protein-protein binding. The presence of solvent is very important to the formation of protein-protein complexes through both favorable and unfavorable contributions. For example, the extent to which that salt bridges contribute to the binding stability is predominantly determined by their desolvation penalties, which is difficult to examine experimentally but has been previously studied using implicit solvent models. Here, extensive implicit and explicit solvent simulations were carried out to directly compare the two solvent models in estimating the desolvation penalties of salt bridges upon protein binding. In addition, the effects of high temperature and salt concentration on the desolvation penalties were also explored. In the fourth study, molecular simulations were employed to model rearrangements of an intermolecular beta sheet in a protein-peptide complex, providing insight into how nature might correct for mistakes in binding orientation for protein-protein interactions involving the formation of beta sheets. The rearrangement mechanism includes a hydrophobic residue of the peptide anchoring itself to a transient hydrophobic pocket on the protein and helping the peptide to “crawl” back to its native state. Finally, in the fifth study, the relative stabilities of the dimeric and newly discovered trimeric states for a model coiled-coil protein, the GCN4 leucine zipper were compared in isolation. Parallel tempering molecular dynamic simulations in implicit solvent, performed on the microsecond timescale, revealed that while the dimer fold is more stable at room temperature, both oligomers have similar stabilities at temperatures well below the melting temperatures and therefore the same sequence can populate both folds depending on the environment

Interakční preference v komplexech protein - DNA.

Interakční preference v komplexes protein - DNA Dávid Jakubec Abstrakt Interakce proteinů s DNA jsou základem mnoha esenciálních biologických pochodů. Navzdory dosavadním snahám se zatím nepodařilo kompletně objasnit pravidla řídící rozpoznávání specifických úseků nukleových kyselin proteiny. V této práci se pokouším prozkoumat proces rozpoznávání DNA rozdělením složité sítě kontaktů na rozhraní protein - DNA do příspěvků jednotlivých párů aminokyselina - nukleotid. Tyto páry byly získány z exis- tujících struktur protein - DNA komplexů ve vysokém rozlišení a zpracovány bioinformatickými metodami a nástroji výpočetné chemie. Nově jsem zavedl kritéria specificity sprahující pozorované geometrické preference s relativní energetickou bilancí párů. Aplikací těchto kritérií jsem rozšířil knihovnu párů aminokyselina - nukleotid které se mohou podílet na přímém rozpoznávání sekvence. S cílem prozkoumat fyzikální základy pozorované specificity jsem vypočítal mapy elektrostatických potenciálů pro jednotlivé nukleotidy a vy- brané komplexy. 1Interaction preferences in protein - DNA complexes Dávid Jakubec Abstract Interactions of proteins with DNA lie at the basis of many fundamental bio- logical processes. Despite ongoing efforts, the rules governing the recognition of specific nucleic acid sequences have still not been universally elucidated. In this work, I attempt to explore the recognition process by splitting the intricate network of contacts at the protein - DNA interface into contribu- tions of individual amino acid - nucleotide pairs. These pairs are extracted from existing high-resolution structures of protein - DNA complexes and in- vestigated by bioinformatics and computational-chemistry based methods. Criteria of specificity based on the coupling of observed geometrical prefer- ences and the respective interaction energies are introduced. The application of these criteria is used to expand the library of amino acid - nucleotide pairs potentially significant for direct sequence recognition. Electrostatic poten- tial maps are calculated for individual nucleotides as well as for selected complexes to investigate the physical basis of the observed specificity. 1Department of BiochemistryKatedra biochemiePřírodovědecká fakultaFaculty of Scienc

Towards Improving The Accuracy of Implicit Solvent Models and Understanding Electrostatic Catalysis in Complex Solvent Environment

This thesis develops improved protocols for studying reactions in solution and uses them to explore the possibility of harnessing complex non-standard solvent environments to catalyse chemical reactions. The thesis covers different but related topics: Improving the accuracy of implicit solvent models. Implicit solvent models are simple cost-effective strategies for modelling solvent as a polarizable continuum. However, the accuracy of this approach can be quite variable. Herein, we examine approaches to improving their accuracy through cavity scaling, the choice of theoretical level and the inclusion of explicit solvent molecules. For SMD, we show that the best performance is achieved when cavity scaling is not employed, while for PCM we present a series of electrostatic scale factors that are radii, solvent and ion type dependent. For both families of method, we also highlight the importance choosing an appropriate level of theory, and identify when explicit solvent molecules are required.. Modelling electrostatic catalysis in complex solvent environment. Recent nanoscale experiments have shown that electric fields are capable of catalysing and controlling chemical reactions, but experimental platforms for scaling these effects remain elusive. Herein, two different approaches to addressing this challenge are explored. The first is using the internal electric field of ordered solvents and ionic liquids, the second is using the electric fields that form naturally at the gas-water interface. A multi-scale modelling approach was developed using polarizable force field based molecular dynamic simulation, post-HF, DFT and semi-empirical quantum chemical calculations. We showed that after exposure to an external electric field, ensembles of solvent or ionic liquid molecules become ordered and this ordering can generate an internal electric field, which persists even after the external potential is removed. Experimental collaborators subsequently detected this field as an open-circuit potential that is strong and long-lived. Computationally we showed that this field is enough to lower reaction barriers by as much as 20 kcal mol-1, and we also developed a predictive structure-reactivity model to choose ionic liquids that optimize this field. In the second approach, we harnessed the electric fields of the gas-water interface. A collaborator showed that in the presence of static, inert gas bubbles, the oxidation potential of HO anion/HO radical was dramatically lowered (by more than 0.5V), much more than any subtle concentration effects predicted by the Nernst equation. Further experiments showed that a high unbalanced concentration of HO- ions (as much as 5M) accumulate at the interface. Our multi-scale modelling calculations showed that this reduction in potential was due to the mutual repulsion of the HO- ions and as little as 1M unbalanced excess was enough to explain the experimental results. The work raises opportunities in reducing the cost of electrochemical processes, and points to electrostatic effects contributing to the well-known catalytic effects of "on water" reactions. Works in this thesis are expected to be useful in the future studies of solution-phase pKa, redox potential, electrostatic catalysis and ionic liquids-based electrochemical devices

Accuracy assessment of the linear Poisson-Boltzmann equation and reparametrization of the OBC generalized Born model for nucleic acids and nucleic acid-protein complexes

The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model