Biomolecular Assembly In Silico Using Massively Parallel Simulations To Probe Protein And Rna Folding Dynamics

It remains a major challenge in molecular biophysics to understand how biomolecules fold and interact with each other to carry out their cellular processes in a functional organism. Computational molecular dynamics (MD) simulations are indispensible tools for characterizing biomolecular processes at the microscopic level. In this thesis, I will present three distinct MD simulation studies of RNA folding, protein-RNA assembly, and protein-nanoparticle interaction mechanisms with direct comparisons to experiments whenever possible. I first present a coarse-grained and empirical force field MD simulations of tRNA. The coarse-grained MD simulations are based on the funneled energy landscape theory of biomolecular folding, and we perform tRNA folding TIS model MD simulations of four E. coli tRNAs with distinct sequences but very similar secondary and tertiary structures. The folding mechanisms are highly dependent on a sequence dependent base-stacking interaction term, demonstrating that the stabilities of the individual loops and stem determine the folding mechanism. We also matched melting profiles with classical empirically observed ones. The secondary structural elements can form in parallel if their stabilities are sufficiently similar, and this may explain why tRNAs have been shown experimentally to have fast and slow folding rates. We also observe the premature folding of some loops that must backtrack and completely unfold before folding to the native state can proceed. The backtracking mechanism has been predicted by simulations and verified in experiments in proteins, and our study is the first to predict the scenario for RNA molecules and it amenable to verification by experiments. Next, I present preliminary development of a novel protein-RNA complex assembly model for coarse-grained MD simulations. The model is applied to aminoacyltRNA synthetase in complex with its cognate tRNA molecule, specifically, a modeled MetRS:tRNA[superscript fMet] complex. The model is based on the native structure based Go-type model for proteins and the TIS model for RNA, but the introduction of the protein-RNA interactions is not trivial. Also, since these models were developed independently, matching their stabilities is a challenge. In our Go-TIS model Hamiltonian, we introduce protein-RNA interactions based on the Lorentz-Berthelot mixing rules and matched the empirical stabilities of the MetRS and tRNA[superscript fMet] separately. The model is not yet complete and we still have issues with regard to the binding stabilities. Finally, I present our novel GPU-optimizedMD simulation model of protein-nanoparticle interactions that was developed in collaboration with an experimental group. In this model, we use the Go-type model for proteins with an electrostatic term to describe the interactions between the charged residues and the charged citrate coated spherical nanoparticle. In excellent agreement with CD spectra, we observe the binding of the proteins to the nanoparticle surface, resulting in the melting of the secondary structure, notably the [alpha symbol]-helices. The percentage of helical melting is in quantitative agreement with our coarse-grained MD simulations.

Very broad overview of the field intended for an interdisciplinary audience; Lively discussion of current challenges written in a colloquial style; Author is a rising star in this discipline; Suitably accessible for beginners and suitably rigorous for experts; Features extensive four-color illustrations; Appendices featuring homework assignments and reading lists complement the material in the main text

Protein Simulation focuses on predicting how protein will act in vivo. These studies use computer analysis, computer modeling, and statistical probability to predict protein function. * Force Fields* Ligand Binding* Protein Membrane Simulation* Enzyme Dynamics* Protein Folding and unfolding simulations

The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models. 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation.

This book presents various computer-aided drug discovery methods for the design and development of ligand and structure-based drug molecules. A wide variety of computational approaches are now being used in various stages of drug discovery and development, as well as in clinical studies. Yet, despite the rapid advances in computer software and hardware, combined with the exponential growth in the available biological information, there are many challenges that still need to be addressed, as this book shows. In turn, it shares valuable insights into receptor-ligand interactions in connection with various biological functions and human diseases. The book discusses a wide range of phylogenetic methods and highlights the applications of Molecular Dynamics Simulation in the drug discovery process. It also explores the application of quantum mechanics in order to provide better accuracy when calculating protein-ligand binding interactions and predicting binding affinities. In closing, the book provides illustrative descriptions of major challenges associated with computer-aided drug discovery for the development of therapeutic drugs. Given its scope, it offers a valuable asset for life sciences researchers, medicinal chemists and bioinformaticians looking for the latest information on computer-aided methodologies for drug development, together with their applications in drug discovery.

Homology modeling is an extremely useful and versatile technique that is gaining more and more space and demand in research in computational and theoretical biology. This book, “Homology Molecular Modeling - Perspectives and Applications”, brings together unpublished chapters on this technique. In this book, 7 chapters are intimately related to the theme of molecular modeling, carefully selected and edited for academic and scientific readers. It is an indispensable read for anyone interested in the areas of bioinformatics and computational biology. Divided into 4 sections, the reader will have a didactic and comprehensive view of the theme, with updated and relevant concepts on the subject. This book was organized from researchers to researchers with the aim of spreading the fascinating area of molecular modeling by homology.

Molecular Dynamics Simulation of Nanocomposites using BIOVIA Materials Studio, Lammps and Gromacs presents the three major software packages used for the molecular dynamics simulation of nanocomposites. The book explains, in detail, how to use each of these packages, also providing real-world examples that show when each should be used. The latter two of these are open-source codes which can be used for modeling at no cost. Several case studies how each software package is used to predict various properties of nanocomposites, including metal-matrix, polymer-matrix and ceramic-matrix based nanocomposites. Properties explored include mechanical, thermal, optical and electrical properties. This is the first book that explores methodologies for using Materials Studio, Lammps and Gromacs in the same place. It will be beneficial for students, researchers and scientists working in the field of molecular dynamics simulation. Gives a detailed explanation of basic commands and modules of Materials Studio, Lammps and Gromacs Shows how Materials Studio, Lammps and Gromacs predict mechanical, thermal, electrical and optical properties of nanocomposites Uses case studies to show which software should be used to solve a variety of nanoscale modeling problems

This volume explores the recent advancements in biomolecular simulations of proteins, small molecules, and nucleic acids, with a primary focus on classical molecular dynamics (MD) simulations at atomistic, coarse-grained, and quantum/ab-initio levels. The chapters in this book are divided into four parts: Part One looks at recent techniques used in the development of physic-chemical models of proteins, small molecules, nucleic acids, and lipids; Part Two discusses enhanced sampling and free-energy calculations; Part Three talks about integrative computational and experimental approaches for biomolecular simulations; and Part Four focuses on analyzing, visualizing, and comparing biomolecular simulations. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Biomolecular Simulations: Methods and Protocols is a valuable resource for both novice and expert researchers who are interested in studying different areas of biomolecular simulations, and discovering new tools to progress their future projects.