Modeling RNA, protein, and synthetic molecules using coarse-grained and all-atom representations

The aim of computational chemistry is to depict and understand the dynamics and interactions of molecular systems. In addition to increased comprehension in the physical and life sciences, this insight yields important applications to therapeutic design and materials science. In computational chemistry, molecules can be modeled in a number of representations depending on the molecular system and phenomena of interest. In this work, both simplified, coarse-grained representations and all-atom representations are used to model the interactions of RNA, cucurbituril host-guest chemistry, and cadmium selenide quantum dot binding to the Src homology 3 domain. For RNA, a coarse-grained model was developed termed RACER (RnA CoarsE-gRained) to accurately predict RNA structure and folding free energy. After optimization to statistical potentials, RACER accurately predicted the structures of 14 RNAs with an average 4.15Å root mean square deviation (RMSD) to the experimental structure. Further, RACER captured the sequence-specific variation in folding free energy for a set of 6 RNA hairpins and 5 RNA duplexes, with a R² correlation of 0.96 to experiment. The binding free energies of a cucurbituril host with 14 guests were computed using a polarizable force field and the free energy techniques of Bennett acceptance ratio and the orthogonal space random walk. The polarizable force field captured binding accurately, yet unexpectedly, the orthogonal space random walk method converged slowly, albeit at still reduced computational expense to the Bennett acceptance ratio. Lastly, the nanotoxicity effects of trioctylphosphine oxide coated cadmium selenide quantum dots are investigated with the model Src homology 3 protein domain in complex with its native proline rich motif ligand. With increasing quantum dot concentration, there is an increasing preference for the quantum dots to bind to the proline rich motif active site, inhibiting Src homology 3 function.Biomedical Engineerin

Coordinate Regulation of G Protein Signaling via Dynamic Interactions of Receptor and GAP

Signal output from receptor–G-protein–effector modules is a dynamic function of the nucleotide exchange activity of the receptor, the GTPase-accelerating activity of GTPase-activating proteins (GAPs), and their interactions. GAPs may inhibit steady-state signaling but may also accelerate deactivation upon removal of stimulus without significantly inhibiting output when the receptor is active. Further, some effectors (e.g., phospholipase C-β) are themselves GAPs, and it is unclear how such effectors can be stimulated by G proteins at the same time as they accelerate G protein deactivation. The multiple combinations of protein–protein associations and interacting regulatory effects that allow such complex behaviors in this system do not permit the usual simplifying assumptions of traditional enzyme kinetics and are uniquely subject to systems-level analysis. We developed a kinetic model for G protein signaling that permits analysis of both interactive and independent G protein binding and regulation by receptor and GAP. We evaluated parameters of the model (all forward and reverse rate constants) by global least-squares fitting to a diverse set of steady-state GTPase measurements in an m1 muscarinic receptor–Gq–phospholipase C-β1 module in which GTPase activities were varied by ∼104-fold. We provide multiple tests to validate the fitted parameter set, which is consistent with results from the few previous pre-steady-state kinetic measurements. Results indicate that (1) GAP potentiates the GDP/GTP exchange activity of the receptor, an activity never before reported; (2) exchange activity of the receptor is biased toward replacement of GDP by GTP; (3) receptor and GAP bind G protein with negative cooperativity when G protein is bound to either GTP or GDP, promoting rapid GAP binding and dissociation; (4) GAP indirectly stabilizes the continuous binding of receptor to G protein during steady-state GTPase hydrolysis, thus further enhancing receptor activity; and (5) receptor accelerates GDP/GTP exchange primarily by opening an otherwise closed nucleotide binding site on the G protein but has minimal effect on affinity (Kassoc = kassoc/kdissoc) of G protein for nucleotide. Model-based simulation explains how GAP activity can accelerate deactivation >10-fold upon removal of agonist but still allow high signal output while the receptor is active. Analysis of GTPase flux through distinct reaction pathways and consequent accumulation of specific GTPase cycle intermediates indicate that, in the presence of a GAP, the receptor remains bound to G protein throughout the GTPase cycle and that GAP binds primarily during the GTP-bound phase. The analysis explains these behaviors and relates them to the specific regulatory phenomena described above. The work also demonstrates the applicability of appropriately data-constrained system-level analysis to signaling networks of this scale

Computational Modeling and Automation Techniques to Study Biomolecular Dynamics

Physically-principled computational modeling and automation techniques have emerged as potent methodologies in exploring biomolecular dynamics and generating experimentally-testable hypotheses. In this dissertation, we develop a set of simulation automation techniques and present results on case studies of biomolecular simulation. Nucleosomes form the fundamental building blocks of eukaryotic chromatin. We use multiscale modeling and discrete molecular dynamics simulations to investigate the dynamics of the Xenopus laevis nucleosome core particle, the fundamental unit of chromatin. Histone tails are flexible and are poorly resolved in X-ray crystal structures. We probe how molecular-level dynamics of the histone tails, core histones and associated DNA mediate chromatin stability at the scale of single-nucleosomes. Based on the positional fluctuations of core histone residues, we postulate cold sites, a set of core histone residues essential for stabilizing the Xenopus laevis nucleosome core particle. We explore changes in the biophysical stability of mono-nucleosomes by designing mutations in core histones and using Medusa, a high-throughput computational technique to explore changes in mononucleosomal stability resulting from point mutations. The presence of centromere-specific H3 variant histone (Cse4) in centromere-specific nucleosomes defines the kinetochore locus. However, structural details of the centromere-specific nucleosomes remain to be completely understood. We construct a homology model of the Saccharomyces cerevisiae centromeric nucleosome and generate a biophysically-principled C-loop model for elongation of Saccharomyces cerevisiae kinetochore. We present simulation automation techniques by means of two web-based servers: iFold (http://iFold.dokhlab.org) and iFoldRNA (http://iFoldRNA.dokhlab.org). iFold enables automated simulations of protein folding, unfolding using discrete molecular dynamics. iFoldRNA enables ab initio RNA structure prediction using replica-exchange discrete molecular dynamics simulations. We also demonstrate rapid and accurate three-dimensional structure prediction of over 150 RNA molecules. We used all-atom molecular dynamics simulations to study the mechanistic and structural differences between two anticancer therapeutics - cisplatin and oxaliplatin. Our simulations suggest that the cisplatinated- and oxaliplatinated- DNA cause differential effects on the dynamics and bending propensities of adducted DNA. This study suggest a role of differential bending propensities in the efficacies of oxaliplatin and cisplatin. In summary, the research presented in this dissertation helps us understand the mechanisms of biomolecular interactions at atomic and mesoscale levels. This dissertation adds to scientific knowledge by a set of methodologies for exploring the dynamics of protein and RNA molecules. Physically-principled simulations of the nucleosome core particle yield experimentally-testable hypotheses on chromatin structure and function

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field

Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration

We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In collective-variable biasing, we first discuss methods stemming from thermodynamic integration that use mean force biasing, including the adaptive biasing force algorithm and temperature acceleration. We then turn to methods that use bias potentials, including umbrella sampling and metadynamics. We next consider parallel tempering and replica-exchange methods. We conclude with a brief presentation of some combination methods. \ua9 2013 by the author; licensee MDPI, Basel, Switzerland

Computational and experimental studies of biomolecules

Integrating experiments and computational modeling is critical for understanding the structure and dynamics of biomolecules. Beyond providing validation for experimental results, computational modeling, that incorporates accurate physical models and enhanced sampling methods, can provide insight into the mechanisms underlying experimental observations. I will present four projects where experiments and computational modeling were used together, to understand mechanisms underlying the structure and dynamics of biomolecules. The first project involves using enhanced sampling to improve the efficiency of calculating the hydration free energies of small molecules using a polarizable force field. These predictions are compared with a conventional free energy method, and excellent agreement is found between the methods. The second project involves using atomic molecular dynamics simulations to determine the molecular mechanism underlying the ability of nanosensor to detect point-mutations in a DNA sequence. By analyzing the nearest-neighbor hydrogen bonding profile, from simulations of the nanosensor, a molecular mechanism was proposed to explain the experimental data. The third project involves the incorporation of non-canonical hydrogen bonding in a RNA coarse-grained model in order to improve 3D structure prediction. This new model is applied to study the sequence-dependent stability of several RNAs including RNA G-quadruplexes. The final project involves the development of a new single-molecule assay to measure local transitions in nucleic acid structures using ultrashort DNA tethers. This project involves collaboration with an experimental biochemistry group to design the DNA tethers and to prepare single-molecule samples. All projects involve the development of new methods to understand the 3D structure and dynamics of biomoleculesPhysic

A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations

Trp-cage is a designed 20-residue polypeptide that, in spite of its size, shares several features with larger globular proteins. Although the system has been intensively investigated experimentally and theoretically, its folding mechanism is not yet fully understood. Indeed, some experiments suggest a two-state behavior, while others point to the presence of intermediates. In this work we show that the results of a bias-exchange metadynamics simulation can be used for constructing a detailed thermodynamic and kinetic model of the system. The model, although constructed from a biased simulation, has a quality similar to those extracted from the analysis of long unbiased molecular dynamics trajectories. This is demonstrated by a careful benchmark of the approach on a smaller system, the solvated Ace-Ala3-Nme peptide. For the Trp-cage folding, the model predicts that the relaxation time of 3100 ns observed experimentally is due to the presence of a compact molten globule-like conformation. This state has an occupancy of only 3% at 300 K, but acts as a kinetic trap. Instead, non-compact structures relax to the folded state on the sub-microsecond timescale. The model also predicts the presence of a state at of 4.4 Å from the NMR structure in which the Trp strongly interacts with Pro12. This state can explain the abnormal temperature dependence of the and chemical shifts. The structures of the two most stable misfolded intermediates are in agreement with NMR experiments on the unfolded protein. Our work shows that, using biased molecular dynamics trajectories, it is possible to construct a model describing in detail the Trp-cage folding kinetics and thermodynamics in agreement with experimental data