Bayesian selection for coarse-grained models of liquid water

The necessity for accurate and computationally efficient representations of water in atomistic simulations that can span biologically relevant timescales has born the necessity of coarse-grained (CG) modeling. Despite numerous advances, CG water models rely mostly on a-priori specified assumptions. How these assumptions affect the model accuracy, efficiency, and in particular transferability, has not been systematically investigated. Here we propose a data driven, comparison and selection for CG water models through a Hierarchical Bayesian framework. We examine CG water models that differ in their level of coarse-graining, structure, and number of interaction sites. We find that the importance of electrostatic interactions for the physical system under consideration is a dominant criterion for the model selection. Multi-site models are favored, unless the effects of water in electrostatic screening are not relevant, in which case the single site model is preferred due to its computational savings. The charge distribution is found to play an important role in the multi-site model's accuracy while the flexibility of the bonds/angles may only slightly improve the models. Furthermore, we find significant variations in the computational cost of these models. We present a data informed rationale for the selection of CG water models and provide guidance for future water model designs

Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics

Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code.Comment: 44 pages, 19 figure

Molecular Simulations of Protein-Induced Membrane Remodeling

Membranes organize much of the cell and host a great deal of molecular machinery required to integrate signals from the outside, regulate the surrounding matrix, change shape, move, and grow. Understanding how a dense forest of proteins, sugars, and biomarkers modulates the shape of the cell is necessary to produce more detailed, accurate predictions of cell behavior, particularly in the studies of cell signaling processes that lead to oncogenesis. In this dissertation, I will present a series of molecular models which, when combined with continuum models and both in vitro and in vivo experiments, describe the molecular basis for membrane morphology changes. In particular, we investigate the mechanisms by which proteins assemble on a bilayer undergoing thermal fluctuations. This work serves to quantify and explain a series of biophysical experiments in molecular detail, and contributes to the development of multiscale models for predicting cell fate

Dissipative electro-elastic network model of protein electrostatics

We propose a dissipative electro-elastic network model (DENM) to describe the dynamics and statistics of electrostatic fluctuations at active sites of proteins. The model combines the harmonic network of residue beads with overdamped dynamics of the normal modes of the network characterized by two friction coefficients. The electrostatic component is introduced to the model through atomic charges of the protein force field. The overall effect of the electrostatic fluctuations of the network is recorded through the frequency-dependent response functions of the electrostatic potential and electric field at the active site. We also consider the dynamics of displacements of individual residues in the network and the dynamics of distances between pairs of residues. The model is tested against loss spectra of residue displacements and the electrostatic potential and electric field at the heme's iron from all-atom molecular dynamics simulations of three hydrated globular proteins

Membrane models for molecular simulations of peripheral membrane proteins

Peripheral membrane proteins (PMPs) bind temporarily to the surface of biological membranes. They also exist in a soluble form and their tertiary structure is often known. Yet, their membrane-bound form and their interfacial-binding site with membrane lipids remain difficult to observe directly. Their binding and unbinding mechanism, the conformational changes of the PMPs and their influence on the membrane structure are notoriously challenging to study experimentally. Molecular dynamics simulations are particularly useful to fill some knowledge-gaps and provide hypothesis that can be experimentally challenged to further our understanding of PMP-membrane recognition. Because of the time-scales of PMP-membrane binding events and the computational costs associated with molecular dynamics simulations, membrane models at different levels of resolution are used and often combined in multiscale simulation strategies. We here review membrane models belonging to three classes: atomistic, coarse-grained and implicit. Differences between models are rooted in the underlying theories and the reference data they are parameterized against. The choice of membrane model should therefore not only be guided by its computational efficiency. The range of applications of each model is discussed and illustrated using examples from the literature.publishedVersio

Computational Methods in Biomolecules:Study of Hydrophilic Interactions in Protein Folding & Constant-pH Molecular Simulation of pH Sensitive Lipid MORC16

Water molecules play a significant role in biological process and are directly involved with bio-molecules and organic compounds and ions. Recent research has focused on the thermal dynamics and kinetics of water molecules in solution, including experimental (infrared spectroscopy and Raman spectroscopy) and computational (Quantum Mechanics and Molecular Dynamics) approaches. The reason that water molecules are so unique, why they have such a profound influence on bio-activity, why water molecules show some anomalies compared to other small molecules, and where and how water molecules exert their influence on solutes are some of the areas under study. We studied some properties of hydrogen bond networks, and the relationship of these properties with solutes in water. Molecular dynamics simulation, followed by an analysis of “water bridges”, which represent protein-water interaction have been carried out on folded and unfolded proteins. Results suggest that the formation of transient water bridges within a certain distance helps to consolidate the protein, possibly in transition states, and may help further guide the correct folding of proteins from these transition states. This is supporting evidence that a hydrophilic interaction is the driving force of protein folding. Biological membranes are complex structures formed mostly by lipids and proteins. For this reason the lipid bilayer has received much attention, through computation and experimental studies in recent years. In this dissertation, we report results of a newly designed pH sensitive lipid MORC16, through all-atom and coarse-grained models. The results did not yield a MORC16 amphiphile which flips its conformation in response to protonation. This may be due to imperfect force field parameters for this lipid, an imperfect protonation definition, or formation of hydrogen bond does not responsible for conformation flip in our models. Despite this, some insights for future work were obtained