Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Z06: Multi-Scale Computational and Theoretical Methods in Molecular BiophysicsFocus Recordings Available
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Sponsoring Units: DBIO DCOMP DSOFT Chair: Raffaello Potestio, DBIO Room: McCormick Place W-178B |
Friday, March 18, 2022 11:30AM - 11:42AM |
Z06.00001: Coarse-grained simulations elucidate the role of Inositol Hexakisphosphate (IP6) in HIV-1 mature viral capsid self-assembly Manish Gupta, Gregory A Voth The human immunodeficiency virus (HIV) maturation process is engineered by capsid (CA) protein, which self-assembles into a cone-shaped shell to encase and protect the viral RNA. Naturally occurring inositol hexakisphosphates (IP6) facilitate mature capsid assembly by coordinating a ring of arginines at the pores distributed throughout the capsid lattice surface. In this work, we adopted a Coarse-Graining approach to study the structural basis of CA and IP6 interaction during maturation since it is difficult to effectively address the implicated time and length scale using a fine-grained atomistic model. We use coarse-grained molecular dynamics simulations to elucidate the molecular mechanisms that enable IP6 to have crucial roles in the HIV replication cycle. We find that in the absence of IP6, CA prefers tubular morphology which is primarily comprised of hexameric CA components. The mechanistic details of self-assembly reveal that at physiologically relevant concentrations, IP6 stabilizes the capsid lattice by binding at the regions of high curvature during maturation and increases the stable life of the capsid. The self-assembly process is accelerated by IP6 and favors rapid incorporation of CA pentamers which leads to increased structural pleomorphism in mature capsids. |
Friday, March 18, 2022 11:42AM - 11:54AM |
Z06.00002: Tools for Local Elasticity Calculations and their Applications to Simulations of Lipid Biomembranes Andrew L Lewis, Benjamin E Himberg, Juan M Vanegas Modeling the physical and chemical properties of a biological system present a unique challenge due to the varying length and time scales of many biological processes. Our Research Group has developed tools using the Irving-Kirkwood-Noll Statistical mechanics framework for the purpose of modeling biological processes. Here I will present our development and implementation of the tools for the elasticity calculations in an open-source library MDstress and the GROMACS-LS post-processing analysis code. We Implement the stress-stress fluctuation formula in three parts in order to compute the local elasticity tensor for a variety of materials. To compute the so-called ‘Born term’ of the stress-stress fluctuation formula, we derive relations for the second derivatives of 2, 3, and 4-body potentials commonly used in biomolecular simulations. We validate our numerical implementation on a simple suite of tests on liquids and solids including, solid and liquid Argon, liquid water, and a fluid lipid bilayer using the coarse-grained MARTINI force field. Preliminary results show the efficacy of our tools and results in good agreement with prior simulations and experiments. Furthermore, we explore important questions regarding the elastic properties and fluidity of models of lipid membranes. |
Friday, March 18, 2022 11:54AM - 12:06PM |
Z06.00003: Towards a model for charge-driven protein reconstitution using dynamical self-consistent field theory Sylvia M Luyben, Robert A Wickham In protein reconstitution, membrane proteins are extracted from their native membrane and inserted into an artificial membrane selected to have more desirable properties for a given application. Experiments found that adding opposite charge to the artificial membrane and protein has advantages over the neutral reconstitution, namely increased speed and reduced complexity of the reconstitution1. We extend neutral polymeric dynamical self-consistent field theory (dSCFT) to include charge interactions, salt, and solvent. This enables us to consider together charge interactions, self-assembly, and the dynamics of polymers and solvent, which we believe are key elements in the charge-driven reconstitution mechanism. We apply this dSCFT to study the influence of charge on the kinetics of reconstitution of toy proteins, modeled as ABA triblock copolymers with anionic solvophilic A blocks, into a cationic ABA triblock copolymer membrane in solvent. |
Friday, March 18, 2022 12:06PM - 12:18PM |
Z06.00004: Dynamical self-consistent field theory simulation of dendritic phytoglycogen nanoparticles Benjamin E Morling, Sylvia M Luyben, John R Dutcher, Robert A Wickham Phytoglycogen (PG) is a naturally occurring, highly branched, glucose dendrimer that is extracted from sweet corn as soft, compact nanoparticles.1 We use dynamical self-consistent field theory (dSCFT) to simulate the dynamical evolution of a PG nanoparticle solubilized in water. We evolve the 11-generation dendrimer using an efficient, stable operator decomposition of the dendrimer into its branches. By varying the strength of the interactions between the PG nanoparticle and water, we are able to tune the size and the degree of hydration of the nanoparticle and compare with the values measured using small angle neutron scattering.1 Motivated by experimental investigations of chemically modified versions of PG nanoparticles, we extend our dSCFT approach to describe the interactions of small molecular species with the nanoparticles. |
Friday, March 18, 2022 12:18PM - 12:30PM |
Z06.00005: Iterative steady-state restarting of weighted ensemble simulations John D Russo, Jeremy T Copperman, Daniel M Zuckerman Although the weighted ensemble (WE) algorithm provides an efficient, unbiased framework for rare-event sampling in molecular dynamics (MD), WE convergence timescales are often still limiting for very slow systems. This is because unbiased estimates of observables generally are computed from simulations which have converged to steady state. Recent work has shown that history-augmented Markov models (haMSMs) can provide estimates of steady state from transient, unconverged WE data; additionally, a new WE simulation can be initialized using structures from the initial simulation, weighted according to steady state. We demonstrate how this process improves performance, as well as a new iterative pipeline of repeated restarts based on haMSM steady-state estimates. |
Friday, March 18, 2022 12:30PM - 12:42PM |
Z06.00006: Dynamic Density Functional Theory of Multicomponent Cellular Membranes Liam G Stanton, Tomas Oppelstrup, Helgi I Ingolfsson, Michael P Surh, Felice C Lightstone, James N Glosli We present a continuum model trained on molecular dynamics (MD) simulations for cellular membranes composed of an arbitrary number of lipid types. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This framework represents a paradigm shift by enabling simulations that can access micron length-scales and time-scales on the order of seconds, all while maintaining near-fidelity to the underlying MD models. As an application, we consider membrane interactions with RAS, a potentially oncogenic protein. Simulation results are presented and verified with MD simulations, and implications of this new capability are discussed. |
Friday, March 18, 2022 12:42PM - 12:54PM |
Z06.00007: Modeling the cell cycle of the minimal bacterial cell JCVI-syn3A Madeline L Stover, Zane R Thornburg, David M Bianchi, Troy A Brier, Benjamin R Gilbert, James Saenz, Clyde A Hutchison III, John I Glass, Zaida Luthey-Schulten The minimal bacterium JCVI-syn3A, with only 492 genes, provides a unique opportunity for whole-cell computational modeling. We have a whole-cell model of metabolism and genetic information processing based on gene essentiality and proteomics studies (Breuer et al., eLife 2019). This is a kinetic model, as opposed to flux balance analysis of the essential metabolism which only predicts steady state behavior, and our simulations are hybrid stochastic-deterministic. To achieve a complete cell cycle in the whole-cell computational model of JCVI-syn3A it is necessary to model its growth and subsequent division mediated by Z-ring assembly. Our whole-cell kinetic model predicts a membrane growth rate based on the synthesis and incorporation of lipids and membrane proteins. The well-stirred kinetic model recapitulates the experimentally measured growth rates, with the cell surface area and initial protein counts doubling on average in 97 minutes. Cryo-electron tomograms give the location of the cell's ribosomes for a small (~200 nm radius) and large cell (~250 nm radius). From this experimental data, we simulate growth by updating membrane size and ribosome positions and translocating membrane proteins. |
Friday, March 18, 2022 12:54PM - 1:06PM |
Z06.00008: Coarse-grained simulations of RNAs allowing for conformational transitions of the sugar pucker Yiheng Wu, Riccardo Alessandri, Aria E Coraor, Tobin R Sosnick, Juan De Pablo All-atom molecular dynamic simulations are generally restricted by the time scale that can be sampled. To address this issue, various coarse-grained RNA models have been developed. These models often assume the ribose, a 5 membered ring, is fixed in only one conformation, typically the 3'-endo found in A-form helices. The models neglect the 2'-endo conformation that can be found in B-form helices and other RNA structures and is known to influence ion binding and catalytic function in addition to secondary structure propensity. Accordingly, we have developed a coarse-grained RNA force field to include transitions between the two sugar pucker states in order to accurately reproduce RNA structures and thermodynamics. To model the sugar pucker, we train a coarse-grained collective variable that distinguishes between the two states. Using this collective variable, we interpolate between two sets of force field parameters to properly reflect the structural differences between the two conformational states. The parameters of the entire model are optimized against RNA structures and melting temperatures utilizing our contrastive divergence algorithm. Our coarse-grained RNA model opens up the opportunities to study complex RNA dynamics especially those coupled to sugar pucker transitions. |
Friday, March 18, 2022 1:06PM - 1:18PM |
Z06.00009: Building Stimuli-Responsive Nanoparticle Assemblies In Polymer Matrix and Solutions Pothukuchi Rajesh Pavan, Mithun Radhakrishna Nanoparticle assemblies had drawn tremendous interest because of their potential applications in the fields of biomedicine, drug delivery, therapeutics, and cancer cell imaging. Building nanoparticle assemblies that are responsive to external stimuli offers great potential to tune a wide array of morphological transitions. Self-assembly of polymer grafted nanoparticles is responsive to external stimuli including polymer chain length, polymer concentration, salt concentration, pH, ionic strength of salt, and so on. In the present work, we used electrostatics to tune these stimuli-responsive transitions in solutions. Molecular dynamics simulations have been performed in the framework of the coarse grain model to study and understand the transitions in self-assembly. Transitions in self-assembly at different graft lengths and graft densities are reported. By varying the parameters including polymer and salt concentration, matrix length, and polymer weight, we are able to tune the transitions of self-assembled morphologies from rings to dispersed state, ordered crystal structures to smaller disordered aggregates. We believe that this model will act as a template in building stimuli-responsive systems which offer diverse applications in bio-imaging, targeting drug delivery, and sensing applications. |
Friday, March 18, 2022 1:18PM - 1:30PM |
Z06.00010: Rational discovery of cardiolipin-selective small molecules by coarse-grained high-throughput simulations Bernadette Mohr, Kirill Shmilovich, Isabel S Kleinwächter, Dirk Schneider, Andrew L Ferguson, Tristan Bereau High throughput screening is a relevant tool for molecular discovery and design. We make use of efficient coarse-grained free-energy calculations in combination with active learning to streamline the identification of relevant physical and chemical properties that determine selectivity towards a given biomolecular target. Simplifying the molecular representation through a small set of bead types offers two advantages: first, many molecules map to the same CG representation and second, screening boils down to systematically varying among the set of CG bead types available. The gained information allows us to formulate design rules that can be used to identify candidate molecules for experimental validation. |
Friday, March 18, 2022 1:30PM - 2:06PM |
Z06.00011: Simulating the molecular processes of living matter - what is missing from current models Invited Speaker: Ilpo Vattulainen Modeling and simulations of biological systems help to understand how biological functions occur, how molecules that generate the functions are activated, and how the function of these molecules can be controlled. However, the challenge for simulation models is that very often they do not correspond to conditions that prevail in living matter. Although biological processes occur under equilibrium conditions, they are often simulated in equilibrium. Although proteins are often enzymatically glycosylated, this is typically not considered in simulation models. The function of proteins is very sensitive to the protonation states of their amino acids, but these are very difficult to account for, for example, in the vicinity of cell membranes, where the electrostatic environment is exceptionally heterogeneous and poorly defined. These and many other similar situations illustrate the challenges that need to be addressed to allow simulation of biomolecular functions under realistic conditions matching in vivo experiments. In this work, we discuss the significance of these challenges as well as the preliminary methodological work that has been conducted. |
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