(507b) Using a Coarse-Grained Modeling Framework to Identify Oligomeric Motifs with Tunable Secondary Structure
AIChE Annual Meeting
2021 Annual Meeting
Engineering Sciences and Fundamentals
Thermodynamics of Biomolecular Folding and Assembly
Wednesday, November 10, 2021 - 12:45pm to 1:00pm
We developed a Python-based molecular simulation framework (cg_openmm)2 for modeling coarse-grained hetero-oligomers and screening them for structural and thermodynamic characteristics of cooperative secondary structures. cg_openmm facilitates the building of coarse-grained topology and random starting configurations, setup of GPU-accelerated replica exchange molecular dynamics simulations (REMD) with the OpenMM software package and features a suite of post-processing thermodynamic and structural analysis tools. Native structures are identified using structure-based clustering, and the Multistate Bennett Acceptance Ratio (MBAR)3 reweighting technique is applied to REMD energies to generate smooth curves of heat capacity, free energy, enthalpy, and entropy of folding, and native contact fraction expectation as functions of temperature.
In this work we demonstrate the capabilities of cg_openmm on a simple 1-1 Lennard-Jones coarse-grained model, in which each residue contains 1 backbone and 1 sidechain bead. By scanning both nonbonded and bonded force field parameter space at the coarse-grained level, we identify and characterize sets of parameters which result in the formation of stable helices through cooperative folding transitions. Moreover, we show that the geometries and stabilities of these helices can be tuned by manipulating the force field parameters. Finally, we discuss insight obtained from cg_openmm into the design of transitions between multiple different secondary structures.
(1) Cheng, R. P. Beyond de Novo Protein Design - De Novo Design of Non-Natural Folded Oligomers. Current Opinion in Structural Biology. 2004, pp 512â520.
(2) cg_openmm is available on GitHub at https://github.com/shirtsgroup/cg_openmm
(3) Shirts, M. R.; Chodera, J. D. Statistically Optimal Analysis of Samples from Multiple Equilibrium States. J. Chem. Phys. 2008, 129 (12).