Contact-based molecular dynamics of structured and disordered proteins in a coarse-grained model: Fixed contacts, switchable contacts and those described by pseudo-improper-dihedral angles

L Mioduszewski and J Bednarz and M Chwastyk and M Cieplak, COMPUTER PHYSICS COMMUNICATIONS, 284, 108611 (2023).

DOI: 10.1016/j.cpc.2022.108611

We present a coarse-grained C alpha-based protein model that can be used to simulate structured, intrinsically disordered and partially disordered proteins. We use a Go-like potential for the structured parts and two different variants of a transferable potential for the disordered parts. The first variant uses dynamic structure-based (DSB) contacts that form and disappear quasi-adiabatically during the simulation. By using specific structural criteria we distinguish sidechain-sidechain, sidechain-backbone and backbone -backbone contacts. The second variant is a non-radial multi-body pseudo-improper-dihedral (PID) potential that does not include time-dependent terms but requires more computational resources. Our model can simulate in reasonable time thousands of residues on millisecond time scales.Program summary Program Title: cg CPC Library link to program files: https://doi .org /10 .17632 /dp7gwrs94n .1 Code Ocean capsule: https://codeocean .com /capsule /9528010 Licensing provisions: MIT Programming language: C++ (new version), Fortran (old version) Nature of problem: Simulations of one or more protein chains, structured, intrinsically disordered or partially disordered. Calculating their equilibrium and kinetic properties in processes including but not limited to: folding, aggregation, conformational changes, formation of complexes, aggregation, response to deformation. All those processes need long simulation times or many trajectories to properly sample the system. Solution method: The simulations use a molecular dynamics implicit-solvent coarse-grained model where each pseudoatom represents one amino acid residue. Residues are harmonically connected to form a chain. The system evolves according to Langevin dynamics. The backbone stiffness potential involves bond angle and dihedral angle terms (or a chirality term in the structured case). Residues interact via modified Lennard-Jones or Debye-Hueckel potentials. The potential is different for structured and disordered parts of a protein. A Go model contact map is used for the structured parts, where an interaction between two residues is attractive if effective spheres associated with their heavy atoms overlap in the native structure 1,2,3,4. Non-attractive contacts use only the repulsive part of the Lennard-Jones potential. The potential for the disordered parts has two versions: in the quasi-adiabatic Dynamic Structure-Based (DSB) variant, the contacts are formed dynamically based on the structure of the chain and are quasi-adiabatically turned on and off 5,6. In the slower, but more accurate Pseudo-Improper-Dihedral (PID) variant the Lennard-Jones term is multiplied by a pseudo-improper- dihedral term that gives similar geometric restrictions with a time- independent Hamiltonian 7. Proteins can be pulled by their termini to mimic stretching by an Atomic Force Microscope 8,9. Multiple protein chains can be put in a simulation box with periodic boundary conditions or with walls that may be repulsive or attractive for the residues. The walls may move to mimic pulling or shearing deformations. The program accepts PDB files or protein sequences with optional user-defined contact maps. It saves the results in an output file (summary), a PDB file (structure) and a map file (contact map in a given snapshot). Additional comments including restrictions and unusual features: The program supports openMP (effective up to 8 cores, use 4 cores for optimal speed-up). The C++ version is hosted on Github, the Fortran version is distributed as a .tar archive. References 1 M. Sikora, J.I. Sulkowska, M. Cieplak, PLoS Comput. Biol. 5 (2009) e1000547. 2 J.I. Sulkowska, M. Cieplak, Biophys. J. 95 (2008) 317491. 3 K. Wolek, A. Gomez-Sicilia, M. Cieplak, J. Chem. Phys. 143 (2015) 243105. 4 Y. Zhao, M. Chwastyk, M. Cieplak, Sci. Rep. 7 (2017) 39851. 5 L. Mioduszewski, M. Cieplak, Phys. Chem. Chem. Phys. 20 (2018) 19057-19070. 6 L. Mioduszewski, M. Cieplak, PLoS Comput. Biol. 17 (3) (2021) e1008840. 7 L. Mioduszewski, B. Ro zycki, M. Cieplak, J. Chem. Theory Comput. 16 (7) (2020) 4726-4733. 8 Y. Zhao, M. Chwastyk, M. Cieplak, J. Chem. Phys. 146 (2017) 225102. 9 M. Chwastyk, A. Galera-Prat, M. Sikora, A. Gomez-Sicilia, M. Carrion-Vazquez, M. Cieplak, Proteins 82 (2014) 717-726.(c) 2022 Published by Elsevier B.V.

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