bioRxivBiophysics
SMOG 2 and OpenSMOG: Extending the limits of structure-based models
A. B. Oliveira, V. Contessoto, A. Hassan, S. Byju, A. Wang, Y. Wang, E. G. Dodero Rojas, U. Mohanty, J. Noel, J. N. Onuchic, P. C. Whitford
Abstract
Applying simulations with structure-based (G[o]-like) models has proven to be an effective strategy for investigating the factors that control biomolecular dynamics. The common element of these models is that some (or all) of the intra/inter-molecular inter-actions are explicitly defined to stabilize an experimentally-determined structure. To facilitate the development and application of this broad class of models, we previously released the SMOG 2 software package. This suite allows one to easily customize and distribute structure-based (i.e. SMOG) models for any type of polymer-ligand system. Since its original release, user feedback has driven the implementation of numerous enhancements. Here, we describe recent extensions to the software and demonstrate the capabilities of the most recent version, SMOG v2.4. Changes include new tools that aid user-defined customization of force fields, as well as an interface with the OpenMM simulation libraries (OpenSMOG v1.0). To illustrate the utility of these advances, we present several applications of SMOG2 and OpenSMOG, which include systems with millions of atoms, long polymers and explicit ions. We also highlight how one can incorporate non-structure-based (e.g. AMBER-based) energetics to define a hybrid class of models. The representative applications include large-scale rearrangements of the SARS-CoV-2 Spike protein, the HIV-1 capsid in the presence of explicit ions, and crystallographic lattices of ribosomes and proteins. In summary, SMOG 2 and OpenSMOG provide robust support for researchers who seek to apply structure-based models to large and/or intricate biomolecular systems.
- assay: proteomics
- research focus: protein folding & dynamics
- research focus: software / tool
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
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