SMOG 2: A Versatile Software Package for Generating Structure-Based Models

PLoS Comput Biol. 2016 Mar 10;12(3):e1004794. doi: 10.1371/journal.pcbi.1004794. eCollection 2016 Mar.

Abstract

Molecular dynamics simulations with coarse-grained or simplified Hamiltonians have proven to be an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Originally developed in the context of protein folding, structure-based models (SBMs) have since been extended to probe a diverse range of biomolecular processes, spanning from protein and RNA folding to functional transitions in molecular machines. The hallmark feature of a structure-based model is that part, or all, of the potential energy function is defined by a known structure. Within this general class of models, there exist many possible variations in resolution and energetic composition. SMOG 2 is a downloadable software package that reads user-designated structural information and user-defined energy definitions, in order to produce the files necessary to use SBMs with high performance molecular dynamics packages: GROMACS and NAMD. SMOG 2 is bundled with XML-formatted template files that define commonly used SBMs, and it can process template files that are altered according to the needs of each user. This computational infrastructure also allows for experimental or bioinformatics-derived restraints or novel structural features to be included, e.g. novel ligands, prosthetic groups and post-translational/transcriptional modifications. The code and user guide can be downloaded at http://smog-server.org/smog2.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Models, Chemical*
  • Molecular Dynamics Simulation*
  • Protein Conformation
  • Proteins / chemistry*
  • Proteins / ultrastructure*
  • Software Design
  • Software Validation
  • Software*

Substances

  • Proteins

Grants and funding

Work at the Center for Theoretical Biological Physics was sponsored by the NSF (Grants No. PHY- 1427654 and No. MCB-1214457) and by the Welch Foundation (Grant No. C-1792). PCW was supported by a NSF CAREER Award (Grant No. MCB-1350312). JKN is an Alexander von Humboldt Postdoctoral Fellow. JNO acknowledges support as a CPRIT Scholar in Cancer Research sponsored by the Cancer Prevention and Research Institute of Texas. Computing resources were supported in part by the Cyberinfrastructure for Computational Research funded by NSF under Grant No. CNS-0821727. Additionally, computing was supported by the National Science Foundation through XSEDE resources provided by the Texas Advanced Computing Center under grant Nos. TG-MCB110021 and MCB140274, the C3DDB Cluster, and the Northeastern University Discovery Cluster.