Performance Comparison of Systematic Methods for Rigorous Definition of Coarse-Grained Sites of Large Biomolecules

Yuwei Zhang, Zexing Cao, John Zhang, Fei Xia

Research output: Contribution to journalArticle

Abstract

Construction of coarse-grained (CG) models for large biomolecules used for multiscale simulations demands a rigorous definition of CG sites for them. Several coarse-graining methods such as the simulated annealing and steepest descent (SASD) based on the essential dynamics coarse-graining (ED-CG) or the stepwise local iterative optimization (SLIO) based on the fluctuation maximization coarse-graining (FM-CG), were developed to do it. However, the practical applications of these methods such as SASD based on ED-CG are subject to limitations because they are too expensive. In this work, we extend the applicability of ED-CG by combining it with the SLIO algorithm. A comprehensive comparison of optimized results and accuracy of various algorithms based on ED-CG show that SLIO is the fastest as well as the most accurate algorithm among them. ED-CG combined with SLIO could give converged results as the number of CG sites increases, which demonstrates that it is another efficient method for coarse-graining large biomolecules. The construction of CG sites for Ras protein by using MD fluctuations demonstrates that the CG sites derived from FM-CG can reflect the fluctuation properties of secondary structures in Ras accurately.

Original languageEnglish (US)
Pages (from-to)214-222
Number of pages9
JournalJournal of Chemical Information and Modeling
Volume57
Issue number2
DOIs
StatePublished - Feb 27 2017

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performance comparison
Biomolecules
fluctuation
demand simulation
Simulated annealing
ras Proteins
Proteins

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Performance Comparison of Systematic Methods for Rigorous Definition of Coarse-Grained Sites of Large Biomolecules. / Zhang, Yuwei; Cao, Zexing; Zhang, John; Xia, Fei.

In: Journal of Chemical Information and Modeling, Vol. 57, No. 2, 27.02.2017, p. 214-222.

Research output: Contribution to journalArticle

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