Literature DB >> 26668124

A new algorithm for construction of coarse-grained sites of large biomolecules.

Min Li1, John Z H Zhang1,2, Fei Xia2,3.   

Abstract

The development of coarse-grained (CG) models for large biomolecules remains a challenge in multiscale simulations, including a rigorous definition of CG representations for them. In this work, we proposed a new stepwise optimization imposed with the boundary-constraint (SOBC) algorithm to construct the CG sites of large biomolecules, based on the s cheme of essential dynamics CG. By means of SOBC, we can rigorously derive the CG representations of biomolecules with less computational cost. The SOBC is particularly efficient for the CG definition of large systems with thousands of residues. The resulted CG sites can be parameterized as a CG model using the normal mode analysis based fluctuation matching method. Through normal mode analysis, the obtained modes of CG model can accurately reflect the functionally related slow motions of biomolecules. The SOBC algorithm can be used for the construction of CG sites of large biomolecules such as F-actin and for the study of mechanical properties of biomaterials.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  ENM-ED-CG; coarse-grained representation; elastic network model

Mesh:

Substances:

Year:  2015        PMID: 26668124     DOI: 10.1002/jcc.24265

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  3 in total

Review 1.  Bottom-up Coarse-Graining: Principles and Perspectives.

Authors:  Jaehyeok Jin; Alexander J Pak; Aleksander E P Durumeric; Timothy D Loose; Gregory A Voth
Journal:  J Chem Theory Comput       Date:  2022-09-07       Impact factor: 6.578

2.  Comparative Normal Mode Analysis of the Dynamics of DENV and ZIKV Capsids.

Authors:  Yin-Chen Hsieh; Frédéric Poitevin; Marc Delarue; Patrice Koehl
Journal:  Front Mol Biosci       Date:  2016-12-27

3.  Data-driven coarse graining of large biomolecular structures.

Authors:  Yi-Ling Chen; Michael Habeck
Journal:  PLoS One       Date:  2017-08-17       Impact factor: 3.240

  3 in total

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