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Determining Optimal Coarse-Grained Representation for Biomolecules Using Internal Cluster Validation Indexes.
Wu, Zhenliang; Zhang, Yuwei; Zhang, John Zenghui; Xia, Kelin; Xia, Fei.
Affiliation
  • Wu Z; Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
  • Zhang Y; Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
  • Zhang JZ; Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
  • Xia K; NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China.
  • Xia F; Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
J Comput Chem ; 41(1): 14-20, 2020 01 05.
Article in En | MEDLINE | ID: mdl-31568566
The development of ultracoarse-grained models for large biomolecules needs to derive the optimal number of coarse-grained (CG) sites to represent the targets. In this work, we propose to use the statistical internal cluster validation indexes to determine the optimal number of CG sites that are optimized based on the essential dynamics coarse-graining method. The calculated curves of Calinski-Harabasz and Silhouette Coefficient indexes exhibit the extrema corresponding to the similar CG numbers. The calculated ratios of the optimal CG numbers to the residue numbers of fine-grained models are in the range from 4 to 2. The comparison of the stability of index results indicates that Calinski-Harabasz index is the better choice to determine the optimal CG representation in coarse-graining. © 2019 Wiley Periodicals, Inc.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Comput Chem Journal subject: QUIMICA Year: 2020 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Comput Chem Journal subject: QUIMICA Year: 2020 Document type: Article Affiliation country: Country of publication: