Optimized Elastic Network Models with Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics

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IEEE/ACM Transactions on Computational Biology and Bioinformatics

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The elastic network models (ENMs) are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE) on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The outstanding improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.


Elastic Network Models; Inter residue Cooperativity; Protein Dynamics; Inverse Covariation Estimation; Particle Swarm Optimization


Biostatistics | Physical Sciences and Mathematics | Statistics and Probability



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