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• W2356074920 abstract "Abstract Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96–6.37% and 2.42–5.19% on the three datasets over using single templates. MTMG’s performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html ." @default.
• W2356074920 created "2016-06-24" @default.
• W2356074920 creator A5012654073 @default.
• W2356074920 creator A5044354277 @default.
• W2356074920 date "2016-05-10" @default.
• W2356074920 modified "2023-10-17" @default.
• W2356074920 title "A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling" @default.
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• W2356074920 doi "https://doi.org/10.1038/srep25687" @default.
• W2356074920 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4861977" @default.
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• W2356074920 hasPublicationYear "2016" @default.
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