Literature DB >> 24093228

Detecting protein conformational changes in interactions via scaling known structures.

Fei Guo1, Shuai Cheng Li, Wenji Ma, Lusheng Wang.   

Abstract

Conformational changes frequently occur when proteins interact with other proteins. How to detect such changes in silico is a major problem. Existing methods for docking with conformational changes remain time-consuming, and they solve only a small portion of protein complexes accurately. This work presents a more accurate method (FlexDoBi) for docking with conformational changes. FlexDoBi generates the possible conformational changes of the interface residues that transform the proteins from their unbound states to bound states. Based on the generated conformational changes, multidimensional scaling is performed to construct candidates for the bound structure. We develop a new energy item for determining the orientation of docking subunits and selecting of plausible conformational changes. Experimental results illustrate that FlexDoBi achieves better results. On 20 complexes, we obtained an average iRMSD of 1.55Å, which compares favorably with the average iRMSD of 1.94Å for FiberDock. Compared to ZDOCK, our results are of 0.27Å less in average iRMSD of the medium difficulty group.

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Substances:

Year:  2013        PMID: 24093228      PMCID: PMC3791054          DOI: 10.1089/cmb.2013.0069

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  25 in total

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Journal:  J Mol Biol       Date:  1997-04-04       Impact factor: 5.469

9.  LoopWeaver: loop modeling by the weighted scaling of verified proteins.

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Journal:  J Comput Biol       Date:  2013-03       Impact factor: 1.479

10.  Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.

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  3 in total

Review 1.  Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition.

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Journal:  BMC Bioinformatics       Date:  2019-12-24       Impact factor: 3.169

Review 3.  Application of Multilayer Network Models in Bioinformatics.

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  3 in total

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