Literature DB >> 26344195

Protein structure prediction using residue- and fragment-environment potentials in CASP11.

Hyungrae Kim1, Daisuke Kihara2,3.   

Abstract

An accurate scoring function that can select near-native structure models from a pool of alternative models is key for successful protein structure prediction. For the critical assessment of techniques for protein structure prediction (CASP) 11, we have built a protocol of protein structure prediction that has novel coarse-grained scoring functions for selecting decoys as the heart of its pipeline. The score named PRESCO (Protein Residue Environment SCOre) developed recently by our group evaluates the native-likeness of local structural environment of residues in a structure decoy considering positions and the depth of side-chains of spatially neighboring residues. We also introduced a helix interaction potential as an additional scoring function for selecting decoys. The best models selected by PRESCO and the helix interaction potential underwent structure refinement, which includes side-chain modeling and relaxation with a short molecular dynamics simulation. Our protocol was successful, achieving the top rank in the free modeling category with a significant margin of the accumulated Z-score to the subsequent groups when the top 1 models were considered. Proteins 2016; 84(Suppl 1):105-117.
© 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  CASP11; decoy selection; helix interaction; knowledge-based potential; protein structure prediction; residue environments; scoring functions

Mesh:

Substances:

Year:  2015        PMID: 26344195      PMCID: PMC4781684          DOI: 10.1002/prot.24920

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  54 in total

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Journal:  Proteins       Date:  2013-06-01

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8.  Analysis of CASP8 targets, predictions and assessment methods.

Authors:  Shuoyong Shi; Jimin Pei; Ruslan I Sadreyev; Lisa N Kinch; Indraneel Majumdar; Jing Tong; Hua Cheng; Bong-Hyun Kim; Nick V Grishin
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9.  Effective inter-residue contact definitions for accurate protein fold recognition.

Authors:  Chao Yuan; Hao Chen; Daisuke Kihara
Journal:  BMC Bioinformatics       Date:  2012-11-09       Impact factor: 3.169

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  9 in total

1.  Discovery of Nicotinamide Adenine Dinucleotide Binding Proteins in the Escherichia coli Proteome Using a Combined Energetic- and Structural-Bioinformatics-Based Approach.

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Journal:  J Proteome Res       Date:  2016-12-05       Impact factor: 4.466

2.  Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46.

Authors:  Charles Christoffer; Genki Terashi; Woong-Hee Shin; Tunde Aderinwale; Sai Raghavendra Maddhuri Venkata Subramaniya; Lenna Peterson; Jacob Verburgt; Daisuke Kihara
Journal:  Proteins       Date:  2019-11-25

3.  Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.

Authors:  Lenna X Peterson; Woong-Hee Shin; Hyungrae Kim; Daisuke Kihara
Journal:  Proteins       Date:  2017-09-11

4.  Protein docking model evaluation by 3D deep convolutional neural networks.

Authors:  Xiao Wang; Genki Terashi; Charles W Christoffer; Mengmeng Zhu; Daisuke Kihara
Journal:  Bioinformatics       Date:  2020-04-01       Impact factor: 6.937

5.  Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

Authors:  Lenna X Peterson; Hyungrae Kim; Juan Esquivel-Rodriguez; Amitava Roy; Xusi Han; Woong-Hee Shin; Jian Zhang; Genki Terashi; Matt Lee; Daisuke Kihara
Journal:  Proteins       Date:  2016-10-14

6.  CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline.

Authors:  Axel W Fischer; Sten Heinze; Daniel K Putnam; Bian Li; James C Pino; Yan Xia; Carlos F Lopez; Jens Meiler
Journal:  PLoS One       Date:  2016-04-05       Impact factor: 3.240

7.  Ensemble-based evaluation for protein structure models.

Authors:  Michal Jamroz; Andrzej Kolinski; Daisuke Kihara
Journal:  Bioinformatics       Date:  2016-06-15       Impact factor: 6.937

8.  Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.

Authors:  Woong-Hee Shin; Xuejiao Kang; Jian Zhang; Daisuke Kihara
Journal:  Sci Rep       Date:  2017-01-11       Impact factor: 4.379

9.  Protein Docking Model Evaluation by Graph Neural Networks.

Authors:  Xiao Wang; Sean T Flannery; Daisuke Kihara
Journal:  Front Mol Biosci       Date:  2021-05-25
  9 in total

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