Literature DB >> 18329160

Amino acid network and its scoring application in protein-protein docking.

Shan Chang1, Xiong Jiao, Chun-hua Li, Xin-qi Gong, Wei-zu Chen, Cun-xin Wang.   

Abstract

Protein-protein complex, composed of hydrophobic and hydrophilic residues, can be divided into hydrophobic and hydrophilic amino acid network structures respectively. In this paper, we are interested in analyzing these two different types of networks and find that these networks are of small-world properties. Due to the characteristic complementarity of the complex interfaces, protein-protein docking can be viewed as a particular network rewiring. These networks of correct docked complex conformations have much more increase of the degree values and decay of the clustering coefficients than those of the incorrect ones. Therefore, two scoring terms based on the network parameters are proposed, in which the geometric complementarity, hydrophobic-hydrophobic and polar-polar interactions are taken into account. Compared with a two-term energy function, a simple scoring function HPNet which includes the two network-based scoring terms shows advantages in two aspects, not relying on energy considerations and better discrimination. Furthermore, combing the network-based scoring terms with some other energy terms, a new multi-term scoring function HPNet-combine can also make some improvements to the scoring function of RosettaDock.

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Year:  2007        PMID: 18329160     DOI: 10.1016/j.bpc.2007.12.005

Source DB:  PubMed          Journal:  Biophys Chem        ISSN: 0301-4622            Impact factor:   2.352


  10 in total

1.  Prediction of protein-binding areas by small-world residue networks and application to docking.

Authors:  Carles Pons; Fabian Glaser; Juan Fernandez-Recio
Journal:  BMC Bioinformatics       Date:  2011-09-26       Impact factor: 3.169

2.  Scoring function based on weighted residue network.

Authors:  Xiong Jiao; Shan Chang
Journal:  Int J Mol Sci       Date:  2011-12-02       Impact factor: 5.923

3.  NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues.

Authors:  Edward S C Shih; Ming-Jing Hwang
Journal:  Biology (Basel)       Date:  2015-03-24

4.  The scoring of poses in protein-protein docking: current capabilities and future directions.

Authors:  Iain H Moal; Mieczyslaw Torchala; Paul A Bates; Juan Fernández-Recio
Journal:  BMC Bioinformatics       Date:  2013-10-01       Impact factor: 3.169

5.  Finding correct protein-protein docking models using ProQDock.

Authors:  Sankar Basu; Björn Wallner
Journal:  Bioinformatics       Date:  2016-06-15       Impact factor: 6.937

Review 6.  Molecular docking as a popular tool in drug design, an in silico travel.

Authors:  Jerome de Ruyck; Guillaume Brysbaert; Ralf Blossey; Marc F Lensink
Journal:  Adv Appl Bioinform Chem       Date:  2016-06-28

7.  iScore: a novel graph kernel-based function for scoring protein-protein docking models.

Authors:  Cunliang Geng; Yong Jung; Nicolas Renaud; Vasant Honavar; Alexandre M J J Bonvin; Li C Xue
Journal:  Bioinformatics       Date:  2020-01-01       Impact factor: 6.937

8.  A modified amino acid network model contains similar and dissimilar weight.

Authors:  Xiong Jiao; Lifeng Yang; Meiwen An; Weiyi Chen
Journal:  Comput Math Methods Med       Date:  2013-01-02       Impact factor: 2.238

9.  Insights into protein-DNA interactions through structure network analysis.

Authors:  R Sathyapriya; M S Vijayabaskar; Saraswathi Vishveshwara
Journal:  PLoS Comput Biol       Date:  2008-09-05       Impact factor: 4.475

10.  Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures.

Authors:  Surabhi Maheshwari; Michal Brylinski
Journal:  BMC Struct Biol       Date:  2015-11-23
  10 in total

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