A new search subspace to compensate failure of cavity-based localization of ligand-binding sites. (June 2017)

• Record Type: Journal Article
• Authors: Singh, Kalpana; Lahiri, Tapobrata
• Is Part Of: Computational biology and chemistry. Volume 68(2017)
• Page Start: 6
• Page End: 11
• Publication Date: 2017-06
• Subjects: Ligand-binding site localization -- Alternative search subspace -- Protein atomic cluster roughness; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.01.013
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A new R-subspace alternative to protein-cavity is proposed to localize binding sites. R-subspace is found to be better to localize ligand-binding site (LBS). Proteins for which cavity-subspace fails to localize LBS can be predicted. R-subspace compensates most of the cavity-failure cases. R-subspace and cavity-subspace complementarily enhance success rate to localize LBS. Abstract: The common exercise adopted in almost all the ligand-binding sites (LBS) predictive methods is to considerably reduce the search space up to a meager fraction of the whole protein. In this exercise it is assumed that the LBS are mostly localized within a search subspace, cavities, which topologically appear to be valleys within a protein surface. Therefore, extraction of cavities is considered as a most important preprocessing step for finally predicting LBS. However, prediction of LBS based on cavity search subspace is found to fail for some proteins. To solve this problem a new search subspace was introduced which was found successful to localize LBS in most of the proteins used in this work for which cavity-based method MetaPocket 2.0 failed. Therefore this work appeared to augment well the existing binding site predictive methods through its applicability for complementary set of proteins for which cavity-based methods might fail. Also, to decide on the proteins for which instead of cavity-subspace the new subspace should be explored, a decision framework based on simple heuristic is made which uses geometric parameters of cavities extracted through MetaPocket 2.0. It is found that option for selecting the new or cavity-search subspace can be predicted correctly for nearly 87.5% of test proteins.