Back to Search Start Over

Prediction of Protein–ligand Interaction Based on Sequence Similarity and Ligand Structural Features

Authors :
Dmitry Karasev
Boris Sobolev
Alexey Lagunin
Dmitry Filimonov
Vladimir Poroikov
Source :
International Journal of Molecular Sciences, Vol 21, Iss 21, p 8152 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the original procedure overcoming the data ambiguity. Our study showed the effective prediction of new targets for ligands with an average accuracy of 0.96. The prediction of new ligands for targets displayed the average accuracy 0.95; accuracy estimates were close to our previous results, comparable in accuracy to those of other methods or exceeded them. Using the fuzzy coefficients reflecting the target-to-ligand specificity, we provided predicting interactions for new proteins and new ligands; the obtained accuracy values from 0.89 to 0.99 were acceptable for such a sophisticated task. The protein kinase family case demonstrated the ability to account for subtle features of proteins and ligands required for the specificity of protein–ligand interaction.

Details

Language :
English
ISSN :
14220067 and 16616596
Volume :
21
Issue :
21
Database :
Directory of Open Access Journals
Journal :
International Journal of Molecular Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.f95db19f9f5d4dc1bcc09013f979fe0a
Document Type :
article
Full Text :
https://doi.org/10.3390/ijms21218152