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.
Highlights
Discovery of the biologically active substances and their target proteins is a crucial stage in drug development
The earlier version of our method evaluated the possible interaction of protein–ligand pairs by comparing a test sequence with the training proteins partitioned into classes of ligand interactors and non-interactors [29]
We tested our program on five protein groups: G protein-coupled receptors (GPCRs), protein kinases, ligand-gated ion channels, voltage-gated ion channels, and nuclear receptors
Summary
Discovery of the biologically active substances and their target proteins is a crucial stage in drug development. In the case of proteins with unknown ligands, the SAR application for prediction of targets is impossible Another way is modeling the 3D structure of the protein–ligand complex. We suggest the method that predicts protein–ligand interactions from the amino acid sequences classified by the protein affinities to ligands. The earlier version of our method evaluated the possible interaction of protein–ligand pairs by comparing a test sequence with the training proteins partitioned into classes of ligand interactors and non-interactors [29]. Superimposing sequence segments of the test sequence and a training one, the program calculated the positional similarity scores The latter were weighted by the belonging coefficients and integrated on the entire training sets to obtain the estimate of protein–ligand interaction. We showed the applicability of the suggested method for protein families differed by the included proteins’ diversity
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