Large-scale recognition of high-affinity protease–inhibitor complexes using topological autocorrelation and support vector machines

Abstract

Several methods have been developed for the computer-aided discovery of new protease substrates and inhibitors. In this paper, we report a novel machine learning implementation to identify high-affinity protease–inhibitor complexes. The implemented proteochemometrics algorithm consists of creating topological autocorrelation descriptors for proteases and inhibitors, and then to develop support vector machine models to relate the feature vectors to the affinity class (high or low) of hypothetical protein–inhibitor complexes based on experimental inhibition constant (Ki) values. The approach based on the autocorrelation features surpassed an atom-centred (AC) approach using AC information of inhibitors. Unique to our approach is that our final classifier could recognise 80% of inhibition-complex of new ligands to be stable or unstable using only chemical connectivity of the ligands and sequence information of the targets. Moreover, the analysis of substructure classification showed a very homogenous behaviour of the model on the whole target–ligand space. The predictor is available online at: http://www.materialsinformatics.net/autoproti.html

Keywords::

• QSAR
• machine learning
• data-driven models
• drug design

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

^1. Corresponding author current affiliation: Virtual Nanoscience Laboratory, CSIRO Materials Science & Engineering, 343 Royal Parade, VIC 3052, Australia.

^2. Professor Akinori Sarai sadly passed away on 17 July 2013.

Additional information

Funding

This work has been supported in part by Grants-in-Aid for Scientific Research [grant number 20016022], [grant number 21310131] to AS from Ministry of Education, Culture, Sports, Science and Technology in Japan.