A robust machine learning approach for the prediction of allosteric binding sites

Allosteric regulatory sites are highly prized targets in drug discovery. They remain difficult to detect by conventional methods, with the vast majority of known examples being found serendipitously. Herein, a rigorous, wholly-computational protocol is presented for the prediction of allosteric sites. Previous attempts to predict the location of allosteric sites by computational means drew on only a small amount of data. Moreover, no attempt was made to modify the initial crystal structure beyond the in silico deletion of the allosteric ligand. This behaviour can leave behind a conformation with a significant structural deformation, often betraying the location of the allosteric binding site. Despite this artificial advantage, modest success rates are observed at best. This work addresses both of these issues. A set of 60 protein crystal structures with known allosteric modulators was collected. To remove the imprint on protein structure caused by the presence of bound modulators, molecular dynamics was performed on each protein prior to analysis. A wide variety of analytical techniques were then employed to extract meaningful data from the trajectories. Upon fusing them into a single, coherent dataset, random forest - a machine learning algorithm - was applied to train a high performance classification model. After successive rounds of optimisation, the final model presented in this work correctly identified the allosteric site for 72% of the proteins tested. This is not only an improvement over alternative strategies in the literature; crucially, this method is unique among site prediction tools in that is does not abuse crystal structures containing imprints of bound ligands - of key importance when making live predictions, where no allosteric regulatory sites are known.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral-pg

Grantor dc:publisher.institution

University of Strathclyde

Year dc:date.issued

2016

Author dc:creator

• Vassileiou, Antony Dimitri

Advisors dc:contributor.advisor

• Johnston, Blair
• Dufton, Mark

DOI

10.48730/2y3k-8251

Identifier

T14537

Author Identifier

201293005

OAI identifier oai:identifier

oai:strathclyde:pc289j10v