PASSerRank: Prediction of Allosteric Sites with Learning to Rank

TL;DR

PASSerRank introduces a learning to rank approach for predicting allosteric sites in proteins, significantly improving ranking accuracy and aiding drug discovery efforts.

Contribution

This study presents a novel learning to rank model for allosteric site prediction, outperforming existing machine learning models in ranking accuracy.

Findings

Achieved top 3 ranking for allosteric pockets in over 80% of test cases.

Outperformed other machine learning models in F1 score and Matthews correlation coefficient.

Model available on PASSer platform for drug discovery applications.

Abstract

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, i.e., how well a pocket meets the characteristics of known allosteric sites. The model outperforms other common machine learning models with higher F1 score and Matthews correlation coefficient. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top 3 positions for 83.6% and 80.5% of test proteins, respectively. The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.