Evaluating and Scoring Ebolavirus Protein-protein Docking Models Using PIsToN

TL;DR

This paper introduces PIsToN, a deep learning-based scoring function that significantly improves the evaluation and differentiation of native-like protein-protein docking models, demonstrated on Ebola Virus VP40 complex models.

Contribution

The paper presents PIsToN, a novel transformer-based scoring function that outperforms existing methods in assessing protein-protein docking models.

Findings

PIsToN outperforms state-of-the-art scoring functions.

Demonstrated effectiveness on Ebola Virus VP40 complex models.

Provides a reliable method for evaluating docking conformations.

Abstract

Protein-protein docking is crucial for understanding how proteins interact. Numerous docking tools have been developed to discover possible conformations of two interacting proteins. However, the reliability and success of these docking tools rely on their scoring function. Accurate and efficient scoring functions are necessary to distinguish between native and non-native docking models to ensure the accuracy of a docking tool. Like in other fields where deep learning methods have been successfully utilized, these methods have also introduced innovative scoring functions. An outstanding tool for scoring and differentiating native-like docking models from non-native or incorrect conformations is called Protein binding Interfaces with Transformer Networks (PIsToN). PIsToN significantly outperforms state-of-the-art scoring functions. Using models of complexes obtained from binding the Ebola Virus Protein VP40 to the host cell's Sec24c protein as an example, we show how to evaluate docking models using PIsToN.