SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties

TL;DR

This paper reviews methods from the SHREC 2021 contest that evaluate the effectiveness of computational approaches in retrieving and classifying protein surfaces based on geometry and physicochemical properties.

Contribution

It introduces a benchmark for assessing retrieval and classification techniques using protein surface data with and without physicochemical information.

Findings

Physicochemical properties improve retrieval accuracy

Methods effectively identify protein conformations

Surface geometry alone provides significant classification insights

Abstract

This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemical properties. The goal of the contest is to assess the capability of different computational approaches to identify different conformations of the same protein, or the presence of common sub-parts, starting from a set of molecular surfaces. We addressed two problems: defining the similarity solely based on the surface geometry or with the inclusion of physicochemical information, such as electrostatic potential, amino acid hydrophobicity, and the presence of hydrogen bond donors and acceptors. Retrieval and classification performances, with respect to the single protein or the existence of common sub-sequences, are analysed according to a number of information retrieval indicators.