A novel and effective scoring scheme for structure classification and pairwise similarity measurement

TL;DR

This paper introduces a new scoring scheme for protein structure classification and similarity measurement, leveraging features from alpha carbon distance matrices, inspired by pattern recognition, and demonstrates superior performance on benchmark datasets.

Contribution

The paper presents a novel scoring scheme based on features from protein alpha carbon distance matrices, improving accuracy over existing methods.

Findings

Outperforms current state-of-the-art methods in family matching.

Effective on standard benchmark structures.

Provides a web service for real-time similarity measurement.

Abstract

Protein tertiary structure defines its functions, classification and binding sites. Similar structural characteristics between two proteins often lead to the similar characteristics thereof. Determining structural similarity accurately in real time is a crucial research issue. In this paper, we present a novel and effective scoring scheme that is dependent on novel features extracted from protein alpha carbon distance matrices. Our scoring scheme is inspired from pattern recognition and computer vision. Our method is significantly better than the current state of the art methods in terms of family match of pairs of protein structures and other statistical measurements. The effectiveness of our method is tested on standard benchmark structures. A web service is available at http://research.buet.ac.bd:8080/Comograd/score.html where you can get the similarity measurement score between two protein structures based on our method.