Automated Protein Structure Classification: A Survey

TL;DR

This survey reviews recent automated methods for classifying protein structures, highlighting their techniques, accuracy, efficiency, and future research challenges in managing rapidly expanding structural data.

Contribution

It provides a comprehensive overview and comparison of recent automated protein structure classification methods, identifying open problems and future directions.

Findings

Automated methods improve classification speed and accuracy.

Different techniques vary in methodology and efficiency.

Open problems include handling large datasets and improving accuracy.

Abstract

Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.