PROFASA-a web-based protein fragment and structure analysis workstation

Front Bioeng Biotechnol. 2023 Jul 21:11:1192094. doi: 10.3389/fbioe.2023.1192094. eCollection 2023.

Abstract

Introduction: In the field of bioinformatics and computational biology, protein structure modelling and analysis is a crucial aspect. However, most existing tools require a high degree of technical expertise and lack a user-friendly interface. To address this problem, we developed a protein workstation called PROFASA. Methods: PROFASA is an innovative protein workstation that combines state-of-the-art protein structure visualisation techniques with cutting-edge tools and algorithms for protein analysis. Our goal is to provide users with a comprehensive platform for all protein sequence and structure analyses. PROFASA is designed with the idea of simplifying complex protein analysis workflows into one-click operations, while providing powerful customisation options to meet the needs of professional users. Results: PROFASA provides a one-stop solution that enables users to perform protein structure evaluation, parametric analysis and protein visualisation. Users can use I-TASSER or AlphaFold2 to construct protein models with one click, generate new protein sequences, models, and calculate protein parameters. In addition, PROFASA offers features such as real-time collaboration, note sharing, and shared projects, making it an ideal tool for researchers and teaching professionals. Discussion: PROFASA's innovation lies in its user-friendly interface and one-stop solution. It not only lowers the barrier to entry for protein computation, analysis and visualisation tools, but also opens up new possibilities for protein research and education. We expect PROFASA to advance the study of protein design and engineering and open up new research areas.

Keywords: bioinformatics; computational biology; edutainment; gamification; molecular visualisation; protein modeling; proteins.

Grants and funding

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6223. This work is supported by the Higher Education Authority’s Technological University Transformation Fund and Munster Technological University.