Implementation for identification and differentiation between exosites and active sites of proteins using deep learning

• Grant number: 23/13576-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: March 01, 2024
• End date: December 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Geraldo Francisco Donegá Zafalon
• Grantee: Luiza Guimarães Cavarçan
• Host Institution: Instituto de Biociências, Letras e Ciências Exatas (IBILCE). Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. São José do Rio Preto , SP, Brazil
• Associated research grant: 20/08615-8 - Protein exosites, cryptic sites and moonlighting: identification, functional mapping and effects of changes in structure, AP.TEM

Abstract

Exosites are structures present in the surface layer of proteins that allow protein-protein and protein-peptide interactions to occur and can be identified through analysis of the protein sequence in question. Observing these exosites it is possible to define new probabilities of interactions even if these have not been observed in nature. However, discovering new types of exosites is manually laborious and costly work, which often makes their identification and analysis unfeasible. Therefore, the present project proposes the use of deep learning techniques to develop a classifier which is able to identify, among the most diverse amino acid sequences, subsequences that represent new exosites and active sites, as well as an analysis of proteins of interest supported by the developed classifier and sequence alignment algorithms, in order to find new possibilities for interactions that can help the development of new drugs.