Implementation of machine learning and pattern recognition techniques for prospecting protein exosites as modulators of protein-DNA and protein-RNA interactions

• Grant number: 23/13610-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2023
• End date: October 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Geraldo Francisco Donegá Zafalon
• Grantee: Gabriel Augusto Prevato
• 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 modulate protein-protein and protein-peptide interactions and can be identified by physicochemical and structural descriptors. By observing these exosites, new probabilities of interactions can be defined, even if these have not been observed in nature. However, discovering new types of exosites is manual, laborious and costly work, which often makes their identification and analysis unfeasible. Therefore, the present project proposes the use of machine learning techniques to develop a classifier which is able to identify, among physicochemical descriptors and amino acid structures, which of them represent new exosites, with these acting as modulators of protein-DNA and protein-RNA interactions. With the analysis of proteins of interest aided by the developed classifier, together with sequence alignment and pattern recognition algorithms, the aim is to find possibilities for interactions that can aid the development of new drugs.