Ciências Biológicas

Protein exosites, cryptic sites and moonlighting: identification, functional mapping and structural alteration effects
Bases

Ultimas formações
2019 - 2023     Doutor(a), Universidade Federal de Santa Maria
Atividades de Pesquisa

The project involves the development of a multilayer approach to identify protein exositesusing data compilation and neural network training. A pipeline will be developed to identify potential protein partners using phage display experiments and co-localization information. The use of MRND will help identify protein-protein partners that interact at exosite loci. The project will also involvevirtual screening of compounds on the identified exosites of target proteins to identify potential candidates for medicines, taking into account the flexibility of pockets using a set approach. To the internal D-oligopeptide libraries will also be screened against identified pockets using asimilar approach. In addition to the above objectives, this project will also explore the use of NMR and metabolomics to further investigate exosites and identify potential drug candidates. these techniques will complement the other project methods and provide a more comprehensive understanding of the models.

Palavras-chave
None, Machine Learning, Computational Chemistry, Computation , Comp
Palavras-chave (lattes)
Coautores
Resumo Lattes
Postdoctoral Research Scientist (UNICAMP/EMBRAPA) and FAPESP Fellow with a Ph.D. in Biological Sciences (Computational Chemistry focus, UFSM). Expertise in applying Machine Learning, Deep Learning, and Computational Structural Biology to protein structure-function analysis and drug discovery. Developed novel predictive web servers (e.g., STINGAllo) using advanced ML/DL techniques (CatBoost ensembles, Transformers) and large-scale protein data. Significant contributions include SARS-CoV-2 inhibitor research and lecturing experience in Machine Learning for Chemistry. Proven track record of high-impact publications.
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Citações por Ano
Apoio FAPESP em números

1
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