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In silico studies of the free energy of binding of kallikrein inhibitors

Grant number: 21/04450-7
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): June 01, 2021
Effective date (End): April 30, 2023
Field of knowledge:Physical Sciences and Mathematics - Chemistry
Principal Investigator:Renato Ferreira de Freitas
Grantee:Wemenes José Lima Silva
Host Institution: Centro de Ciências Naturais e Humanas (CCNH). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
Associated research grant:18/11011-7 - Design of inhibitors of kallikreins 5, 6, and 7 using computational methods and fragment-based approaches, AP.JP


The serine proteases KLK5, KLK6, and KLK7 are considered potential targets for the treatment of Netherton syndrome (KLK5 and KLK7) and neurodegenerative diseases (KLK6). However, there is still a need to develop potent, selective, and active inhibitors in cell and animal models to validate these claims. The identification of compounds that have the potential to inhibit these enzymes can help to change this scenario. Structure-based virtual screening based (SBVS) is a strategy that uses computational methods to identify new inhibitors. These methods have been successful in reproducing the experimentally observed ligand-receptor binding modes. Also, SBVS methods are generally effective in separating active and inactive compounds in retrospective studies. However, these methods are not yet accurate enough to calculate the free energy of binding, which limits their exclusive use in the hit-to-lead optimization phase.In this project, in silico studies will be carried out using more sophisticated computational methods such as molecular dynamics simulations followed by the calculation of the free energy of binding using methods such as GBSA, PBSA, and the free energy perturbation (FEP). The results of these calculations will be compared with the experimental affinity values of KLK5, KLK6, and KLK7 inhibitors to identify a computational protocol that can be used to guide the optimization of the inhibitors of these enzymes. (AU)

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