PyAutoFEP: uma ferramenta de automação para cálculos de FEP para o programa GROMACS integrando técnicas de amostragem estendida

• Ano de defesa: 2021
• Autor(a) principal: Luan Carvalho Martins
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ICB - INSTITUTO DE CIÊNCIAS BIOLOGICAS; Programa de Pós-Graduação em Bioinformatica; UFMG
• Palavras-chave em Português: Perturbações de energia livre; Dinâmicas Moleculares; Ligantes; Descoberta de fármacos de pequenas moléculas; FEP; Bioinformática; Software; Preparações Farmacêuticas
• Link de acesso: http://hdl.handle.net/1843/37822

Free Energy Perturbation (FEP) calculations are now routinely used in drug discovery to estimate the relative free energy of binding (RFEB) of small molecules to a biomolecular target of interest. Using enhanced sampling can improve the correlation between predictions and experimental data, especially in systems with conformational changes. Due to the large number of perturbations required in drug discovery campaigns, manual setup of FEP calculations is no longer viable. Here, we introduce PyAutoFEP, a flexible and open-source tool to aid the setup of RFEB FEP. PyAutoFEP is written in Python3, and automates the generation of perturbation maps, dual-topologies, system building and molecular dynamics (MD), and analysis. PyAutoFEP supports multiple force fields, incorporates two flavors of REST2 enhanced sampling method, and allows flexible λ values along perturbation windows. To validate PyAutoFEP, it was applied to a set of 14 Farnesoid X receptor ligands, a system included in the Drug Design Data Resource Grand Challenge 2. A mean 88% correct sign prediction was achieved, and 75% of the predictions had an error below 1.5 kcal/mol. Results using Amber03/GAFF, CHARMM36m/CGenFF, and OLSA-AA/M/LigParGen had Pearson’s r values of 0.71 ± 0.13, 0.30 ± 0.27 and 0.66 ± 0.20, respectively. The Amber03/GAFF and OLSA-AA/M/LigParGen results were on par with the top Grand Challenge 2 submissions. Applying REST2 improved the results using CHARMM36m/CGenFF (Pearson’s r = 0.43 ± 0.21), but had little impact on the other force fields. Finally, we estimated the probability of finding a molecule 1 pKi better than a lead when using PyAutoFEP to screen 10 or 100 analogs. The probabilities, when comparing to random sampling, increased up 7-fold when 100 molecules were to be screened, suggesting that PyAutoFEP would likely be useful for lead optimization. PyAutoFEP is available on GitHub at https://github.com/lmmpf/PyAutoFEP.