HTP SurflexDock: a web tool for Structure-Based Virtual Screening analysis based on the Ensemble Docking protocol
Structure-Based Virtual Screening (SVBS) is a technique traditionally used to find a set of specific inhibitors for a receptor structure during the preliminary stages of drug discovery studies. However, more than 90% of SBVS best ranks compounds do not have the expected biological effect at the end of the process. In this context, strategies to increase the success rate must be employed to ensure the experiment's success. Here, we introduce the HTP SurflexDock, a tool that improves the success rate of SBVS experiments through two strategies: First, the ensemble docking protocol enables the simulation of the implicit flexibility of the receptor structure. Second, a post-processing steps allows the user to rescore promising compounds by expanding the conformational space search or estimating the binding free energy through the MM/PBSA protocol. HTP SurflexDock is useful when dealing with flexible receptors and when structural information about the receptor contact area is insufficient or optimized for a specific ligand type. HTP SurflexDock is freely available as a web service or may be downloaded at https://htpsurflexdock.biocomp.uenf.br/.
Antunes, D. A., Devaurs, D. and Kavraki, L. E. (2015). Understanding the challenges of protein flexibility in drug design. Expert Opinion on Drug Discovery, v. 10, n. 12, p. 1301–1313.
Bateman, B. T., Patorno, E., Desai, R. J., et al. (2017). Angiotensin-converting enzyme inhibitors and the risk of congenital malformations. Obstetrics and gynecology, v. 129, n. 1, p. 174.
Bernardi, A., Faller, R., Reith, D. and Kirschner, K. N. (2019). ACPYPE update for nonuniform 1--4 scale factors: Conversion of the GLYCAM06 force field from AMBER to GROMACS. SoftwareX, v. 10, p. 100241.
Case, D. A., Belfon, K., Ben-Shalom, I., et al. (2020). Amber 2020.
Chaudhury, S. and Gray, J. J. (2008). Conformer selection and induced fit in flexible backbone protein--protein docking using computational and NMR ensembles. Journal of molecular biology, v. 381, n. 4, p. 1068–1087.
Daura, X., Gademann, K., Jaun, B., et al. (1999). Peptide folding: when simulation meets experiment. Angewandte Chemie International Edition, v. 38, n. 1–2, p. 236–240.
De A Filho, J. L., Del Real Tamariz, A. and Fernandez, J. H. (2018). AutoModel: A Client-Server Tool for Intuitive and Interactive Homology Modeling of Protein-Ligand Complexes. In Brazilian Symposium on Bioinformatics.
De Almeida Filho, J. L. and Fernandez, J. H. (2019). MDR SurFlexDock: A Semi-automatic Webserver for Discrete Receptor-Ensemble Docking. In Brazilian Symposium on Bioinformatics.
Evans, M., Carrero, J.-J., Szummer, K., et al. (2016). Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers in myocardial infarction patients with renal dysfunction. Journal of the American College of Cardiology, v. 67, n. 14, p. 1687–1697.
Feixas, F., Lindert, S., Sinko, W., et al. (2014). An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. Journal of chemical theory and computation, v. 5, n. 12, p. 31–45.
Fernandez, J.H., Neshich, G., Camargo, A.C.M. (2004) Using bradykinin-potentiating peptide structures to develop new antihypertensive drugs. Genetics and Molecular Research, v. 3, n. 4, Pages 554-563.
Gasteiger, J. and Marsili, M. (1978). A new model for calculating atomic charges in molecules. Tetrahedron letters, v. 19, n. 34, p. 3181–3184.
Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in science and engineering, v. 9, n. 3, p. 90–95.
Kolodzik, A., Schneider, N. and Rarey, M. (2018). Structure-Based Virtual Screening. Applied Chemoinformatics: Achievements and Future Opportunities, p. 313–331.
Lavecchia, A. and Di Giovanni, C. (2013). Virtual screening strategies in drug discovery: a critical review. Current medicinal chemistry, v. 20, n. 23, p. 2839–2860.
Lionta, E., Spyrou, G., K Vassilatis, D. and Cournia, Z. (2014). Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current topics in medicinal chemistry, v. 14, n. 16, p. 1923–1938.
Masuyer, G., Schwager, S. L. U., Sturrock, E. D., Isaac, R. E. and Acharya, K. R. (2012). Molecular recognition and regulation of human angiotensin-I converting enzyme (ACE) activity by natural inhibitory peptides. Scientific Reports, v. 2, p. 1–10.
Matos, A., Caetano, B., De Almeida Filho, J. L., et al. (2022). Identification of Hypericin as a candidate repurposed therapeutic agent for COVID-19 and its potential anti-SARS-CoV-2 activity. Frontiers in Microbiology, section Virology, doi:10.3389/fmicb.2022.828984.
Morris, G. M., Goodsell, D. S., Halliday, R. S., et al. (1998). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry, v. 19, n. 14, p. 1639–1662.
Morris, G. M., Huey, R., Lindstrom, W., et al. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry, v. 30, n. 16, p. 2785–2791.
Mysinger, M. M., Carchia, M., Irwin, J. J. and Shoichet, B. K. (2012). Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. Journal of medicinal chemistry, v. 55, n. 14, p. 6582–6594.
Norgan, A. P., Coffman, P. K., Kocher, J.-P. A., Katzmann, D. J. and Sosa, C. P. (2011). Multilevel parallelization of AutoDock 4.2. Journal of cheminformatics, v. 3, n. 1, p. 12.
O'Boyle, N. M., Banck, M., James, C. A., et al. (2011). Open Babel: An open chemical toolbox. Journal of cheminformatics, v. 3, n. 1, p. 1–14.
Perola, E. (2006). Minimizing false positives in kinase virtual screens. Proteins: Structure, Function, and Bioinformatics, v. 64, n. 2, p. 422–435.
Ren, J., Yuan, X., Li, J., et al. (2020). Assessing the performance of the g_mmpbsa tools to simulate the inhibition of oseltamivir to influenza virus neuraminidase by molecular mechanics Poisson--Boltzmann surface area methods. Journal of the Chinese Chemical Society, v. 67, n. 1, p. 46–53.
Sinko, W., Lindert, S. and McCammon, J. A. (2013). Accounting for Receptor Flexibility and Enhanced Sampling Methods in Computer-Aided Drug Design. Chemical biology & drug design, v. 81, n. 1, p. 41–49.
Song, L. F., Lee, T., Zhu, C., York, D. M. and Merz, K. M. (2019). Using AMBER18 for Relative Free Energy Calculations. Journal of Chemical Information and Modeling,
Sterling, T. and Irwin, J. J. (nov 2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, v. 55, n. 11, p. 2324–2337.
Truchon, J.-F. and Bayly, C. I. (2007). Evaluating virtual screening methods: good and bad metrics for the ‘early recognition’ problem. Journal of chemical information and modeling, v. 47, n. 2, p. 488–508.
Wang, C., Greene, D., Xiao, L., Qi, R. and Luo, R. (2018). Recent Developments and Applications of the MMPBSA Method. Frontiers in Molecular Biosciences, v. 4, p. 87.
Wang, E., Sun, H., Wang, J., et al. (2019). End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical Reviews, v. 119, n. 16, p. 9478–9508.
Wang, Z., Sun, H., Shen, C., et al. (2020). Combined strategies in structure-based virtual screening. Physical Chemistry Chemical Physics, v. 22, n. 6, p. 3149–3159.
Wisnasari, S., Rohman, M. S. and Lukitasari, M. (2016). In silico binding affinity study of lisinopril and captopril to I/D intron 16 variant of angiotensin converting enzyme protein. International Journal of Pharmaceutical and Clinical Research, v. 8, n. 8, p. 1132–1134.