Abstract
Recently, a new piece of software called OptiPharm has been proposed to optimize the similarity between two given molecules. A comprehensive study proved it was very competitive compared with state-of-the-art algorithms such as WEGA and ROCS. However, all of these methods, including OptiPharm, assume the proteins as rigid, resulting in poor or inefficient predictions. The consideration of conformational changes and thus the molecule’s flexibility is necessary. In this work, we have extended the OptiPharm’s functionality by applying a methodology that considers the flexibility of the molecules. Apart from that, the new OptiPharm presents some strengths regarding its previous version. More precisely, it reduces the search space dimension and introduces circular limits for the angular variables to enhance searchability. As results will show, these improvements help OptiPharm to achieve better predictions.
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References
Ain, Q.U., Aleksandrova, A., Roessler, F.D., Ballester, P.J.: Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci. 5(6), 405–424 (2015)
Axenopoulos, A., Rafailidis, D., Papadopoulos, G., Houstis, E.N., Daras, P.: Similarity search of flexible 3D molecules combining local and global shape descriptors. IEEE/ACM Trans. Comput. Biol. Bioinf. 13(5), 954–970 (2016)
Cano-Muñoz, M., Jurado, S., Morel, B., Conejero-Lara, F.: Conformational flexibility of the conserved hydrophobic pocket of HIV-1 gp41. Implications for the discovery of small-molecule fusion inhibitors. Int. J. Biol. Macromol. 192, 90–99 (2021)
Carlson, H.A.: Protein flexibility and drug design: how to hit a moving target. Curr. Opin. Chem. Biol. 6(4), 447–452 (2002)
Debnath, S., Debnath, T., Majumdar, S., Arunasree, M.K., Aparna, V.: A combined pharmacophore modeling, 3D QSAR, virtual screening, molecular docking, and ADME studies to identify potential HDAC8 inhibitors. Med. Chem. Res. 25(11), 2434–2450 (2016). https://doi.org/10.1007/s00044-016-1652-5
Ganesan, A., Coote, M.L., Barakat, K.: Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug Discov. Today 22(2), 249–269 (2017)
Ge, H., Wang, Y., Zhao, W., Lin, W., Yan, X., Xu, J.: Scaffold hopping of potential anti-tumor agents by WEGA: a shape-based approach. Med. Chem. Commun. 5(6), 737–741 (2014)
Han, R., Zhang, F., Wan, X., Fernández, J.J., Sun, F., Liu, Z.: A marker-free automatic alignment method based on scale-invariant features. J. Struct. Biol. 186(1), 167–180 (2014)
Hu, J., Liu, Z., Yu, D.J., Zhang, Y.: LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening. In: Bioinformatics, vol. 34, pp. 2209–2218. Oxford University Press (2018)
Kalászi, A., Szisz, D., Imre, G., Polgár, T.: Screen3D: a novel fully flexible high-throughput shape-similarity search method. J. Chem. Inf. Model. 54(4), 1036–1049 (2014)
Leelananda, S.P., Lindert, S.: Computational methods in drug discovery. Beilstein J. Org. Chem. 12, 2694–2718 (2016)
Lipinski, C.A.: Rule of five in 2015 and beyond: target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv. Drug Deliv. Rev. 101, 34–41 (2016)
OMEGA 4.1.0.2: OpenEye Scientific Software: Santa Fe, NM, USA (2019). http://www.eyesopen.com
Puertas-Martín, S., Redondo, J.L., Ortigosa, P.M., Pérez-Sánchez, H.: OptiPharm: an evolutionary algorithm to compare shape similarity. Sci. Rep. 9(1), 1398 (2019)
ROCS: OpenEye Scientific Software: Santa Fe, NM. http://www.eyesopen.com
Rogers, D.J., Tanimoto, T.T.: A computer program for classifying plants. Science 132(3434), 1115–1118 (1960)
Seidel, T., Bryant, S.D., Ibis, G., Poli, G., Langer, T.: 3D pharmacophore modeling techniques in computer-aided molecular design using LigandScout. Wiley (2017)
Selvaraj, C., et al.: Microsecond MD simulation and multiple-conformation virtual screening to identify potential anti-COVID-19 inhibitors against SARS-CoV-2 main protease. Front. Chem. 8, 1–15, 595273 (2021). https://doi.org/10.3389/fchem.2020.595273
Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327 (1977)
Yan, X., Li, J., Liu, Z., Zheng, M., Ge, H., Xu, J.: Enhancing molecular shape comparison by weighted gaussian functions. J. Chem. Inf. Model. 53(8), 1967–1978 (2013)
Yan, X., Liao, C., Liu, Z., Hagler, A.T., Gu, Q., Xu, J.: Chemical structure similarity search for ligand-based virtual screening: methods and computational resources. Curr. Drug Targets 17(14), 1580–1585 (2016)
Yuriev, E., Ramsland, P.A.: Latest developments in molecular docking: 2010–2011 in review. J. Mol. Recogn. 26(5), 215–239 (2013)
Acknowledgments
This work was supported by the Spanish Ministry of Economy and Competitiveness through the CTQ2017-87974-R, RTI2018-095993-B-I00 and EQC2019-006418-P grants; by the Junta de Andalucía through the grant Proyectos de excelencia (P18-RT-1193), by the Programa Regional de Fomento de la Investigación (Plan de Actuación 2018, Región de Murcia, Spain) through the: ”Ayudas a la realización de proyectos para el desarrollo de investigación científica y técnica por grupos competitivos (20988/PI/18)” grant; by the University of Almeria throught the grant: Ayudas a proyectos de investigación I+D+I en el marco del Programa Operativo FEDER 2014-20” (UAL18-TIC-A020-B). Savíns Puertas Martín is a fellow of the ‘Margarita Salas’ grant (RR_A_2021_21), financed by the European Union (NextGenerationEU).
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Puertas-Martín, S., Redondo, J.L., Garzón, E.M., Pérez-Sánchez, H., Ortigosa, P.M. (2022). Increasing the Accuracy of Optipharm’s Virtual Screening Predictions by Implementing Molecular Flexibility. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_20
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