5,114
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

Fragmented blind docking: a novel protein–ligand binding prediction protocol

ORCID Icon, , ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon show all
Pages 13472-13481 | Received 02 Mar 2021, Accepted 28 Sep 2021, Published online: 12 Oct 2021

References

  • Agrawal, P., Singh, H., Srivastava, H. K., Singh, S., Kishore, G., & Raghava, G. P. S. (2019). Benchmarking of different molecular docking methods for protein–peptide docking. BMC Bioinformatics, 19(S13), 426. https://doi.org/10.1186/s12859-018-2449-y
  • Ahmed, L., Rasulev, B., Turabekova, M., Leszczynska, D., & Leszczynski, J. (2013). Receptor- and ligand-based study of fullerene analogues: Comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations. Organic & Biomolecular Chemistry, 11(35), 5798–5808. https://doi.org/10.1039/c3ob40878g
  • Al-Sha'er, M. A., & Taha, M. O. (2012). Application of docking-based comparative intermolecular contacts analysis to validate Hsp90α docking studies and subsequent in silico screening for inhibitors. Journal of Molecular Modeling, 18(11), 4843–4863. https://doi.org/10.1007/s00894-012-1479-z
  • Aparoy, P., Kumar Reddy, K., & Reddanna, P. (2012). Structure and ligand based drug design strategies in the development of Novel 5- LOX inhibitors. Current Medicinal Chemistry, 19(22), 3763–3778. https://doi.org/10.2174/092986712801661112
  • Bacilieri, M., & Moro, S. (2006). Ligand-based drug design methodologies in drug discovery process: An overview. Current Drug Discovery Technologies, 3(3), 155–165. https://doi.org/10.2174/157016306780136781
  • Batool, M., Ahmad, B., & Choi, S. (2019). A structure-based drug discovery paradigm. International Journal of Molecular Sciences, 20(11), 2783. https://doi.org/10.3390/ijms20112783
  • Ben-Shimon, A., & Niv, M. Y. (2015). AnchorDock: Blind and flexible anchor-driven peptide docking. Structure (London, England : 1993), 23(5), 929–940. https://doi.org/10.1016/j.str.2015.03.010
  • Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
  • Blaszczyk, M., Kurcinski, M., Kouza, M., Wieteska, L., Debinski, A., Kolinski, A., & Kmiecik, S. (2016). Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods (San Diego, Calif.), 93, 72–83. https://doi.org/10.1016/j.ymeth.2015.07.004
  • Brik, A., & Wong, C. H. (2003). HIV-1 protease: Mechanism and drug discovery. Organic & Biomolecular Chemistry, 1(1), 5–14. https://doi.org/10.1039/b208248a
  • Brown, W. M., & Jagt, D. L. V. (2004). Creating artificial binding pocket boundaries to improve the efficiency of flexible ligand docking. Journal of Chemical Information and Computer Sciences, 44(4), 1412–1422. https://doi.org/10.1021/ci049853r
  • Castro-Alvarez, A., Costa, A. M., & Vilarrasa, J. (2017). The Performance of several docking programs at reproducing protein–macrolide-like crystal structures. Molecules, 22(1), 136. https://doi.org/10.3390/molecules22010136
  • Cavasotto, C. N., & Abagyan, R. A. (2004). Protein flexibility in ligand docking and virtual screening to protein kinases. Journal of Molecular Biology, 337(1), 209–225. https://doi.org/10.1016/j.jmb.2004.01.003
  • Chang, M. W., Ayeni, C., Breuer, S., & Torbett, B. E. (2010). Virtual screening for HIV protease inhibitors: A comparison of AutoDock 4 and Vina. PLoS One, 5(8), e11955. https://doi.org/10.1371/journal.pone.0011955
  • Chen, F., Liu, H., Sun, H., Pan, P., Li, Y., Li, D., & Hou, T. (2016). Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Physical Chemistry Chemical Physics: PCCP, 18(32), 22129–22139. https://doi.org/10.1039/c6cp03670h
  • Cole, J. C., Murray, C. W., Nissink, J. W. M., Taylor, R. D., & Taylor, R. (2005). Comparing protein–ligand docking programs is difficult. Proteins, 60(3), 325–332. https://doi.org/10.1002/prot.20497
  • Deeks, S. G., Smith, M., Holodniy, M., & Kahn, J. O. (1997). HIV-1 protease inhibitors. A review for clinicians. JAMA, 277(2), 145–153. https://doi.org/10.1001/jama.277.2.145
  • Du, X., Li, Y., Xia, Y. L., Ai, S. M., Liang, J., Sang, P., Ji, X. L., & Liu, S. Q. (2016). Insights into protein–ligand interactions: Mechanisms, models, and methods. International Journal of Molecular Sciences, 17(2), 144. https://doi.org/10.3390/ijms17020144
  • El Khoury, L., Santos-Martins, D., Sasmal, S., Eberhardt, J., Bianco, G., Ambrosio, F. A., Solis-Vasquez, L., Koch, A., Forli, S., & Mobley, D. L. (2019). Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4. Journal of Computer-Aided Molecular Design, 33(12), 1011–1020. https://doi.org/10.1007/s10822-019-00240-w
  • Ferreira, L., dos Santos, R., Oliva, G., & Andricopulo, A. (2015). Molecular docking and structure-based drug design strategies. Molecules (Basel, Switzerland), 20(7), 13384–13421. https://doi.org/10.3390/molecules200713384
  • Gabb, H. A., Jackson, R. M., & Sternberg, M. J. E. (1997). Modelling protein docking using shape complementarity, electrostatics and biochemical information. Journal of Molecular Biology, 272(1), 106–120. https://doi.org/10.1006/jmbi.1997.1203
  • Gaillard, T. (2018). Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark. Journal of Chemical Information and Modeling, 58(8), 1697–1706. https://doi.org/10.1021/acs.jcim.8b00312
  • Garrido, C., Gurbuxani, S., Ravagnan, L., & Kroemer, G. (2001). Heat shock proteins: Endogenous modulators of apoptotic cell death. Biochemical and Biophysical Research Communications, 286(3), 433–442. https://doi.org/10.1006/bbrc.2001.5427
  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
  • Ghersi, D., & Sanchez, R. (2009). Improving accuracy and efficiency of blind protein–ligand docking by focusing on predicted binding sites. Proteins, 74(2), 417–424. https://doi.org/10.1002/prot.22154
  • Graves, A. P., Shivakumar, D. M., Boyce, S. E., Jacobson, M. P., Case, D. A., & Shoichet, B. K. (2008). Rescoring docking hit lists for model cavity sites: Predictions and experimental testing. Journal of Molecular Biology, 377(3), 914–934. https://doi.org/10.1016/j.jmb.2008.01.049
  • Grosdidier, A., Zoete, V., & Michielin, O. (2009). Blind docking of 260 protein–ligand complexes with EADock 2.0. Journal of Computational Chemistry, 30(13), 2021–2030. https://doi.org/10.1002/jcc.21202
  • Gupta, M., Sharma, R., & Kumar, A. (2018). Docking techniques in pharmacology: How much promising? Computational Biology and Chemistry, 76, 210–217. https://doi.org/10.1016/j.compbiolchem.2018.06.005
  • Hassan, N. M., Alhossary, A. A., Mu, Y., & Kwoh, C. K. (2017). Protein–ligand blind docking using QuickVina-W with inter-process spatio-temporal integration. Scientific Reports, 7(1), 15451. https://doi.org/10.1038/s41598-017-15571-7
  • Hernandez, M., Ghersi, D., & Sanchez, R. (2009). SITEHOUND-web: A server for ligand binding site identification in protein structures. Nucleic Acids Research, 37(Web Server issue), W413–W416. https://doi.org/10.1093/nar/gkp281
  • Hernndez-Santoyo, A., Yair, A., Altuzar, V., Vivanco-Cid, H., & Mendoza-Barrer, C. (2013). Protein–protein and protein–ligand docking. In Protein engineering – Technology and application. InTech. https://doi.org/10.5772/56376
  • Hetényi, C., & van der Spoel, D. (2002). Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Science, 11(7), 1729–1737. https://doi.org/10.1110/ps.0202302
  • Hetényi, C., & Van Der Spoel, D. (2006). Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Letters, 580(5), 1447–1450. https://doi.org/10.1016/j.febslet.2006.01.074
  • Hetényi, C., & Van Der Spoel, D. (2011). Toward prediction of functional protein pockets using blind docking and pocket search algorithms. Protein Science, 20(5), 880–893. https://doi.org/10.1002/pro.618
  • Hu, X., Balaz, S., & Shelver, W. H. (2004). A practical approach to docking of zinc metalloproteinase inhibitors. Journal of Molecular Graphics & Modelling, 22(4), 293–307. https://doi.org/10.1016/j.jmgm.2003.11.002
  • Hughes, J., Rees, S., Kalindjian, S., & Philpott, K. (2011). Principles of early drug discovery. British Journal of Pharmacology, 162(6), 1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x
  • Imbernón, B., Serrano, A., Bueno-Crespo, A., Abellán, J. L., Pérez-Sánchez, H., & Cecilia, J. M. (2021). METADOCK 2: A high-throughput parallel metaheuristic scheme for molecular docking. Bioinformatics (Oxford, England), 37(11), 1515–1520. https://doi.org/10.1093/bioinformatics/btz958
  • Kairys, V., Baranauskiene, L., Kazlauskiene, M., Matulis, D., & Kazlauskas, E. (2019). Binding affinity in drug design: Experimental and computational techniques. Expert Opinion on Drug Discovery, 14(8), 755–768. https://doi.org/10.1080/17460441.2019.1623202
  • Lape, M., Elam, C., & Paula, S. (2010). Comparison of current docking tools for the simulation of inhibitor binding by the transmembrane domain of the sarco/endoplasmic reticulum calcium ATPase. Biophysical Chemistry, 150(1–3), 88–97. https://doi.org/10.1016/j.bpc.2010.01.011
  • Li, J., Fu, A., & Zhang, L. (2019). An overview of scoring functions used for protein–ligand interactions in molecular docking. Interdisciplinary Sciences, Computational Life Sciences, 11(2), 320–328. https://doi.org/10.1007/s12539-019-00327-w
  • Lin, Y. F., Cheng, C. W., Shih, C. S., Hwang, J. K., Yu, C. S., & Lu, C. H. (2016). MIB: Metal ion-binding site prediction and docking server. Journal of Chemical Information and Modeling, 56(12), 2287–2291. https://doi.org/10.1021/acs.jcim.6b00407
  • Liu, Y., Grimm, M., Dai, W., Tao, Hou, M., Chun, Xiao, Z. X., & Cao, Y. (2020). CB-Dock: A web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacologica Sinica, 41(1), 138–144. https://doi.org/10.1038/s41401-019-0228-6
  • Liu, Z., Li, Y., Han, L., Li, J., Liu, J., Zhao, Z., Nie, W., Liu, Y., & Wang, R. (2015). PDB-wide collection of binding data: Current status of the PDBbind database. Bioinformatics (Oxford, England), 31(3), 405–412. https://doi.org/10.1093/bioinformatics/btu626
  • Lyne, P. D., Lamb, M. L., & Saeh, J. C. (2006). Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. Journal of Medicinal Chemistry, 49(16), 4805–4808. https://doi.org/10.1021/jm060522a
  • Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015). Role of computer-aided drug design in modern drug discovery. Archives of Pharmacal Research, 38(9), 1686–1701. https://doi.org/10.1007/s12272-015-0640-5
  • Mey, A. S. J. S., Juárez-Jiménez, J., Hennessy, A., & Michel, J. (2016). Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge. Bioorganic & Medicinal Chemistry, 24(20), 4890–4899. https://doi.org/10.1016/j.bmc.2016.07.044
  • Mobaraki, N., Hemmateenejad, B., Weikl, T. R., & Sakhteman, A. (2019). On the relationship between docking scores and protein conformational changes in HIV-1 protease. Journal of Molecular Graphics & Modelling, 91, 186–193. https://doi.org/10.1016/j.jmgm.2019.06.011
  • Mobley, D. L., Liu, S., Lim, N. M., Wymer, K. L., Perryman, A. L., Forli, S., Deng, N., Su, J., Branson, K., & Olson, A. J. (2014). Blind prediction of HIV integrase binding from the SAMPL4 challenge. Journal of Computer-Aided Molecular Design, 28(4), 327–345. https://doi.org/10.1007/s10822-014-9723-5
  • Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Morris, G. M., Huey, R., & Olson, A. J. (2008). UNIT using AutoDock for ligand-receptor docking. Current Protocols in Bioinformatics, 24(1), 8–14. https://doi.org/10.1002/0471250953.bi0814s24
  • Muscat, S., Pallante, L., Stojceski, F., Danani, A., Grasso, G., & Deriu, M. A. (2020). The impact of natural compounds on S-shaped Aβ42 fibril: From molecular docking to biophysical characterization. International Journal of Molecular Sciences, 21(6), 2017. https://doi.org/10.3390/ijms21062017
  • Nguyen, H., Roe, D. R., & Simmerling, C. (2013). Improved generalized born solvent model parameters for protein simulations. Journal of Chemical Theory and Computation, 9(4), 2020–2034. https://doi.org/10.1021/ct3010485
  • Nissink, J. W. M., Murray, C., Hartshorn, M., Verdonk, M. L., Cole, J. C., & Taylor, R. (2002). A new test set for validating predictions of protein–ligand interaction. Proteins, 49(4), 457–471. https://doi.org/10.1002/prot.10232
  • Novič, M., Tibaut, T., Anderluh, M., Borišek, J., & Tomašič, T. (2016). The comparison of docking search algorithms and scoring functions: An overview and case studies. In S. Dastmalchi, M. Hamzeh-Mivehroud, & B. Sokouti (Eds.), Methods and algorithms for molecular docking-based drug design and discovery (pp. 99–127). IGI Global. https://doi.org/10.4018/978-1-5225-0115-2.ch004
  • Palacio-Rodríguez, K., Lans, I., Cavasotto, C. N., & Cossio, P. (2019). Exponential consensus ranking improves the outcome in docking and receptor ensemble docking. Scientific Reports, 9(1), 5142. https://doi.org/10.1038/s41598-019-41594-3
  • Penkler, D. L., & Tastan Bishop, Ö. (2019). Modulation of Human Hsp90α Conformational Dynamics by Allosteric Ligand Interaction at the C-Terminal Domain. Scientific Reports, 9(1), 1600. https://doi.org/10.1038/s41598-018-35835-0
  • Preto, J., Gentile, F., Winter, P., Churchill, C., Omar, S. I., & Tuszynski, J. A. (2018). Molecular dynamics and related computational methods with applications to drug discovery. In L. Bonilla, E. Kaxiras, R. Melnik (Eds.), Coupled Mathematical Models for Physical and Biological Nanoscale Systems and Their Applications. BIRS-16w5069 2016. Springer Proceedings in Mathematics and Statistics, vol. 232 (pp. 267–285). Springer. https://doi.org/10.1007/978-3-319-76599-0_14
  • Prieto-Martínez, F. D., Arciniega, M., & Medina-Franco, J. L. (2018). Acoplamiento molecular: Avances recientes y retos. TIP Revista Especializada en Ciencias Químico-Biológicas, 21. https://doi.org/10.22201/fesz.23958723e.2018.0.143
  • Prodromou, C., & Pearl, L. (2003). Structure and functional relationships of Hsp90. Current Cancer Drug Targets, 3(5), 301–323. https://doi.org/10.2174/1568009033481877
  • Prodromou, C., Roe, S. M., Piper, P. W., & Pearl, L. H. (1997). A molecular clamp in the crystal structure of the N-terminal domain of the yeast Hsp90 chaperone. Nature Structural Biology, 4(6), 477–482. https://doi.org/10.1038/nsb0697-477
  • Quiroga, R., & Villarreal, M. A. (2016). Vinardo: A scoring function based on Autodock Vina improves scoring, docking, and virtual screening. PLoS One, 11(5), e0155183. https://doi.org/10.1371/journal.pone.0155183
  • Rastelli, G., Del Rio, A., Degliesposti, G., & Sgobba, M. (2010). Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. Journal of Computational Chemistry, 31(4), 797–810. https://doi.org/10.1002/jcc.21372
  • Rastelli, G., & Pinzi, L. (2019). Refinement and rescoring of virtual screening results. Frontiers in Chemistry, 7, 498. https://doi.org/10.3389/fchem.2019.00498
  • Salomon-Ferrer, R., Case, D. A., & Walker, R. C. (2013). An overview of the Amber biomolecular simulation package. Wiley Interdisciplinary Reviews: Computational Molecular Science, 3(2), 198–210. https://doi.org/10.1002/wcms.1121
  • Sanner, M. F. (1999). Python: A programming language for software integration and development. Journal of Molecular Graphics & Modelling, 17(1), 57–61.
  • Seeliger, D., & De Groot, B. L. (2010). Ligand docking and binding site analysis with PyMOL and Autodock/Vina. Journal of Computer-Aided Molecular Design, 24(5), 417–422. https://doi.org/10.1007/s10822-010-9352-6
  • Sgobba, M., Caporuscio, F., Anighoro, A., Portioli, C., & Rastelli, G. (2012). Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. European Journal of Medicinal Chemistry, 58, 431–440. https://doi.org/10.1016/j.ejmech.2012.10.024
  • Śledź, P., & Caflisch, A. (2018). Protein structure-based drug design: From docking to molecular dynamics. Current Opinion in Structural Biology, 48, 93–102. https://doi.org/10.1016/j.sbi.2017.10.010
  • Su, P.-C., Tsai, C.-C., Mehboob, S., Hevener, K. E., & Johnson, M. E. (2015). Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F. tularensis enoyl-ACP reductase (FabI). Journal of Computational Chemistry, 36(25), 1859–1873. https://doi.org/10.1002/jcc.24011
  • Sun, H., Li, Y., Shen, M., Tian, S., Xu, L., Pan, P., Guan, Y., & Hou, T. (2014). Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Physical Chemistry Chemical Physics, 16(40), 22035–22045. https://doi.org/10.1039/c4cp03179b
  • Sun, H., Li, Y., Tian, S., Xu, L., & Hou, T. (2014). Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Physical Chemistry Chemical Physics, 16(31), 16719–16729. https://doi.org/10.1039/c4cp01388c
  • Surabhi, S., & Singh, B. (2018). Computer aided drug design: An overview. Journal of Drug Delivery and Therapeutics, 8(5), 504–509. https://doi.org/10.22270/jddt.v8i5.1894
  • Thompson, D. C., Humblet, C., & Joseph-McCarthy, D. (2008). Investigation of MM-PBSA rescoring of docking poses. Journal of Chemical Information and Modeling, 48(5), 1081–1091. https://doi.org/10.1021/ci700470c
  • Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
  • Veselovsky, A. V., & Ivanov, A. S. (2003). Strategy of computer-aided drug design. Current Drug Targets. Infectious Disorders, 3(1), 33–40. https://doi.org/10.2174/1568005033342145
  • Vuignier, K., Schappler, J., Veuthey, J. L., Carrupt, P. A., & Martel, S. (2010). Drug-protein binding: A critical review of analytical tools. Analytical and Bioanalytical Chemistry, 398(1), 53–66. https://doi.org/10.1007/s00216-010-3737-1
  • Wang, R., Fang, X., Lu, Y., Yang, C.-Y., & Wang, S. (2005). The PDBbind database: Methodologies and updates. Journal of Medicinal Chemistry, 48(12), 4111–4119. https://doi.org/10.1021/jm048957q
  • Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z. H., & Hou, T. (2019). End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chemical Reviews, 119(16), 9478–9508. https://doi.org/10.1021/acs.chemrev.9b00055
  • Wang, Z., Wang, X., Li, Y., Lei, T., Wang, E., Li, D., Kang, Y., Zhu, F., & Hou, T. (2019). farPPI: A webserver for accurate prediction of protein–ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods. Bioinformatics (Oxford, England), 35(10), 1777–1779. https://doi.org/10.1093/bioinformatics/bty879
  • Webb, B., & Sali, A. (2016). Comparative protein structure modeling using MODELLER. Current Protocols in Bioinformatics, 54(1), 5.6.1–5.6.37. https://doi.org/10.1002/cpbi.3
  • Weng, G., Wang, E., Wang, Z., Liu, H., Zhu, F., Li, D., & Hou, T. (2019). HawkDock: A web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA. Nucleic Acids Research, 47(W1), W322–W330. https://doi.org/10.1093/nar/gkz397
  • Yang, J., Roy, A., & Zhang, Y. (2013). Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 29(20), 2588–2595. https://doi.org/10.1093/bioinformatics/btt447
  • Zaheer-Ul-Haq, Halim, S. A., Uddin, R., & Madura, J. D. (2010). Benchmarking docking and scoring protocol for the identification of potential acetylcholinesterase inhibitors. Journal of Molecular Graphics and Modelling, 28(8), 870–882. https://doi.org/10.1016/j.jmgm.2010.03.007
  • Zhang, X., Perez-Sanchez, H., C., & Lightstone, F. (2017). A comprehensive docking and MM/GBSA rescoring study of ligand recognition upon binding antithrombin. Current Topics in Medicinal Chemistry, 17(14), 1631–1639. https://doi.org/10.2174/1568026616666161117112604