Abstract
In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the chemical space is newer, later 1990s. With the progress of computational methodologies to create machine-learning models to predict the ligand-binding affinity, clearly there is a need for novel approaches to the problem of protein-ligand interactions. New abstractions are required to guide the conceptual analysis of the molecular recognition problem. Using a systems approach, we proposed to address protein-ligand scoring functions using the modern idea of the scoring function space. In this chapter, we describe the fundamental concept behind the scoring function space and how it has been applied to develop the new generation of targeted-scoring functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL et al (2016) SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb Chem High Throughput Screen 19:801–812
de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF (2017) Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun 494:305–310
Azevedo LS, Moraes FP, Xavier MM, Pantoja EO, Villavicencio B, Finck JÁ et al (2012) Recent progress of molecular docking simulations applied to development of drugs. Curr Bioinforma 7:352–365
Bitencourt-Ferreira G, de Azevedo WF Jr (2018) Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem 240:63–69
Jiménez J, Škalič M, Martínez-Rosell G, De Fabritiis G (2018) KDEEP: protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J Chem Inf Model 58:287–296
de Ávila MB, de Azevedo WF Jr (2018) Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des 92:1468–1474
Amaral MEA, Nery LR, Leite CE, de Azevedo Junior WF, Campos MM (2018) Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Investig New Drugs 36:782–796
Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo WF Jr (2018) Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem 235:1–8
Freitas PG, Elias TC, Pinto IA, Costa LT, de Carvalho PVSD, Omote DQ et al (2018) Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein. Lett Drug Des Discov 15:488–499
Pintro VO, Azevedo WF (2017) Optimized virtual screening workflow. Towards target-based polynomial scoring functions for HIV-1 protease. Comb Chem High Throughput Screen 20:820–827
de Ávila MB, Bitencourt-Ferreira G, de Azevedo WF Jr (2019) Structural basis for inhibition of enoyl-[acyl carrier protein] reductase (InhA) from Mycobacterium tuberculosis. Curr Med Chem. https://doi.org/10.2174/0929867326666181203125229
Volkart PA, Bitencourt-Ferreira G, Souto AA, de Azevedo WF (2019) Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr Drug Targets 20(7):716–726. https://doi.org/10.2174/1389450120666181204165344
Russo S, De Azevedo WF (2019) Advances in the understanding of the cannabinoid receptor 1 - focusing on the inverse agonists interactions. Curr Med Chem. https://doi.org/10.2174/0929867325666180417165247
Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF (2017) Supervised machine learning methods applied to predict ligand-binding affinity. Curr Med Chem 24:2459–2470
Smith JM (1970) Natural selection and the concept of a protein space. Nature 225:563–564
Hou J, Jun SR, Zhang C, Kim SH (2005) Global mapping of the protein structure space and application in structure-based inference of protein function. Proc Natl Acad Sci U S A 102:3651–3656
Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16:3–50
Dobson CM (2004) Chemical space and biology. Nature 432:824–828
Kirkpatrick P, Ellis C (2004) Chemical space. Nature 432:823
Lipinski C, Hopkins A (2004) Navigating chemical space for biology and medicine. Nature 432:855–861
Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865
Stockwell BR (2004) Exploring biology with small organic molecules. Nature 432:846–854
Dias R, Timmers LF, Caceres RA, de Azevedo WF Jr (2008) Evaluation of molecular docking using polynomial empirical scoring functions. Curr Drug Targets 9:1062–1070
de Azevedo WF Jr, Dias R (2008) Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorg Med Chem 16:9378–9382
Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321
Heberlé G, de Azevedo WF Jr (2011) Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem 18:1339–1352
De Azevedo WF Jr (2010) MolDock applied to structure-based virtual screening. Curr Drug Targets 11:327–334
Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202
Morris GM, Goodsell DS, Huey R, Olson AJ (1996) Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4. J Comput Aided Mol Des 10:293–304
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK et al (1998) Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J Comput Chem 19:1639–1662
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461
Zar JH (1972) Significance testing of the Spearman rank correlation coefficient. J Am Stat Assoc 67:578–580
Acknowledgments
This work was supported by grants from CNPq (Brazil) (308883/2014-4). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001. GB-F acknowledges support from PUCRS/BPA fellowship. WFA is a senior researcher for CNPq (Brazil) (Process Numbers: 308883/2014-4 and 309029/2018-0).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Bitencourt-Ferreira, G., de Azevedo, W.F. (2019). Exploring the Scoring Function Space. In: de Azevedo Jr., W. (eds) Docking Screens for Drug Discovery. Methods in Molecular Biology, vol 2053. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9752-7_17
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9752-7_17
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9751-0
Online ISBN: 978-1-4939-9752-7
eBook Packages: Springer Protocols