Abstract
Comparative Binding Energy (COMBINE) analysis is an approach for deriving a target-specific scoring function to compute binding free energy, drug-binding kinetics, or a related property by exploiting the information contained in the three-dimensional structures of receptor–ligand complexes. Here, we describe the process of setting up and running COMBINE analysis to derive a Quantitative Structure-Kinetics Relationship (QSKR) for the dissociation rate constants (koff) of inhibitors of a drug target. The derived QSKR model can be used to estimate residence times (τ, τ=1/koff) for similar inhibitors binding to the same target, and it can also help to identify key receptor–ligand interactions that distinguish inhibitors with short and long residence times. Herein, we demonstrate the protocol for the application of COMBINE analysis on a dataset of 70 inhibitors of heat shock protein 90 (HSP90) belonging to 11 different chemical classes. The procedure is generally applicable to any drug target with known structural information on its complexes with inhibitors.
Key words
- Comparative binding energy (COMBINE) analysis
- Dissociation rate constant
- Drug-protein binding kinetics
- Quantitative structure-kinetics relationship (QSKR)
- Residence time
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References
Copeland RA, Pompliano DL, Meek TD (2006) Drug–target residence time and its implications for lead optimization. Nat Rev Drug Discov 5:730–739
Bruce NJ, Ganotra GK, Kokh DB et al (2018) New approaches for computing ligand–receptor binding kinetics. Curr Opin Struct Biol 49:1–10
Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967
Cruciani G, Watson KA (1994) Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen Phosphorylase b. J Med Chem 37:2589–2601
Ortiz AR, Pastor M, Palomer A et al (1997) Reliability of comparative molecular field analysis models : effects of data scaling and variable selection using a set of human synovial fluid. J Med Chem 2623:1136–1148
Ganotra GK, Wade RC (2018) Prediction of drug-target binding kinetics by comparative binding energy analysis. ACS Med Chem Lett 9:1134–1139
Ortiz AR, Pisabarro MT, Gago F et al (1995) Prediction of drug binding affinities by comparative binding energy analysis. J Med Chem 38:2681–2691
Murcia M, Ortiz AR (2004) Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors. J Med Chem 47:805–820
Peón A, Coderch C, Gago F et al (2013) Comparative binding energy (COMBINE) analysis for understanding the binding determinants of TypeII Dehydroquinase inhibitors. ChemMedChem 8:740–747
Nakamura S, Nakanishi I, Kitaura K (2006) Binding affinity prediction of non-peptide inhibitors of HIV-1 protease using COMBINE model introduced from peptide inhibitors. Bioorganic Med Chem Lett 16:6334–6337
Liu S, Fu R, Cheng X et al (2012) Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE). BMC Struct Biol 12:21
Peters MB, Merz KM (2006) Semiempirical comparative binding energy analysis (SE-COMBINE) of a series of trypsin inhibitors. J Chem Theory Comput 2:383–399
Tomic S, Nilsson L, Wade RC (2000) Nuclear receptor-DNA binding specificity: a COMBINE and free-Wilson QSAR analysis. J Med Chem 43:1780–1792
Wang T, Wade RC (2002) Comparative binding energy (COMBINE) analysis of OppA-peptide complexes to relate structure to binding thermodynamics. J Med Chem 45:4828–4837
Nakamura S, Ohmura R, Nakanishi I (2017) An interaction-based approach for affinity prediction between antigen peptide and human leukocyte antigen using COMBINE analysis. Chem-Bio Informatics J 17:93–102
Tomić S, Bertoša B, Wang T et al (2007) COMBINE analysis of the specificity of binding of Ras proteins to their effectors. Proteins Struct Funct Genet 67:435–447
Rännar S, Lindgren F, Geladi P et al (1994) A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: theory and algorithm. J Chemom 8:111–125
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130
Box GEP, Hunter JS, Hunter WG (2005) Statistics for experimenters: design, innovation, and discovery. Wiley-Interscience, New York
Åqvist J, Medina C, Samuelsson JE (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng Des Sel 7:385–391
Nunes-Alves A, Arantes GM (2014) Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging. J Chem Inf Model 54:2309–2319
Bruce NJ, Ganotra GK, Richter S et al (2019) KBbox: a toolbox of computational methods for studying the kinetics of molecular binding. J Chem Inf Model 59:3630–3634
Case DA, Babin V, Berryman JT et al (2014) AMBER14. University of California, San Francisco, CA
Gil-Redondo R, Klett J, Gago F et al (2010) gCOMBINE: a graphical user interface to perform structure-based comparative binding energy (COMBINE) analysis on a set of ligand-receptor complexes. Proteins Struct Funct Bioinforma 78:162–172
Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713
Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a General Amber force field. J Comput Chem 25:1157–1174
Ganotra G, Wade RC (2020) Dataset to perform COMBINE analysis using HSP90. https://doi.org/10.5281/ZENODO.3674994
Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276
Dolinsky TJ, Nielsen JE, McCammon JA et al (2004) PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res 32:W665–W667
Dolinsky TJ, Czodrowski P, Li H et al (2007) PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Res 35:W522–W525
Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8:52–56
Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF chimera - a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612
O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3(33)
Bayly CI, Cieplak P, Cornell WD et al (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97:10269–10280
Acknowledgments
This work was supported by the EU/EFPIA Innovative Medicines Initiative (IMI) Joint Undertaking, K4DD (Grant No. 115366), by the Klaus Tschira Foundation and by a Capes-Humboldt postdoctoral scholarship to A N-A (Capes process number 88881.162167/2017-01). G.K.G. also thanks HGSMathComp Graduate School, Heidelberg University for providing academic and administrative support.
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Ganotra, G.K., Nunes-Alves, A., Wade, R.C. (2021). A Protocol to Use Comparative Binding Energy Analysis to Estimate Drug-Target Residence Time. In: Ballante, F. (eds) Protein-Ligand Interactions and Drug Design. Methods in Molecular Biology, vol 2266. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1209-5_10
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DOI: https://doi.org/10.1007/978-1-0716-1209-5_10
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