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Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites

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Abstract

In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein–ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein–ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

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Abbreviations

PDB:

Protein data bank

SBDD:

Structure-based drug design

MD:

Molecular dynamics

3D:

Three-dimensional

MM-PBSA/GBSA:

Molecular Mechanics-Poisson Boltzmann Surface Area/Generalised Born Surface Area

HIV:

Human immunodeficiency virus

LIE:

Linear interaction energy

FEP:

Free energy perturbation

TI:

Thermodynamic integration

ns:

Nanoseconds

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Acknowledgments

The authors would like to acknowledge The School of Health Sciences, University of KwaZulu-Natal, Westville campus for providing the infrastructure for this work. Neal K. Broomhead would like to acknowledge the National Research Foundation of South Africa for financial support.

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  1. Molecular Modelling & Drug Design Research Group, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4001, South Africa

    Neal K. Broomhead & Mahmoud E. Soliman

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Correspondence to Mahmoud E. Soliman.

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Broomhead, N.K., Soliman, M.E. Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites. Cell Biochem Biophys 75, 15–23 (2017). https://doi.org/10.1007/s12013-016-0769-y

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