Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints

Jie Liu,Minyi Su,Zhihai Liu,Yan Li,Jie Li,Renxiao Wang

doi:10.1186/s12859-017-1750-5

Abstract

BackgroundIn structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193–208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively.ResultsIn this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases.ConclusionsKGS2 can be applied as a convenient “add-on” to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.

Highlights

In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions
We demonstrate in this study that the performance of current scoring functions in binding affinity prediction can be enhanced by KGS2 with the aid of 3D protein-ligand interaction fingerprints
Even at the lowest similarity cutoff applied to reference selection (i.e. similarity index (SI) ≥ 0.10), the errors produced by X-Score + KGS2 are smaller by 0.3 logKa units than those produced by X-Score alone

Summary

Introduction

In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193–208). The primary goal of molecular docking is to predict the binding pose of a given ligand molecule to a molecular target, usually a protein or a nucleic acid. It provides a useful guide especially when experimental means, such as X-ray crystal diffraction or NMR spectroscopy, cannot supply the desired answer in a. If the scoring functions used in molecular docking could be improved in this aspect, molecular docking will certainly become more useful

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Results

Conclusion