A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

BackgroundCurrent scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.ResultsIn our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the reference complex with a maximal similarity to the query complex. Our KGS strategy was evaluated in combination with two scoring functions (X-Score and PLP) on three test sets, containing 112 HIV protease complexes, 44 carbonic anhydrase complexes, and 73 trypsin complexes, respectively. Our results obtained on crystal structures as well as computer-generated docking poses indicated that application of the KGS strategy produced more accurate predictions especially when X-Score or PLP alone did not perform well.ConclusionsCompared to other targeted scoring functions, our KGS strategy does not require any re-parameterization or modification on current scoring methods, and its application is not tied to certain systems. The effectiveness of our KGS strategy is in theory proportional to the ever-increasing knowledge of experimental protein-ligand binding data. Our KGS strategy may serve as a more practical remedy for current scoring functions to improve their accuracy in binding affinity prediction.

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