Predicting the potency of inhibitors is key to in silico screening of promising synthetic or natural compounds. Here we describe a predictive workflow that provides calculated inhibitory values, which concord well with empirical data. Calculations of the free interaction energy ΔG with the YASARA plugin FoldX were used to derive inhibition constants Ki from PDB coordinates of protease-inhibitor complexes. At the same time, corresponding KD values were obtained from the PRODIGY server. These results correlated well with the experimental values, particularly for serine proteases. In addition, analyses were performed for inhibitory complexes of cysteine and aspartic proteases, as well as of metalloproteases, whereby the PRODIGY data appeared to be more consistent. Based on our analyses, we calculated theoretical Ki values for trypsin with sunflower trypsin inhibitor (SFTI-1) variants, which yielded the more rigid Pro14 variant, with probably higher potency than the wild-type inhibitor. Moreover, a hirudin variant with an Arg1 and Trp3 is a promising basis for novel thrombin inhibitors with high potency. Further examples from antibody interaction and a cancer-related effector-receptor system demonstrate that our approach is applicable to protein interaction studies beyond the protease field.
Keywords: SARS-CoV-2 main protease; Van ’t Hoff equation; dissociation constant; free energy; hirudin; inhibition constant; protease inhibitor; sunflower trypsin inhibitor.