Assessment of free energy predictors for ligand binding to a methyllysine histone code reader

Methyllysine histone code readers constitute a new promising group of potential drug targets. For instance, L3MBTL1, a malignant brain tumor (MBT) protein, selectively binds mono- and di-methyllysine epigenetic marks (KMe, KMe(2) ) that eventually results in the negative regulation of multiple genes through the E2F/Rb oncogenic pathway. There is a pressing need in potent and selective small-molecule probes that would enable further target validation and might become therapeutic leads. Such an endeavor would require efficient tools to assess the free energy of protein-ligand binding. However, due to an unparalleled function of the MBT binding pocket (i.e., selective binding to KMe/KMe(2) ) and because of its distinctive structure representing a small aromatic "cage," an accurate assessment of its binding affinity to a ligand appears to be a challenging task. Here, we report a comparative analysis of computationally affordable affinity predictors applied to a set of seven small-molecule ligands interacting with L3MBTL1. The analysis deals with novel ligands and targets, but applies widespread computational approaches and intuitive comparison metrics that makes this study compatible with and incremental to earlier large scale accounts on the efficiency of affinity predictors. Ultimately, this study has revealed three top performers, far ahead of the other techniques, including two scoring functions, PMF04 and PLP, along with a simulation-based method MM-PB/SA. We discuss why some methods may perform better than others on this target class, the limits of their application, as well as how the efficiency of the most CPU-demanding techniques could be optimized.