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

ABSTRACT: 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.

SUBMITTER: Gao C

PROVIDER: S-EPMC3276689 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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

Gao Cen C Herold J Martin JM Kireev Dmitri D

Journal of computational chemistry 20111220 6

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 en ...[more]

PMID: 22183769

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Project description:Accurate prediction of the binding affinities of small-molecule ligands to their biological targets is fundamental for structure-based drug design but remains a very challenging task. In this paper, we have performed computational studies to predict the binding models of 31 small-molecule Smac (the second mitochondria-derived activator of caspase) mimetics to their target, the XIAP (X-linked inhibitor of apoptosis) protein, and their binding affinities. Our results showed that computational docking was able to reliably predict the binding models, as confirmed by experimentally determined crystal structures of some Smac mimetics complexed with XIAP. However, all the computational methods we have tested, including an empirical scoring function, two knowledge-based scoring functions, and MM-GBSA (molecular mechanics and generalized Born surface area), yield poor to modest prediction for binding affinities. The linear correlation coefficient (r(2)) value between the predicted affinities and the experimentally determined affinities was found to be between 0.21 and 0.36. Inclusion of ensemble protein-ligand conformations obtained from molecular dynamic simulations did not significantly improve the prediction. However, major improvement was achieved when the free-energy change for ligands between their free- and bound-states, or "ligand-reorganization free energy", was included in the MM-GBSA calculation, and the r(2) value increased from 0.36 to 0.66. The prediction was validated using 10 additional Smac mimetics designed and evaluated by an independent group. This study demonstrates that ligand reorganization free energy plays an important role in the overall binding free energy between Smac mimetics and XIAP. This term should be evaluated for other ligand-protein systems and included in the development of new scoring functions. To our best knowledge, this is the first computational study to demonstrate the importance of ligand reorganization free energy for the prediction of protein-ligand binding free energy.

Dataset Information

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

Publications

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

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