Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
J Chem Inf Model. 2014 Feb 20;
Authors: Ballester PJ, Schreyer A, Blundell TL
Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein-ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein-ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.
PMID: 24528282 [PubMed - as supplied by publisher]
A structure-guided fragment-based approach for the discovery of allosteric inhibitors targeting the lipophilic binding site of transcription factor EthR.
Biochem J. 2013 Dec 6;
Authors: Surade S, Ty N, Hengrung N, Lechartier B, Cole ST, Abell C, Blundell TL
A structure-guided fragment-based approach is used to target the lipophilic allosteric binding site of Mycobacterium tuberculosis EthR. This elongated channel has many hydrophobic residues lining the binding site, with few opportunities for hydrogen bonding. We demonstrate that a fragment-based approach involving the inclusion of flexible fragments in the library leads to an efficient exploration of chemical space, that fragment binding can lead to an extension of the cavity, and that fragments are able to identify hydrogen-bonding opportunities in this hydrophobic environment that are not exploited in nature. We report the identification of a 1 µM affinity ligand obtained by structure-guided fragment linking.
PMID: 24313835 [PubMed - as supplied by publisher]