Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450

Abstract

Background: Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s). Objective: This review discusses the recent advances of these methods and the implications that are specific for P450s. Methods: Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand–P450 interactions. Conclusion: P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.

Keywords::

• binding affinity
• binding free energy
• CYP
• docking
• in silico
• machine learning
• metabolite formation
• molecular dynamics
• pharmacophore
• polymorphism
• QSAR
• scoring
• selectivity

Acknowledgement

We thank Chris de Graaf for providing us with the original version of Figure 1.