Ensemble modeling of substrate binding to cytochromes P450: analysis of catalytic differences between CYP1A orthologs.

A novel application of modeling and docking approaches involving ensembles of homology models is used to understand structural bases underlying subtle catalytic differences between related cytochromes P450 (CYPs). Mammalian CYP1A1s and fish CYP1As are orthologous enzymes with similar substrate preferences. With some substrates (3,3',4,4'-tetrachlorobiphenyl, TCB) oxidation rates differ by orders of magnitude, while others (e.g., benzo[a]pyrene; B[a]P) are oxidized at similar rates but with somewhat differing regiospecificity. These two environmental chemical substrates (TCB and B[a]P) as well as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) were docked to multiple models of rat, human, scup, and/or killifish CYP1As, based on multiple templates, retaining multiple poses from each model, giving ensembles of docked poses for each species. With TCB, more poses were observed closer to the heme in ensembles of rat or human CYP1A1 than of killifish CYP1A. Analysis of interacting residues suggested that differences in TCB pose distributions are due primarily to Leu387 and Val230 in killifish CYP1A. In silico mutations L387V and V230G enabled TCB to dock closer to the heme in killifish CYP1A. Mutating additional interacting residues (Ala127, Thr233, Asn317, and Tyr386) of killifish CYP1A to the corresponding residues of human CYP1A1 resulted in TCB pose distributions nearly identical with those of human CYP1A1. Docking of TCDD to sets of consensus models of killifish, rat, and human CYP1As showed species differences similar to those with TCB, but with further structural constraints possibly contributing to slower oxidation of TCDD. Docking B[a]P to sets of consensus models of the human and fish CYP1As yielded frequencies of substrate orientations correlating with known regiospecificities for metabolism of B[a]P by these enzymes. The results demonstrate the utility of this ensemble modeling method, which can account for uncertainty inherent in homology modeling and docking by producing statistical distributions of ligand positions.