TY - JOUR
T1 - Substrate-specific screening for mutational hotspots using biased molecular dynamics simulations
AU - Ebert, Maximilian C.C.J.C.
AU - Espinola, Joaquin Guzman
AU - Lamoureux, Guillaume
AU - Pelletier, Joelle N.
N1 - Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/10/6
Y1 - 2017/10/6
N2 - Prediction of substrate-specific mutational hotspots for enzyme engineering is a complex and computationally intensive task. This becomes particularly challenging when the available crystal structures have no ligand, bind a distant homologue of the desired substrate, or hold the ligand in a nonproductive conformation. To address that shortcoming, we present a combined molecular dynamics simulation and molecular docking protocol to predict the conformation of catalytically relevant enzyme-ligand complexes even in the absence of a ligand-bound structure. We applied the adaptive biasing force method to predict the ligand-specific path of diffusion of a fatty acid substrate from the bulk media into the active site of cytochrome P450 CYP102A1 (BM3). Starting with a ligand-free crystal structure, we successfully identified all residues known to be involved in palmitic acid binding to BM3. The binding trajectory also revealed a yet unknown binding residue, Q73, which we confirmed experimentally. Building the free-energy landscape illustrates that, similar to human cytochrome P450s, binding is multistep and does not follow simple Michaelis-Menten kinetics. We confirmed the robustness of the method using a structurally distinct substrate, the small aromatic indole. We then applied the predicted BM3:palmitate complex to molecular docking of a library of 29 palmitate analogues. This produced catalytically relevant binding poses for the entire library, while docking directly into ligand-free and ligand-bound crystal structures gave poor results. This fast and simple computational method is broadly applicable for predicting binding hotspots in a substrate-specific manner and has the potential to drastically reduce the experimental screening effort to tailor an enzyme to substrates of interest.
AB - Prediction of substrate-specific mutational hotspots for enzyme engineering is a complex and computationally intensive task. This becomes particularly challenging when the available crystal structures have no ligand, bind a distant homologue of the desired substrate, or hold the ligand in a nonproductive conformation. To address that shortcoming, we present a combined molecular dynamics simulation and molecular docking protocol to predict the conformation of catalytically relevant enzyme-ligand complexes even in the absence of a ligand-bound structure. We applied the adaptive biasing force method to predict the ligand-specific path of diffusion of a fatty acid substrate from the bulk media into the active site of cytochrome P450 CYP102A1 (BM3). Starting with a ligand-free crystal structure, we successfully identified all residues known to be involved in palmitic acid binding to BM3. The binding trajectory also revealed a yet unknown binding residue, Q73, which we confirmed experimentally. Building the free-energy landscape illustrates that, similar to human cytochrome P450s, binding is multistep and does not follow simple Michaelis-Menten kinetics. We confirmed the robustness of the method using a structurally distinct substrate, the small aromatic indole. We then applied the predicted BM3:palmitate complex to molecular docking of a library of 29 palmitate analogues. This produced catalytically relevant binding poses for the entire library, while docking directly into ligand-free and ligand-bound crystal structures gave poor results. This fast and simple computational method is broadly applicable for predicting binding hotspots in a substrate-specific manner and has the potential to drastically reduce the experimental screening effort to tailor an enzyme to substrates of interest.
KW - Adaptive biasing force
KW - Binding mode prediction
KW - Biocatalysis
KW - Energy landscape
KW - Protein engineering
KW - Virtual screening
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UR - http://www.scopus.com/inward/citedby.url?scp=85032622098&partnerID=8YFLogxK
U2 - 10.1021/acscatal.7b02634
DO - 10.1021/acscatal.7b02634
M3 - Article
AN - SCOPUS:85032622098
SN - 2155-5435
VL - 7
SP - 6786
EP - 6797
JO - ACS Catalysis
JF - ACS Catalysis
IS - 10
ER -