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 - Funding Information:
We thank Daniela Quaglia for fruitful discussions. Computational resources were provided by Calcul Queb́ ec and Compute Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation (CFI), NanoQueb́ ec, RMGA and the Fonds de recherche du Queb́ ec - Nature et technologies (FRQ-NT). This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants RGPIN 227853 (to J.N.P.) and RGPIN 355789 (to G.L.). M.C.C.J.C.E. is the recipient of a Vanier Doctoral Scholarship.
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
UR - http://www.scopus.com/inward/record.url?scp=85032622098&partnerID=8YFLogxK
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 -