TY - JOUR
T1 - Simulation of the Bottleneck Controlling Access into a Rieske Active Site
T2 - Predicting Substrates of Naphthalene 1,2-Dioxygenase
AU - Escalante, Diego E.
AU - Aukema, Kelly G.
AU - Wackett, Lawrence P.
AU - Aksan, Alptekin
N1 - Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/3/27
Y1 - 2017/3/27
N2 - Naphthalene 1,2-dioxygenase (NDO) has been computationally understudied despite the extensive experimental knowledge obtained for this enzyme, including numerous crystal structures and over 100 demonstrated substrates. In this study, we have developed a substrate prediction model that moves away from the traditional active-site-centric approach to include the energetics of substrate entry into the active site. By comparison with experimental data, the accuracy of the model for predicting substrate oxidation is 92%, with a positive predictive value of 93% and a negative predictive value of 98%. Also, the present analysis has revealed that the amino acid residues that provided the largest energetic barrier for compounds entering the active site are residues F224, L227, P234, and L235. In addition, F224 is proposed to play a role in controlling ligand entrance via π-π stacking stabilization as well as providing stabilization via T-shaped π-π interactions once the ligand has reached the active-site cavity. Overall, we present a method capable of being scaled to computationally discover thousands of substrates of NDO, and we present parameters to be used for expanding the prediction method to other members of the Rieske non-heme iron oxygenase family.
AB - Naphthalene 1,2-dioxygenase (NDO) has been computationally understudied despite the extensive experimental knowledge obtained for this enzyme, including numerous crystal structures and over 100 demonstrated substrates. In this study, we have developed a substrate prediction model that moves away from the traditional active-site-centric approach to include the energetics of substrate entry into the active site. By comparison with experimental data, the accuracy of the model for predicting substrate oxidation is 92%, with a positive predictive value of 93% and a negative predictive value of 98%. Also, the present analysis has revealed that the amino acid residues that provided the largest energetic barrier for compounds entering the active site are residues F224, L227, P234, and L235. In addition, F224 is proposed to play a role in controlling ligand entrance via π-π stacking stabilization as well as providing stabilization via T-shaped π-π interactions once the ligand has reached the active-site cavity. Overall, we present a method capable of being scaled to computationally discover thousands of substrates of NDO, and we present parameters to be used for expanding the prediction method to other members of the Rieske non-heme iron oxygenase family.
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U2 - 10.1021/acs.jcim.6b00469
DO - 10.1021/acs.jcim.6b00469
M3 - Article
C2 - 28170277
AN - SCOPUS:85025098508
SN - 1549-9596
VL - 57
SP - 550
EP - 561
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 3
ER -