TY - JOUR
T1 - Computational Analysis of LOX1 Inhibition Identifies Descriptors Responsible for Binding Selectivity
AU - Gousiadou, Chrysoula
AU - Kouskoumvekaki, Irene
N1 - This is an open access article published under an ACS AuthorChoice License
PY - 2018
Y1 - 2018
N2 - Lipoxygenases are a family of cytosolic, peripheral membrane enzymes, which catalyze the hydroperoxidation of polyunsaturated fatty acids and are implicated in the pathogenesis of major human diseases. Over the years, a substantial number of scientific reports have introduced inhibitors active against one or another subtype of the enzyme, but the selectivity issue has proved to be a major challenge for drug design. In the present work, we assembled a dataset of 317 structurally diverse molecules hitherto reported as active against 15S-LOX1, 12S-LOX1, and 15S-LOX2 and identified, using supervised machine learning, a set of structural descriptors responsible for the binding selectivity toward the enzyme 15S-LOX1. We subsequently incorporated these descriptors in the training of QSAR models for LOX1 activity and selectivity. The best performing classifiers are two stacked models that include an ensemble of support vector machine, random forest, and k-nearest neighbor algorithms. These models not only can predict LOX1 activity/inactivity but also can discriminate with high accuracy between molecules that exhibit selective activity toward either one of the isozymes 15S-LOX1 and 12S-LOX1.
AB - Lipoxygenases are a family of cytosolic, peripheral membrane enzymes, which catalyze the hydroperoxidation of polyunsaturated fatty acids and are implicated in the pathogenesis of major human diseases. Over the years, a substantial number of scientific reports have introduced inhibitors active against one or another subtype of the enzyme, but the selectivity issue has proved to be a major challenge for drug design. In the present work, we assembled a dataset of 317 structurally diverse molecules hitherto reported as active against 15S-LOX1, 12S-LOX1, and 15S-LOX2 and identified, using supervised machine learning, a set of structural descriptors responsible for the binding selectivity toward the enzyme 15S-LOX1. We subsequently incorporated these descriptors in the training of QSAR models for LOX1 activity and selectivity. The best performing classifiers are two stacked models that include an ensemble of support vector machine, random forest, and k-nearest neighbor algorithms. These models not only can predict LOX1 activity/inactivity but also can discriminate with high accuracy between molecules that exhibit selective activity toward either one of the isozymes 15S-LOX1 and 12S-LOX1.
U2 - 10.1021/acsomega.7b01622
DO - 10.1021/acsomega.7b01622
M3 - Journal article
C2 - 30023828
SN - 2470-1343
VL - 3
SP - 2261
EP - 2272
JO - ACS Omega
JF - ACS Omega
IS - 2
ER -