Related Experiment Video
Updated: Feb 7, 2026
A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance
Published on: March 19, 2020
Computational Analysis of LOX1 Inhibition Identifies Descriptors Responsible for Binding Selectivity
Chrysoula Gousiadou1, Irene Kouskoumvekaki1
1Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Lyngby, Denmark.
Abstract:
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.
Insights
Researchers developed machine learning models to identify key structural features for selective inhibition of lipoxygenases (LOX). These models accurately predict LOX1 activity and distinguish between inhibitors targeting 15S-LOX1 and 12S-LOX1 isozymes.
Area of Science:
- Biochemistry
- Enzymology
- Pharmacology
Background:
- Lipoxygenases (LOX) are enzymes involved in polyunsaturated fatty acid metabolism.
- LOX enzymes play a role in the pathogenesis of several human diseases.
- Developing selective LOX inhibitors is challenging due to enzyme subtype similarity.
Purpose of the Study:
- To identify structural descriptors that confer binding selectivity for 15S-LOX1.
- To develop Quantitative Structure-Activity Relationship (QSAR) models for LOX1 activity and selectivity.
- To create predictive models capable of discriminating between inhibitors of 15S-LOX1 and 12S-LOX1.
Main Methods:
- Assembled a dataset of 317 diverse molecules active against 15S-LOX1, 12S-LOX1, and 15S-LOX2.
- Employed supervised machine learning to identify selectivity-driving structural descriptors.
- Trained ensemble QSAR models using Support Vector Machine, Random Forest, and k-Nearest Neighbor algorithms.
Main Results:
- Identified specific structural descriptors crucial for 15S-LOX1 binding selectivity.
- Developed highly accurate QSAR models for predicting LOX1 activity and inactivity.
- Achieved high accuracy in discriminating between molecules selective for 15S-LOX1 versus 12S-LOX1.
Conclusions:
- Machine learning can identify key structural features for selective enzyme inhibition.
- Ensemble QSAR models offer a powerful approach for drug design targeting lipoxygenase isozymes.
- These models facilitate the development of selective inhibitors for therapeutic applications.
Related Concept Videos
00:46Feedback Inhibition
01:25Dose-Response Relationship: Selectivity and Specificity
00:49Ligand Binding and Linkage
01:49Conserved Binding Sites
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
02:18The Equilibrium Binding Constant and Binding Strength
02:18The Equilibrium Binding Constant and Binding Strength