Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists. (January 2022)

• Record Type: Journal Article
• Authors: Koh, Dong-Hee; Song, Woo-Seon; Kim, Eun-young
• Is Part Of: Chemosphere. Volume 286:Part 1(2022)
• Publication Date: 2022-01
• Subjects: Peroxisome proliferator-activated receptor gamma -- Antagonist -- Structure–activity relationship -- Multi-step screening -- Read‐across -- Docking-simulation -- Deep-learning; 2D two-dimensional -- ANN artificial neural network -- CCR correct classification ratio -- CRED Continuous/discrete Rule Extractor via Decision tree induction -- DeepRED Deep neural network Rule Extraction via Decision tree induction -- DBSCAN density-based spatial clustering of applications with noise -- FPR false-positive rate -- LMOCV leave-many-out cross-validation -- MOE Molecular Operating Environment software -- PLIF protein–ligand interaction fingerprints -- PPARγ peroxisome proliferator-activated receptor gamma -- PPV positive predictive value -- ROS Random Over Sampler -- SAR structure–activity relationship -- SMILES simplified molecular-input line-entry system -- SMOTE Synthetic Minority Over-sampling Technique -- SVM support vector machines -- TPR true-positive rate; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2021.131540
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: In discovering the potential antagonist of peroxisome proliferator-activated receptor gamma (PPARγ), the structure–activity relationship (SAR) is a useful in silico method. However, it is difficult for conventional SAR approaches to predict the activities of antagonists owing to the large structural diversity of antagonistic compounds. This study provides evidence that multi-step SAR screening is applicable for predicting PPARγ antagonists by combining different complementary methodologies. We constructed three models: read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. To provide user-customized prediction results, our multi-step SAR screening model combined the three SAR models in a stepwise manner, which subdivided them according to potential levels of the PPARγ antagonist. The read-across-like SAR, which considered specific antagonist scaffolds, revealed the highest positive predictive value (PPV). The docking-simulation-interpreting SAR, which considered the molecular surface features, revealed high statistics for the PPV and the true-positive rate (TPR). The deep-learning-based SAR showed the highest TPR at the last classification step. This multi-step SAR screening covered the antagonists of high reliability provided by a read-across-like SAR, as well as the antagonists of diverse scaffolds provided by docking-simulation-interpreting SAR and deep-learning-based SAR. Therefore, to predict PPARγ antagonists, multi-step SAR screening could be as a useful tool. Highlights: The antagonists of PPARγ have recently received attention as modulators of obesity, insulin resistance, and inflammatory response. To discover potential antagonists of PPARγ, a multi-step structure-activity relationship (SAR) approach was developed using by combining different complementary methodologies. As far as we know, this is the first study to build an integrated SAR model that combines multiple algorithm models to obtain PPARγ antagonists. This SAR model could be used as an effective screening tool for determining the PPARγ antagonists.