title: Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors.
abstract: In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries.
authors.affiliation: Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Resaerch Institute, Campus, Lucknow 226031, India; Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow 226031, India.
authors.forename: Sharat
authors.initials: S
authors.lastname: Chandra
authors.affiliation: Biochemistry Division, CSIR-Central Drug Research Institute, Lucknow 226031, India.
authors.forename: Jyotsana
authors.initials: J
authors.lastname: Pandey
authors.affiliation: Biochemistry Division, CSIR-Central Drug Research Institute, Lucknow 226031, India.
authors.forename: Akhilesh Kumar
authors.initials: AK
authors.lastname: Tamrakar
authors.affiliation: Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Resaerch Institute, Campus, Lucknow 226031, India; Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow 226031, India. Electronic address: mi_siddiqi@cdri.res.in.
authors.forename: Mohammad Imran
authors.initials: MI
authors.lastname: Siddiqi
mesh_terms: D001499:Bayes Theorem; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy* / Q000235:genetics; D006801:Humans; D007328:Insulin / Q000737:chemistry / Q000235:genetics; D020738:Leptin / Q000737:chemistry / Q000235:genetics; D000069550:Machine Learning; D008958:Models, Molecular; D009765:Obesity / Q000188:drug therapy* / Q000235:genetics; D011485:Protein Binding; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1 / Q000037:antagonists & inhibitors / Q000737:chemistry* / Q000235:genetics; D015398:Signal Transduction / Q000187:drug effects; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000627:therapeutic use
chemical_list: D007328:Insulin; D020738:Leptin; D054852:Small Molecule Libraries; C516607:PTPN1 protein, human; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1
keywords: Protein-tyrosine phosphatase 1B; Structure fragments; Support vector machine; Type-2 diabetes; Virtual screening