PPIMpred: a web server for high-throughput screening of small molecules targeting protein–protein interaction

• Published in: Royal Society open science 2017-04, Vol.4 (4), p.160501-160501
• Main Authors: Jana, Tanmoy, Ghosh, Abhirupa, Das Mandal, Sukhen, Banerjee, Raja, Saha, Sudipto
• Format: Article
• Language: English
• Subjects: Computer Science; Docking; Modulators; Protein–protein Interaction; Support Vector Machine
• ISSN: 2054-5703; 2054-5703
• DOI: 10.1098/rsos.160501

PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein–protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66–90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores (p-values