MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses - Asim, Muhammad Nabeel; Fazeel, Ahtisham; Ibrahim, Muhammad Ali; Dengel, Andreas; Ahmed, Sheraz (Frontiers in Medicine, 2022)
Viral-host protein protein interaction (VHPPI) prediction is essential to decode molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. Prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable to more accurately predict VHPPI across multiple hosts and viruses. Proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform original feature space to more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07%, 6.07%, 2.95%, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate scientific community, MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.