Malivhu: A Comprehensive Bioinformatics Resource for Filtering SARS and MERS Virus Proteins by Their Classification,...

COVID 19 pandemic is still ongoing, having taken more than 6 million human lives with it, and it seems that the world will have to learn how to live with the virus around. In consequence, there is a need to develop different treatments against it, not only with vaccines, but also new medicines. To do this, human-virus protein-protein interactions (PPIs) play a key part in drug-target discovery, but finding them experimentally can be either costly or sometimes unreliable. Therefore, computational methods arose as a powerful alternative to predict these interactions, reducing costs and helping researchers confirm only certain interactions instead of trying all possible combinations in the laboratory. Malivhu is a tool that predicts human-virus PPIs through a 4-phase process using machine learning models, where phase 1 filters ssRNA(+) class virus proteins, phase 2 filters Coronaviridae family proteins and phase 3 filters severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) species proteins, and phase 4 predicts human-SARS-CoV/SARS-CoV-2/MERS protein-protein interactions. The performance of the models was measured with Matthews correlation coefficient, F1-score, specificity, sensitivity, and accuracy scores, getting accuracies of 99.07%, 99.83%, and 100% for the first 3 phases, respectively, and 94.24% for human-SARS-CoV PPI, 94.50% for human-SARS-CoV-2 PPI, and 95.45% for human-MERS PPI on independent testing. All the prediction models developed for each of the 4 phases were implemented as web server which is freely available at https://kaabil.net/malivhu/.