Prediction and classification of virus families/sub-families, and human-MERS/SARS-CoV/SARS-CoV-2 protein-protein interactions prediction using machine learning
Creators: name: Guevara, David
name: Kaundal, Rakesh
Publisher: name: F1000 Research Limited
Year: 2021

description: The outbreak of disease-causing agents is continuously threatening public health worldwide, particularly the Viruses. Millions of people die due to the effects of illness resulting from virus infection, and over the years more aggressive epidemics are arising; the recent example being the ongoing Covid-19 pandemic. Host and pathogen are the life entities involved in such infectious diseases. The relationship between these two entities are crucial to the outcomes of infection and therefore host-pathogen interactions (HPI) have been studied to better understand the whole infection progression. HPIs are involved in every step of disease: from the initial pathogen transmission, through the activation of the pathogenic mechanism used to overcome (or hijack) host cell defenses, and to the pathogen becoming established and massively reproduce within the host system; all these processes involve the interaction of host and pathogen proteins. Identification of these protein-protein interactions (PPI) could help to unravel the disease pathways, provide methods to improve resistance, and ultimately accelerate the development of drugs and other therapeutics.<br> <br> Knowing about PPIs is important for drug target discovery because we can enhance or disrupt these interactions to relieve diseases. To find about PPIs, there are high and low throughput methods, which are costly and there is a tradeoff between scalability and reliability due to high-throughput methods being able to output a lot of interactions, but many can be false positives. On the other hand, low-throughput methods are very reliable, but can detect a small number of interactions. This is where computational methods come in handy to predict if a pair of proteins interact, which could save a lot of resources and time.<br> <br> For this project, we predict whether two protein sequences are a human-MERS/SARS1/SARS2 interaction with machine learning and neural networks. This predictor will be usable from a web server. The application lets the user input two protein sequences: the virus and the host proteins. In the background, it will work in four phases: (I) it will predict whether the input sequence is, indeed, a viral protein; (II) it will predict whether the protein comes from a coronavirus; (III) it will predict whether the protein comes from MERS, SARS1 or SARS2; and (IV) it will predict whether both proteins interact or not.<br> <br> We have done rigorous testing for phase 4 with Random Forests, a simple convolutional neural network (CNN) and a joint dataset of the three viruses. The dataset was built encoding amino acid triplets as integers, each possible combination having a unique ID, and zero-padding the data in the input vector for those sequences that did not get to the maximum length. With the random forest model, we got 97.25% accuracy using a dataset with a 1:1 ratio between positive interactions and negative interactions. With the CNN, we got 96.97% accuracy using a 1:1 ratio between positive and negative interactions, while we got 96.65% with a 1:5 ratio.<br> <br> For the final version, we are building a CNN (Fig 1) with an attention mechanism for each phase and each virus in phase IV due to the sequential and structural nature of the proteins, which adds up to 6 different models. Also, we are testing these models with more features than just the triplet encoding (Table 1). We hope to achieve a higher accuracy for each of the steps/phases, surpassing the already high score that we got in the preliminary tests. The web server will be freely available for the research community use, and will be implemented shortly at http://bioinfo.usu.edu/tools.