ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor

Abstract

Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus–host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

Machine Learning and Artificial Intelligence for the Prediction of Host–Pathogen Interactions: A Viral Case

“…Small molecule information is often represented using a simplified molecular-input lineentry system (smiles). 27 In such a setting ML can assist in RNA and DNA accessibility analysis, transcription analysis (reviewed in 28 ), protein-protein interactions, [11][12][13]29 as well as, sequence-based host organism or receptor prediction. 14 Noteworthy, in well-defined tasks simple ML algorithms like Random Forest (RF) 30 classification, Multilayer Perceptron (MLP) or kernel-based SVM 31 perform remarkably well.…”

“…For example, Karabulut et al show that on the task of adenovirus infection genus prediction kernel-based SVM reaches performance of 0.96 F1 score and 0.89 area under the receiver operating characteristics curve (AUC) with RF and MLP algorithms trailing remarkably close. 29 In such cases, more advanced algorithms like DL are not very likely to deliver a significant further improvement. However, in the settings outside of the very specific data set these algorithms may deliver a boost in generalization.…”

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses

“…Researchers have developed a machine learning based clustering applications to distinguish several microbial pathogens ( 24 , 25 ) and classification applications to categorize the genes associated with the survival of pathogens under certain environmental conditions, antibiotics, or other disturbances ( 26 ). Similarly, researchers have developed classification applications to determine VHPPIs that play a key role in understanding the functional paradigms of viruses as well as host responses ( 27 , 28 ). With an aim to provide cheap, fast, and accurate virus-host protein-protein analyzes, to date, around 13 AI-based predictors ( 21 – 23 , 27 – 36 ) have been proposed.…”

“…The predictor ( 31 ) was evaluated on data related to 12 viruses, and human proteins. Karabulut et al ( 28 ) proposed meta predictor (ML-AdVInfect) that reaped the benefits of 4 existing predictors namely HOPITOR ( 37 ), InterSPPI-HVPPI ( 31 ), VHPPI, and Denovo ( 29 ). Specifically, the authors passed the predictions of existing predictors to the SVM classifier for the final VHPPI prediction.…”

HMI-PRED 2.0: a biologist-oriented web application for prediction of host–microbe protein–protein interaction by interface mimicry

“…Computational methods have been developed to predict, integrate and analyze general or virus–human protein–protein interactions at a large scale, where the use of non-structural features is incentivized due to the limited structural coverage ( Andrighetti et al , 2020 ; Ding and Kihara, 2018 ; Dong et al , 2021 ; Karabulut et al , 2021 ; Mahajan and Mande, 2017 ; Wu et al , 2020 ). On the other hand, it was suggested that the structural space is sufficient for protein complex modeling ( Kundrotas et al , 2012 ).…”