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PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

ABSTRACT: Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

SUBMITTER: Bell EW

PROVIDER: S-EPMC8897833 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo obtain complete DNA sequences of adenoviral (AdV) D8 genome from patients with conjunctivitis and determine the relation of sequence variation to clinical outcomes.DesignThis study is a post hoc analysis of banked conjunctival swab samples from the BAYnovation Study, a previously conducted, randomized controlled clinical trial for AdV conjunctivitis.ParticipantsNinety-six patients with AdV D8-positive conjunctivitis who received placebo treatment in the BAYnovation Study were included in the study.MethodsDNA from conjunctival swabs was purified and subjected to whole-genome viral DNA sequencing. Adenovirus D8 variants were identified and correlated with clinical outcomes, including 2 machine learning methods.Main outcome measuresViral DNA sequence and development of subepithelial infiltrates (SEIs) were the main outcome measures.ResultsFrom initial sequencing of 80 AdV D8-positive samples, full adenoviral genome reconstructions were obtained for 71. A total of 630 single-nucleotide variants were identified, including 156 missense mutations. Sequence clustering revealed 3 previously unappreciated viral clades within the AdV D8 type. The likelihood of SEI development differed significantly between clades, ranging from 83% for Clade 1 to 46% for Clade 3. Genome-wide analysis of viral single-nucleotide polymorphisms failed to identify single-gene determinants of outcome. Two machine learning models were independently trained to predict clinical outcome using polymorphic sequences. Both machine learning models correctly predicted development of SEI outcomes in a newly sequenced validation set of 16 cases (P = 1.5 × 10-5). Prediction was dependent on ensemble groups of polymorphisms across multiple genes.ConclusionsAdenovirus D8 has ≥ 3 prevalent molecular substrains, which differ in propensity to result in SEIs. Development of SEIs can be accurately predicted from knowledge of full viral sequence. These results suggest that development of SEIs in AdV D8 conjunctivitis is largely attributable to pathologic viral sequence variants within the D8 type and establishes machine learning paradigms as a powerful technique for understanding viral pathogenicity.

Dataset Information

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

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