Evidence map›Paper›PMID 35662463›Full record

ArticleJournal of molecular biology2022

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

Eric W Bell, Jacob H Schwartz, Lydia Freddolino, Yang Zhang

Open access · greenAbstract read
In one paragraph

Article in Journal of molecular biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
9.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 63 citations in OpenAlex.

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  9. Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
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  12. Conserved leucine-rich repeat proteins in the adhesive projectile slime of velvet worms.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  17. Expanding the genotypic and phenotypic spectrum of EAST/SeSAME syndrome: identification of a novel homozygous mutation (c.194 G > A) in KCNJ10 gene.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
    Article
  18. Fine-Tuned Expression of Evolutionarily Conserved Signaling Molecules in theInternational journal of molecular sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Eric W BellDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Jacob H SchwartzDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Lydia FreddolinoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: petefred@umich.edu.
Yang ZhangDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: zhng@umich.edu.
University of Michigan–Ann Arbor · US

Funding

Structure-based functional annotation of microbial genomesR01AI134678 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Lydia Petra Freddolino, SCOTT J. HULTGREN · 2018 to 2026
$6.5M
Advanced approaches to protein structure predictionR35GM136422 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZHANG, YANG · 2020 to 2021
$946k
High-Performance Computing Cluster for Biomedical ResearchS10OD026825 · OD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ATHEY, BRIAN DAVID · 2020 to 2020
$600k
NIAID NIH HHS R01 AI134678NIGMS NIH HHS R35 GM136422NIH HHS S10 OD026825
6 · The paper itself

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.

Indexed as

Machine LearningProtein Interaction MappingProteomeSoftwareBayes TheoremCOVID-19HumansProteomicsSARS-CoV-2Proteomedimer threadingdirect interaction predictioninteractomeinterologySARS-CoV-2

Identifiers

PMID35662463
PMCPMC8897833
OpenAlexW4220851257

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.