PPI-ID: Streamlining protein-protein interaction prediction through domain and SLiM mapping

AlphaFold-Multimer models protein complexes and facilitates protein-protein interaction (PPI) prediction. Mapping of protein interaction domains and motifs onto the 3D structure can lend credence to the model and provide insight into the function of a given interaction. Furthermore, limiting structure prediction to only the domains and motifs that are likely to interact can reduce the computational demand and produce a higher quality model. To satisfy these needs, we built the Protein-Protein Interaction Identifier (PPI-ID). PPI-ID maps interaction domains and motifs onto molecular structures and filters for those that are sufficiently close to interact. Once an interface is found, PPI-ID labels interacting amino acids. Given only sequences, PPI-ID predicts regions for AlphaFold-Multimer modeling, reporting potential interactions only when each protein has one-half of a paired sequence. Testing with known dimers confirms high accuracy of the tool.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-10-16
DOI
https://doi.org/10.1371/journal.pcbi.1013062
Citations
1
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
0.81
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article

PPI-ID: Streamlining protein-protein interaction prediction through domain and SLiM mapping

Nigel S. Atkinson, Haley V. Goodwin
1 citations
PLoS Computational Biology
Protein Structure and Dynamics
0.81
article

PPI-ID: Streamlining protein-protein interaction prediction through domain and SLiM mapping

Nigel S. Atkinson, Haley V. Goodwin
article en
1 citations

Abstract

AlphaFold-Multimer models protein complexes and facilitates protein-protein interaction (PPI) prediction. Mapping of protein interaction domains and motifs onto the 3D structure can lend credence to the model and provide insight into the function of a given interaction. Furthermore, limiting structure prediction to only the domains and motifs that are likely to interact can reduce the computational demand and produce a higher quality model. To satisfy these needs, we built the Protein-Protein Interaction Identifier (PPI-ID). PPI-ID maps interaction domains and motifs onto molecular structures and filters for those that are sufficiently close to interact. Once an interface is found, PPI-ID labels interacting amino acids. Given only sequences, PPI-ID predicts regions for AlphaFold-Multimer modeling, reporting potential interactions only when each protein has one-half of a paired sequence. Testing with known dimers confirms high accuracy of the tool.

PLoS Computational BiologyVol. 21(10)
The University of Texas at Austin (US)
Openalex Percentile: Top 22%
Protein Structure and Dynamics
0.81
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