ArticleJournal of molecular biology2011
iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.
Article in Journal of molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 1 citations in OpenAlex.
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- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions.Cell systems · 2021Article
- dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.Nucleic acids research · 2021Article
- Methods for Molecular Modelling of Protein Complexes.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Graph embeddings on gene ontology annotations for protein-protein interaction prediction.BMC bioinformatics · 2020Article
- Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions.Nucleic acids research · 2019Article
- An integration of deep learning with feature embedding for protein-protein interaction prediction.PeerJ · 2019Article
- Determining the minimum number of protein-protein interactions required to support known protein complexes.PloS one · 2018Article
- Non-interacting proteins may resemble interacting proteins: prevalence and implications.Scientific reports · 2017Article
- Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information.Methods in molecular biology (Clifton, N.J.) · 2017Article
- HOMCOS: an updated server to search and model complex 3D structures.Journal of structural and functional genomics · 2016Article
- HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1.Molecular & cellular proteomics : MCP · 2016Article
- Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.International journal of molecular sciences · 2015Article
- Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.BioMed research international · 2015Article
- Interaction-based discovery of functionally important genes in cancers.Nucleic acids research · 2014Article
- Template-based structure modeling of protein-protein interactions.Current opinion in structural biology · 2014Review
- Protein-protein interaction detection: methods and analysis.International journal of proteomics · 2014Review
- Cancer missense mutations alter binding properties of proteins and their interaction networks.PloS one · 2013Article
- A computational framework for boosting confidence in high-throughput protein-protein interaction datasets.Genome biology · 2012Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the "twilight zone" (<40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses only on the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve a coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer-related genes involved in chromatin remodeling, nucleosome organization, and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.
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Registered trials
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 OpenQuestion graph.