ArticlePloS one2018
LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- PolyProline Predictor: A web server for empirical sequence-based prediction of polyproline II helices.Protein science : a publication of the Protein Society · 2026Article
- Article
- Identification of all-against-all protein-protein interactions based on deep hash learning.BMC bioinformatics · 2022Article
- Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Early Metazoan Origin and Multiple Losses of a Novel Clade of RIM Presynaptic Calcium Channel Scaffolding Protein Homologs.Genome biology and evolution · 2020Article
- MotifAnalyzer-PDZ: A computational program to investigate the evolution of PDZ-binding target specificity.Protein science : a publication of the Protein Society · 2019Article
- Machine-learning techniques for the prediction of protein-protein interactions.Journal of biosciences · 2019Article
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3 authors.
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Abstract
Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)-SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.
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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.