ArticleMethods in molecular biology (Clifton, N.J.)2021
Deep Learning for Protein-Protein Interaction Site Prediction.
Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed.
- Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning.iScience · 2026Article
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- Article
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- Fast end-to-end surface interpretation of SARS-CoV-2 variants by differentiable molecular surface interaction fingerprinting method.Computational and structural biotechnology journal · 2023Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Protein-protein interactions (PPIs) are central to cellular functions. Experimental methods for predicting PPIs are well developed but are time and resource expensive and suffer from high false-positive error rates at scale. Computational prediction of PPIs is highly desirable for a mechanistic understanding of cellular processes and offers the potential to identify highly selective drug targets. In this chapter, details of developing a deep learning approach to predicting which residues in a protein are involved in forming a PPI-a task known as PPI site prediction-are outlined. The key decisions to be made in defining a supervised machine learning project in this domain are here highlighted. Alternative training regimes for deep learning models to address shortcomings in existing approaches and provide starting points for further research are discussed. This chapter is written to serve as a companion to developing deep learning approaches to protein-protein interaction site prediction, and an introduction to developing geometric deep learning projects operating on protein structure graphs.
Indexed as
Identifiers
34236667What OpenQuestion holds
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.