ArticleBriefings in bioinformatics2022
Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- PepMCP: a graph-based membrane contact probability predictor for membrane-lytic antimicrobial peptides.Bioinformatics (Oxford, England) · 2026Article
- Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2026Article
- MPLID (Membrane Protein-Lipid Interaction Database): A Large-Scale Experimental Resource of Residue-Level Protein-Lipid Contacts.GigaScience · 2026Article
- Crystal structure of dihydroorotate dehydrogenase from Helicobacter pylori with bound flavin mononucleotide.Acta crystallographica. Section F, Structural biology communications · 2025Article
- Computational Insights on the Assembly of the Dengue Virus Membrane-Capsid-RNA Complex.The Journal of membrane biology · 2025Article
- Insight into molecular basis and dynamics of full-length CRaf kinase in cellular signaling mechanisms.Biophysical journal · 2024Article
- PMIpred: a physics-informed web server for quantitative protein-membrane interaction prediction.Bioinformatics (Oxford, England) · 2024Article
- Challenges of Protein-Protein Docking of the Membrane Proteins.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Sequence, structure prediction, and epitope analysis of the polymorphic membrane protein family in Chlamydia trachomatis.PloS one · 2024Article
- Using deep learning and large protein language models to predict protein-membrane interfaces of peripheral membrane proteins.Bioinformatics advances · 2024Article
- Structure and Function of ArnD. A Deformylase Essential for Lipid A Modification with 4-Amino-4-deoxy-l-arabinose and Polymyxin Resistance.Biochemistry · 2023Article
- Impaired activity and membrane association of most calpain-5 mutants causal for neovascular inflammatory vitreoretinopathy.Biochimica et biophysica acta. Molecular basis of disease · 2023Article
- Review
- SARS-CoV-2 Omicron Subvariants Balance Host Cell Membrane, Receptor, and Antibody Docking via an Overlapping Target Site.Viruses · 2023Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design.Bioinformatics (Oxford, England) · 2022Article
- Dissecting peripheral protein-membrane interfaces.PLoS computational biology · 2022Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Article
- Specific interactions of peripheral membrane proteins with lipids: what can molecular simulations show us?Bioscience reports · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.
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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 OpenQuestion graph.