ArticleBioinformatics (Oxford, England)2024
PMIpred: a physics-informed web server for quantitative protein-membrane interaction prediction.
Article in Bioinformatics (Oxford, England), 2024. 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.
- Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics-Based Optimization with Limited Activity Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- In silico design and In Vitro validation of WK15: a promising antimicrobial peptide for aquaculture applications.World journal of microbiology & biotechnology · 2026Article
- Screening of amphipathic helices identifies features linked to inner nuclear membrane properties.Nature communications · 2026Article
- PepMCP: a graph-based membrane contact probability predictor for membrane-lytic antimicrobial peptides.Bioinformatics (Oxford, England) · 2026Article
- A Localization-Based Motif Combination Approach To the Design of Antimicrobial Peptides Targeting Gram-Positive and Gram-Negative Bacteria.Probiotics and antimicrobial proteins · 2026Article
- The Effect of Aza-Glycine Substitution on the Internalization of Dabcyl-Containing Short Oligoarginine.Biomedicines · 2026Article
- Article
- SNX-mediated biogenesis of a plant-unique vesicle derived from the multivesicular body.Nature communications · 2026Article
- Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2026Article
- Unpacking the Tumor Protein D52-like Family: Roles in Intracellular Trafficking and Cancer Progression.Cells · 2026Review
- MPLID (Membrane Protein-Lipid Interaction Database): A Large-Scale Experimental Resource of Residue-Level Protein-Lipid Contacts.GigaScience · 2026Article
- A Comprehensive In-Silico Pipeline for the Discovery of Non-toxic, Stable Antimicrobial Peptides From Databases for Targeting Multi-Drug ResistantBioinformatics and biology insights · 2026Article
- Article
- The role of amphipathic and cationic helical peptides in Parkinson's disease.Protein science : a publication of the Protein Society · 2025Review
- Tracking Disordered Extracellular Domains of Membrane Proteins in the Cell with Cu(II)-Based Spin Labels.The journal of physical chemistry. B · 2024Article
- The FAM114A proteins are adaptors for the recycling of Golgi enzymes.Journal of cell science · 2024Article
- The C-terminal sequences of Bcl-2 family proteins mediate interactions that regulate cell death.The Biochemical journal · 2024Review
- SNARE chaperone Sly1 directly mediates close-range vesicle tethering.The Journal of cell biology · 2024Article
- When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases.Journal of chemical theory and computation · 2024Review
- Using deep learning and large protein language models to predict protein-membrane interfaces of peripheral membrane proteins.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationMany membrane peripheral proteins have evolved to transiently interact with the surface of (curved) lipid bilayers. Currently, methods to quantitatively predict sensing and binding free energies for protein sequences or structures are lacking, and such tools could greatly benefit the discovery of membrane-interacting motifs, as well as their de novo design.
resultsHere, we trained a transformer neural network model on molecular dynamics data for >50 000 peptides that is able to accurately predict the (relative) membrane-binding free energy for any given amino acid sequence. Using this information, our physics-informed model is able to classify a peptide's membrane-associative activity as either non-binding, curvature sensing, or membrane binding. Moreover, this method can be applied to detect membrane-interaction regions in a wide variety of proteins, with comparable predictive performance as state-of-the-art data-driven tools like DREAMM, PPM3, and MODA, but with a wider applicability regarding protein diversity, and the added feature to distinguish curvature sensing from general membrane binding. AVAILABILITY AND IMPLEMENTATION: We made these tools available as a web server, coined Protein-Membrane Interaction predictor (PMIpred), which can be accessed at https://pmipred.fkt.physik.tu-dortmund.de.
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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.