Cited by 41Machine Learning in BioinformaticsLipid Membrane Structure and BehaviorProtein Structure and Dynamics

PMIpred: a physics-informed web server for quantitative protein–membrane interaction prediction

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van Hilten, Niek, Verwei, Nino, Methorst, Jeroen, Nase, Carsten, Bernatavicius, Andrius, Risselada, Herre Jelger·2024·Bioinformatics

Published version of a preprint

Abstract

Motivation: Many 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. Results: Here, 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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Authors
van Hilten, N., Verwei, N., Methorst, J., Nase, C., Bernatavicius, A., & Risselada, H. J.
Affiliations
  • Leiden Institute of Chemistry, Leiden University , Leiden 2333 CC, Netherlands
  • Leiden University (NL)
  • TU Dortmund University (DE)
Corresponding author
Risselada, Herre Jelger
Venue
Bioinformatics
Published
2024-02-01
Volume
40
Issue
2
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W4391575167
Cited by
41
References
77
Citations by year
2026: 23 · 2025: 8 · 2024: 10
FWCI
5.58
Type
journal article
Language
en
Open access
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Dutch Research Organisation
  • Deutsche Forschungsgemeinschaft
  • NWO Vidi scheme
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Lipid Membrane Structure and Behavior
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
protein-membrane interactions, curvature sensing, peptide binding prediction, physics-informed machine learning, transformer models, molecular dynamics, web server
MeSH terms
Amino Acid Sequence, Membrane Proteins, Peptides, Physics, Neural Networks, Computer

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