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

Bioinformatics, vol. 40

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.

Authors 6

1. Niek van Hilten Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Chemistry, Leiden University , Leiden 2333 CC, Netherlands

2. Nino Verwei Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Chemistry, Leiden University , Leiden 2333 CC, Netherlands

3. Jeroen Methorst Aachen

   Leiden University

   Affiliation as printed

   Leiden Institute of Chemistry, Leiden University , Leiden 2333 CC, Netherlands

4. Carsten Nase

   TU Dortmund University

   Affiliation as printed

   Department of Physics, Technical University Dortmund , Dortmund 44227, Germany

   Department of Physics, Technical University Dortmund, Dortmund, 44227, Germany

5. Andrius Bernatavicius Aachen Leiden Academic Centre for Drug Research Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Leiden University , Leiden 2333 CC, Netherlands

   Leiden Institute of Advanced Computer Science, Leiden University , Leiden 2333 CA, Netherlands

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, Netherlands

6. Herre Jelger Risselada corresponding Aachen

   Leiden University · TU Dortmund University

   Affiliation as printed

   Department of Physics, Technical University Dortmund , Dortmund 44227, Germany

   Leiden Institute of Chemistry, Leiden University , Leiden 2333 CC, Netherlands

   Department of Physics, Technical University Dortmund, Dortmund, 44227, Germany