ArticlePLoS computational biology2022
Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 21 citations in OpenAlex.
- Deep learning-driven de novo discovery of binders targeting the transmembrane domain of BamA.Archives of microbiology · 2026Article
- RBMP2 shapes specialized membranes for CObioRxiv : the preprint server for biology · 2026Article
- PepMCP: a graph-based membrane contact probability predictor for membrane-lytic antimicrobial peptides.Bioinformatics (Oxford, England) · 2026Article
- Mechanism-Driven Screening of Membrane-Targeting and Pore-Forming Antimicrobial Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins.Molecules (Basel, Switzerland) · 2024Article
- Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction.Journal of chemical information and modeling · 2024Article
- Toward atomistic models of intact severe acute respiratory syndrome coronavirus 2 via Martini coarse-grained molecular dynamics simulations.Quantitative biology (Beijing, China) · 2023Article
- Article
- Influence of the extracellular domain size on the dynamic behavior of membrane proteins.Biophysical journal · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in the protein structure and function predictions. Here, we propose a characteristic and predictive quantity, the membrane contact probability (MCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that MCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. As the first of many potential applications, we demonstrate that MCP can be used to systematically improve the prediction precision of the protein contact maps and structures.
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