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Cited by 18Protein Structure and DynamicsMachine Learning in BioinformaticsLipid Membrane Structure and Behavior

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Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins

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Wang, Lei, Zhang, Jiangguo, Wang, Dali, Song, Chen·2022·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1009972PMC PMC9000120

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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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Metadata

Authors
Wang, L., Zhang, J., Wang, D., & Song, C.
Affiliations
Peking University (CN)
Corresponding author
Song, Chen
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2022-03-30
Volume
18
Issue
3
Pages
e1009972
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1009972
PMC
PMC9000120
OpenAlex
W4220830943
Cited by
18
References
6
Citations by year
2026: 6 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 2
FWCI
0.98
Type
journal article
Language
en
Update notices
new_version (2022-04-11)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Ministry of Science and Technology of the People’s Republic of China (2021YFE0108100)
  • National Natural Science Foundation of China (21873006, 32071251)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • 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
Keywords
membrane proteins, lipid bilayers, hydrophobic amino acids, solvent accessibility, protein structure prediction, machine learning prediction, molecular dynamics simulation, protein contact maps
MeSH terms
Amino Acids, Lipid Bilayers, Membrane Proteins, Probability, Molecular Dynamics Simulation

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