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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

Open access
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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Fig 1 The method schemes. The overall scheme is presented in the black dashed rectangular in the top panel, and the key steps are described in: (A) Extraction of MCP from the MemProtMD database. (B) The DCRNN model for the MCP prediction. (C) The process of MCP prediction from a query sequence. (D) The method of MCP incorporated into the ResNet model. The MCP was used as a 1D input in the same way as SA. (E) The unit of the ResNet model. Each unit of the ResNet model contains two convolution layers and two activation layers.
Fig 1 The method schemes. The overall scheme is presented in the black dashed rectangular in the top panel, and the key steps are described in: (A) Extraction of MCP from the MemProtMD database. (B) The DCRNN model for the MCP prediction. (C) The process of MCP prediction from a query sequence. (D) The method of MCP incorporated into the ResNet model. The MCP was used as a 1D input in the same way as SA. (E) The unit of the ResNet model. Each unit of the ResNet model contains two convolution layers and two activation layers.CC BY · PubMed Central
Fig 2 Membrane contact probability (MCP) of two representative membrane proteins. (A-B) Comparison between the MD observation (cyan) and the prediction (red) of the MCPs. (C-D), Side and top views of the two representative proteins. (E-F), The outer surface of the representative membrane proteins, colored according to the MCP values obtained from MD simulations. (G-H), Similar to (E-F), but colored according to the predicted MCP values. (I-J), Similar to (E-F), but colored according to the predicted SA values by RaptorX. The PDB ID of the α-helix membrane protein was 5aym [39], representing a crystal structure of a bacterial homologue of iron transporter ferroportin in the outward-facing state with soaked iron with more than 10 transmembrane helices and 440 residues. The PDB ID of the β-sheet membrane protein was 4e1t [40], an X-ray crystal structure of the transmembrane beta-domain from invasion from Yersinia pseudotuberculosis with 245 residues.
Fig 2 Membrane contact probability (MCP) of two representative membrane proteins. (A-B) Comparison between the MD observation (cyan) and the prediction (red) of the MCPs. (C-D), Side and top views of the two representative proteins. (E-F), The outer surface of the representative membrane proteins, colored according to the MCP values obtained from MD simulations. (G-H), Similar to (E-F), but colored according to the predicted MCP values. (I-J), Similar to (E-F), but colored according to the predicted SA values by RaptorX. The PDB ID of the α-helix membrane protein was 5aym [39], representing a crystal structure of a bacterial homologue of iron transporter ferroportin in the outward-facing state with soaked iron with more than 10 transmembrane helices and 440 residues. The PDB ID of the β-sheet membrane protein was 4e1t [40], an X-ray crystal structure of the transmembrane beta-domain from invasion from Yersinia pseudotuberculosis with 245 residues.CC BY · PubMed Central
Fig 3 The 2D plot of the complementary MCP and SA predicting the likely location of amino acids in a protein, for the 327-protein dataset (A) and 102 Pfam protein dataset (B), respectively. The figures are roughly divided into four regions: (I) Both the MCP and SA values are larger than 0.5, indicating the amino acids in this region are likely to be exposed to both water and lipid molecules; (II) MCP >0.5 and SA <0.5, meaning the amino acids in this region are more likely to be exposed to hydrophobic lipid molecules than to water molecules; (III) MCP <0.5 and SA <0.5, meaning the amino acids are not likely to be exposed to either lipid or water molecules, so they are probably embedded in the interior of proteins; and (IV) MCP <0.5 and SA >0.5, meaning the amino acids are more likely to be exposed to water molecules than to the lipid bilayer.
Fig 3 The 2D plot of the complementary MCP and SA predicting the likely location of amino acids in a protein, for the 327-protein dataset (A) and 102 Pfam protein dataset (B), respectively. The figures are roughly divided into four regions: (I) Both the MCP and SA values are larger than 0.5, indicating the amino acids in this region are likely to be exposed to both water and lipid molecules; (II) MCP >0.5 and SA <0.5, meaning the amino acids in this region are more likely to be exposed to hydrophobic lipid molecules than to water molecules; (III) MCP <0.5 and SA <0.5, meaning the amino acids are not likely to be exposed to either lipid or water molecules, so they are probably embedded in the interior of proteins; and (IV) MCP <0.5 and SA >0.5, meaning the amino acids are more likely to be exposed to water molecules than to the lipid bilayer.CC BY · PubMed Central
Fig 4 Membrane contact probability (MCP) of two representative non-transmembrane proteins. (A-B) Comparison between the MD observation (cyan) and the prediction (red) of the MCPs. (C-D), Side and top views of the two representative proteins. (E-F), The outer surface of the representative proteins, colored according to the MCP values obtained from MD simulations. (G-H), Similar to (E-F), but colored according to the predicted MCP values. (I-J), Similar to (E-F), but colored according to the predicted SA values by RaptorX. The PDB ID of the half-membrane-embedded protein was 5nup, which is an X-ray crystal structure of the Gram-negative bacterial α-helical outer membrane (OM) protein composed of 236 residues. The PDB ID of the soluble protein was 1a70, which is an X-ray crystal structure of ferredoxin I (Fd I) from Spinacia oleracea with 97 residues.
Fig 4 Membrane contact probability (MCP) of two representative non-transmembrane proteins. (A-B) Comparison between the MD observation (cyan) and the prediction (red) of the MCPs. (C-D), Side and top views of the two representative proteins. (E-F), The outer surface of the representative proteins, colored according to the MCP values obtained from MD simulations. (G-H), Similar to (E-F), but colored according to the predicted MCP values. (I-J), Similar to (E-F), but colored according to the predicted SA values by RaptorX. The PDB ID of the half-membrane-embedded protein was 5nup, which is an X-ray crystal structure of the Gram-negative bacterial α-helical outer membrane (OM) protein composed of 236 residues. The PDB ID of the soluble protein was 1a70, which is an X-ray crystal structure of ferredoxin I (Fd I) from Spinacia oleracea with 97 residues.CC BY · PubMed Central
Fig 5 The Sar1 structure (1f6b) anchored on a membrane. (A) The initial and (B) final structures of the 1f6b system in the coarse-grained self-assembly MD simulations. (C) The atomistic structure of 1f6b transformed from the CG MD simulation outcome (B). The orange and grey spheres represent the lipid head groups and hydrophobic tails, respectively. The red arrow represents the first principal axis of the protein. Water molecules were filled in the whole simulation box, but are not shown here for clarity. (D) The distance between the COM of the protein and the COM of the bilayer in the nine CG simulation trajectories. (E) The orientation of 1f6b in the trajectories, represented by the angle between the first principal axis of the protein structure and the Z-axis of the simulation system. (F) Similar to (D), (G) similar to (E), but obtained from three atomistic MD simulations. (H) The orientation of 1f6b bound to the lipid bilayer obtained from MD simulations, with the membrane-interacting residues colored according to panel (I). (I) The overlaid MCP generated by our MCP predictor (red) and MD simulations (blue).
Fig 5 The Sar1 structure (1f6b) anchored on a membrane. (A) The initial and (B) final structures of the 1f6b system in the coarse-grained self-assembly MD simulations. (C) The atomistic structure of 1f6b transformed from the CG MD simulation outcome (B). The orange and grey spheres represent the lipid head groups and hydrophobic tails, respectively. The red arrow represents the first principal axis of the protein. Water molecules were filled in the whole simulation box, but are not shown here for clarity. (D) The distance between the COM of the protein and the COM of the bilayer in the nine CG simulation trajectories. (E) The orientation of 1f6b in the trajectories, represented by the angle between the first principal axis of the protein structure and the Z-axis of the simulation system. (F) Similar to (D), (G) similar to (E), but obtained from three atomistic MD simulations. (H) The orientation of 1f6b bound to the lipid bilayer obtained from MD simulations, with the membrane-interacting residues colored according to panel (I). (I) The overlaid MCP generated by our MCP predictor (red) and MD simulations (blue).CC BY · PubMed Central
Fig 6 Incorporation of the predicted MCP improved the CM prediction for the two representative cases. The cutoff for the CM prediction was 8 Å between β carbon atoms. (A), (B) The predicted CMs for 5aym without and with MCP incorporated, respectively. (C) The native CM calculated from the known structure 5aym. (D-F) Similar to (A-C), but for the protein 4e1t.
Fig 6 Incorporation of the predicted MCP improved the CM prediction for the two representative cases. The cutoff for the CM prediction was 8 Å between β carbon atoms. (A), (B) The predicted CMs for 5aym without and with MCP incorporated, respectively. (C) The native CM calculated from the known structure 5aym. (D-F) Similar to (A-C), but for the protein 4e1t.CC BY · PubMed Central
Fig 7 The MCP-incorporated CM predictions improved the protein structure prediction accuracy for the two representative cases. (A), (B) The predicted protein structures for 5aym using the predicted CMs without and with MCP incorporated, respectively. The crystal structure was shown in cyan and the predicted structures were shown in grey. (C-D) Similar to (A-B), but for the protein structure 4e1t.
Fig 7 The MCP-incorporated CM predictions improved the protein structure prediction accuracy for the two representative cases. (A), (B) The predicted protein structures for 5aym using the predicted CMs without and with MCP incorporated, respectively. The crystal structure was shown in cyan and the predicted structures were shown in grey. (C-D) Similar to (A-B), but for the protein structure 4e1t.CC BY · PubMed Central

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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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