MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction
Corresponding Author: Hong-Bin Shen
Nano-Micro Letters,
Vol. 10 No. 1 (2018), Article Number: 2
Abstract
Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein’s structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/.
Highlights:
1 MemBrain is a fully automatic online tool for transmembrane protein structure prediction, which is able to predict the irregular half-transmembrane helix.
2 MemBrain’s theoretic predictions provide timely and important clues for further wet-lab experiments.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne, The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000). doi:10.1093/nar/28.1.235
- M. Cserzö, E. Wallin, I. Simon, G. von Heijne, A. Elofsson, Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. Protein Eng. 10(6), 673–676 (1997). doi:10.1093/protein/10.6.673
- A.L. Hopkins, C.R. Groom, The druggable genome. Nat. Rev. Drug Discov. 1(9), 727–730 (2002). doi:10.1038/nrd892
- H.B. Shen, J. Yang, K.C. Chou, Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. J. Theor. Biol. 240(1), 9–13 (2006). doi:10.1038/nrd897
- MathSciNet
- K.C. Chou, H.B. Shen, MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun. 360(2), 339–345 (2007). doi:10.1016/j.bbrc.2007.06.027
- H.B. Shen, J.J. Chou, MemBrain: improving the accuracy of predicting transmembrane helices. PLoS ONE 3(6), e2399 (2008). doi:10.1371/journal.pone.0002399
- J. Yang, R. Jang, Y. Zhang, H.B. Shen, High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling. Bioinformatics 29(20), 2579–2587 (2013). doi:10.1093/bioinformatics/btt440
- F. Xiao, H.B. Shen, Prediction enhancement of residue real-value relative accessible surface area in transmembrane helical proteins by solving the output preference problem of machine learning-based predictors. J. Chem. Inf. Model. 55(11), 2464–2474 (2015). doi:10.1021/acs.jcim.5b00246
- X. Yin, Y.Y. Xu, H.B. Shen, Enhancing the prediction of transmembrane β-barrel segments with chain learning and feature sparse representation. IEEE/ACM Trans. Comput. Biol. 13(6), 1016–1026 (2016). doi:10.1109/TCBB.2016.2528000
- A. Krogh, B. Larsson, H.G. Von, E.L. Sonnhammer, Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001). doi:10.1006/jmbi.2000.4315
- Z. Yuan, J.S. Mattick, R.D. Teasdale, SVMtm: support vector machines to predict transmembrane segments. J. Comput. Chem. 25, 632–636 (2004). doi:10.1002/jcc.10411
- D.T. Jones, D.W.A. Buchan, D. Cozzetto, M. Pontil, PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28(2), 184–190 (2012). doi:10.1093/bioinformatics/btr638
- A. Fuchs, A. Kirschner, D. Frishman, Prediction of helix–helix contacts and interacting helices in polytopic membrane proteins using neural networks. Proteins 74, 857–871 (2009). doi:10.1002/prot.22194
- N. Timothy, D.T. Jones, Predicting transmembrane helix packing arrangements using residue contacts and a force-directed algorithm. PLoS Comput. Biol. 6, e1000714 (2010). doi:10.1371/journal.pcbi.1000714
- J. Yang, Q.Y. Jin, B. Zhang, H.B. Shen, R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter. Bioinformatics 32(16), 2435–2443 (2016). doi:10.1093/bioinformatics/btw181
- J. Sim, S.Y. Kim, J. Lee, Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21(12), 2844–2849 (2005). doi:10.1093/bioinformatics/bti423
- E. Durham, B. Dorr, N. Woetzel, R. Staritzbichler, J. Meiler, Solvent accessible surface area approximations for rapid and accurate protein structure prediction. J. Mol. Model. 15(9), 1093–1108 (2009). doi:10.1007/s00894-009-0454-9
- S.F. Altschul, T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang, W. Miller, D.J. Lipman, Gapped BLAST and PSI-BLAST: a new generation of protein database search. Nucleic Acids Res. 25(17), 3389–3402 (1997). doi:10.1093/nar/25.17.3389
- A. Bairoch, R. Apweiler, The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28(1), 45–48 (2000). doi:10.1093/nar/28.1.45
- J. Yang, B.J. He, R. Jang, Y. Zhang, H.B. Shen, Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins. Bioinformatics 31(23), 3773–3781 (2015). doi:10.1093/bioinformatics/btv459
- G.E. Tusnady, L. Kalmar, I. Simon, TOPDB: topology data bank of transmembrane proteins. Nucleic Acids Res. 36(suppl_1), D234–D239 (2007). doi:10.1093/nar/gkm751
- G.E. Tusnády, Z. Dosztányi, I. Simon, PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank. Nucleic Acids Res. 33, 275–278 (2005). doi:10.1093/nar/gki002
- M.A. Lomize, A.L. Lomize, I.D. Pogozheva, OPM: orientations of proteins in membranes database. Bioinformatics 22(5), 623–625 (2006). doi:10.1093/bioinformatics/btk023
- M.S. Taylor, T.R. Ruch, P.Y. Hsiao, Y. Hwang, P.F. Zhang et al., Architectural organization of the metabolic regulatory enzyme ghrelin O-acyltransferase. J. Biol. Chem. 288(45), 32211–32228 (2013). doi:10.1074/jbc.M113.510313
- F. Kallenberg, S. Dintner, R. Schmitz, S. Gebhard, Identification of regions important for resistance and signalling within the antimicrobial peptide transporter BceAB of Bacillus subtilis. J. Bacteriol. 195(14), 3287–3297 (2013). doi:10.1128/JB.00419-13
- G.A. Morrill, A.B. Kostellow, L.J. Liu, R.K. Gupta, Evolution of the α-Subunit of Na/K-ATPase from Paramecium to Homo sapiens: invariance of transmembrane helix topology. J. Mol. Evol. 82(4–5), 183–198 (2016). doi:10.1007/s00239-016-9732-1
- P.D. Lena, K. Nagata, P. Baldi, Deep architecture for protein contact map prediction. Bioinformatics 28(19), 2449–2457 (2012). doi:10.1093/bioinformatics/bts475
- S. Wang, S. Sun, Z. Li, R. Zhang, J. Xu, Accuracy de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput. Biol. 13(1), e1005324 (2012). doi:10.1371/journal.pcbi.1005324
References
H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne, The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000). doi:10.1093/nar/28.1.235
M. Cserzö, E. Wallin, I. Simon, G. von Heijne, A. Elofsson, Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. Protein Eng. 10(6), 673–676 (1997). doi:10.1093/protein/10.6.673
A.L. Hopkins, C.R. Groom, The druggable genome. Nat. Rev. Drug Discov. 1(9), 727–730 (2002). doi:10.1038/nrd892
H.B. Shen, J. Yang, K.C. Chou, Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. J. Theor. Biol. 240(1), 9–13 (2006). doi:10.1038/nrd897
MathSciNet
K.C. Chou, H.B. Shen, MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun. 360(2), 339–345 (2007). doi:10.1016/j.bbrc.2007.06.027
H.B. Shen, J.J. Chou, MemBrain: improving the accuracy of predicting transmembrane helices. PLoS ONE 3(6), e2399 (2008). doi:10.1371/journal.pone.0002399
J. Yang, R. Jang, Y. Zhang, H.B. Shen, High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling. Bioinformatics 29(20), 2579–2587 (2013). doi:10.1093/bioinformatics/btt440
F. Xiao, H.B. Shen, Prediction enhancement of residue real-value relative accessible surface area in transmembrane helical proteins by solving the output preference problem of machine learning-based predictors. J. Chem. Inf. Model. 55(11), 2464–2474 (2015). doi:10.1021/acs.jcim.5b00246
X. Yin, Y.Y. Xu, H.B. Shen, Enhancing the prediction of transmembrane β-barrel segments with chain learning and feature sparse representation. IEEE/ACM Trans. Comput. Biol. 13(6), 1016–1026 (2016). doi:10.1109/TCBB.2016.2528000
A. Krogh, B. Larsson, H.G. Von, E.L. Sonnhammer, Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001). doi:10.1006/jmbi.2000.4315
Z. Yuan, J.S. Mattick, R.D. Teasdale, SVMtm: support vector machines to predict transmembrane segments. J. Comput. Chem. 25, 632–636 (2004). doi:10.1002/jcc.10411
D.T. Jones, D.W.A. Buchan, D. Cozzetto, M. Pontil, PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28(2), 184–190 (2012). doi:10.1093/bioinformatics/btr638
A. Fuchs, A. Kirschner, D. Frishman, Prediction of helix–helix contacts and interacting helices in polytopic membrane proteins using neural networks. Proteins 74, 857–871 (2009). doi:10.1002/prot.22194
N. Timothy, D.T. Jones, Predicting transmembrane helix packing arrangements using residue contacts and a force-directed algorithm. PLoS Comput. Biol. 6, e1000714 (2010). doi:10.1371/journal.pcbi.1000714
J. Yang, Q.Y. Jin, B. Zhang, H.B. Shen, R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter. Bioinformatics 32(16), 2435–2443 (2016). doi:10.1093/bioinformatics/btw181
J. Sim, S.Y. Kim, J. Lee, Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21(12), 2844–2849 (2005). doi:10.1093/bioinformatics/bti423
E. Durham, B. Dorr, N. Woetzel, R. Staritzbichler, J. Meiler, Solvent accessible surface area approximations for rapid and accurate protein structure prediction. J. Mol. Model. 15(9), 1093–1108 (2009). doi:10.1007/s00894-009-0454-9
S.F. Altschul, T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang, W. Miller, D.J. Lipman, Gapped BLAST and PSI-BLAST: a new generation of protein database search. Nucleic Acids Res. 25(17), 3389–3402 (1997). doi:10.1093/nar/25.17.3389
A. Bairoch, R. Apweiler, The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28(1), 45–48 (2000). doi:10.1093/nar/28.1.45
J. Yang, B.J. He, R. Jang, Y. Zhang, H.B. Shen, Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins. Bioinformatics 31(23), 3773–3781 (2015). doi:10.1093/bioinformatics/btv459
G.E. Tusnady, L. Kalmar, I. Simon, TOPDB: topology data bank of transmembrane proteins. Nucleic Acids Res. 36(suppl_1), D234–D239 (2007). doi:10.1093/nar/gkm751
G.E. Tusnády, Z. Dosztányi, I. Simon, PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank. Nucleic Acids Res. 33, 275–278 (2005). doi:10.1093/nar/gki002
M.A. Lomize, A.L. Lomize, I.D. Pogozheva, OPM: orientations of proteins in membranes database. Bioinformatics 22(5), 623–625 (2006). doi:10.1093/bioinformatics/btk023
M.S. Taylor, T.R. Ruch, P.Y. Hsiao, Y. Hwang, P.F. Zhang et al., Architectural organization of the metabolic regulatory enzyme ghrelin O-acyltransferase. J. Biol. Chem. 288(45), 32211–32228 (2013). doi:10.1074/jbc.M113.510313
F. Kallenberg, S. Dintner, R. Schmitz, S. Gebhard, Identification of regions important for resistance and signalling within the antimicrobial peptide transporter BceAB of Bacillus subtilis. J. Bacteriol. 195(14), 3287–3297 (2013). doi:10.1128/JB.00419-13
G.A. Morrill, A.B. Kostellow, L.J. Liu, R.K. Gupta, Evolution of the α-Subunit of Na/K-ATPase from Paramecium to Homo sapiens: invariance of transmembrane helix topology. J. Mol. Evol. 82(4–5), 183–198 (2016). doi:10.1007/s00239-016-9732-1
P.D. Lena, K. Nagata, P. Baldi, Deep architecture for protein contact map prediction. Bioinformatics 28(19), 2449–2457 (2012). doi:10.1093/bioinformatics/bts475
S. Wang, S. Sun, Z. Li, R. Zhang, J. Xu, Accuracy de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput. Biol. 13(1), e1005324 (2012). doi:10.1371/journal.pcbi.1005324