Login Register Cart 0
Generic placeholder image

Current Chinese Science

Editor-in-Chief

ISSN (Print): 2210-2981
ISSN (Online): 2210-2914

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article Section: Bioinformatics

Identifying Native and Non-native Membrane Protein Loops by Using Stabilizing Energetic Terms of Three Popular Force Fields

Author(s): Konda Mani Saravanan, Haiping Zhang and Yanjie Wei*

Volume 1, Issue 1, 2021

Published on: 29 July, 2020

Page: [14 - 21] Pages: 8

DOI: 10.2174/2665997201999200729165146

Abstract

Background: Predicting the three-dimensional structure of globular proteins from their amino acid sequence has reached a fair accuracy, but predicting the structure of membrane proteins, especially loop regions, is still a difficult task in structural bioinformatics. The difficulty in predicting membrane loops is due to various factors like length variation, position, flexibility, and they are easily prone to mutation.

Objective: In the present work, we address the problem of identifying and ranking near-native loops from a set of decoys generated by Monte-Carlo simulations.

Methods: We systematically analyzed native and generated non-native decoys to develop a scoring function. The scoring function uses four important stabilizing energy terms from three popular force fields, such as FOLDX, OPLS, and AMBER, to identify and rank near-native membrane loops.

Results: The results reveal better discrimination of native and non-natives and perform poor prediction in binary classifying native and near-native defined based on Root Mean Square Deviation (RMSD), Global Distance Test (GDT), and Template Modeling (TM) score, respectively.

Conclusion: From our observations, we conclude that the important energy features described here may help to improve the loop prediction when the membrane protein database size increases.

Keywords: Loop conformation, membrane proteins, molecular force fields, structure prediction, molecular modeling, template modeling.

« Previous Next »
Graphical Abstract

[1]
White SH, Wimley WC. Membrane protein folding and stability: physical principles. Annu Rev Biophys Biomol Struct 1999; 28(1): 319-65.
[http://dx.doi.org/10.1146/annurev.biophys.28.1.319] [PMID: 10410805]
[2]
Bowie JU. Solving the membrane protein folding problem. Nature 2005; 438(7068): 581-9.
[http://dx.doi.org/10.1038/nature04395] [PMID: 16319877]
[3]
Soto CS, Fasnacht M, Zhu J, Forrest L, Honig B. Loop modeling: Sampling, filtering, and scoring. Proteins 2008; 70(3): 834-43.
[http://dx.doi.org/10.1002/prot.21612] [PMID: 17729286]
[4]
White SH. How hydrogen bonds shape membrane protein structure. Adv Protein Chem 2005; 72: 157-72.
[http://dx.doi.org/10.1016/S0065-3233(05)72006-4] [PMID: 16581376]
[5]
Hildebrand PW, Günther S, Goede A, Forrest L, Frömmel C, Preissner R. Hydrogen-bonding and packing features of membrane proteins: functional implications. Biophys J 2008; 94(6): 1945-53.
[http://dx.doi.org/10.1529/biophysj.107.110395] [PMID: 17921213]
[6]
Toal S, Schweitzer-Stenner R. Local order in the unfolded state: conformational biases and nearest neighbor interactions. Biomolecules 2014; 4(3): 725-73.
[http://dx.doi.org/10.3390/biom4030725] [PMID: 25062017]
[7]
Olson MA, Feig M, Brooks CL III. Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions. J Comput Chem 2008; 29(5): 820-31.
[http://dx.doi.org/10.1002/jcc.20827] [PMID: 17876760]
[8]
Hildebrand PW, Goede A, Bauer RA, Gruening B, Ismer J, Michalsky E. SuperLooper - A prediction server for the modeling of loops in globular and membrane proteins. Nucleic Acids Res 2009; 37(Webserver): 571-4.
[http://dx.doi.org/10.1093/nar/gkp338] [PMID: 19429894]
[9]
Fiser A, Sali A. ModLoop: automated modeling of loops in protein structures. Bioinformatics 2003; 19(18): 2500-1.
[http://dx.doi.org/10.1093/bioinformatics/btg362] [PMID: 14668246]
[10]
Fernandez-Fuentes N, Zhai J, Fiser A. ArchPRED: A template based loop structure prediction server Nucleic Acids Res 2006; 34(Webserver): 173-6.
[http://dx.doi.org/10.1093/nar/gkl113]
[11]
Marks C, Nowak J, Klostermann S, et al. Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction. Bioinformatics 2017; 33(9): 1346-53.
[http://dx.doi.org/10.1093/bioinformatics/btw823] [PMID: 28453681]
[12]
Kelm S, Vangone A, Choi Y, Ebejer JP, Shi J, Deane CM. Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future. Proteins 2014; 82(2): 175-86.
[http://dx.doi.org/10.1002/prot.24299] [PMID: 23589399]
[13]
Choi Y, Deane CM. FREAD revisited: Accurate loop structure prediction using a database search algorithm. Proteins 2010; 78(6): 1431-40.
[http://dx.doi.org/10.1002/prot.22658] [PMID: 20034110]
[14]
Marks C, Deane C, Shi J. Hybrid methods for protein loop modelling ORA Deposit 2016 Available at. ora.ox.ac.uk/objects/uuid:5c0d6343-480e-4de5-a99f-3ca9b5d065c5
[15]
Kim DE, Blum B, Bradley P, Baker D. Sampling bottlenecks in de novo protein structure prediction. J Mol Biol 2009; 393(1): 249-60.
[http://dx.doi.org/10.1016/j.jmb.2009.07.063] [PMID: 19646450]
[16]
Lee J, Lee D, Park H, Coutsias EA, Seok C. Protein loop modeling by using fragment assembly and analytical loop closure. Proteins 2010; 78(16): 3428-36.
[http://dx.doi.org/10.1002/prot.22849] [PMID: 20872556]
[17]
Heo S, Lee J, Joo K, Shin HC, Lee J. Protein loop structure prediction using conformational space annealing. J Chem Inf Model 2017; 57(5): 1068-78.
[http://dx.doi.org/10.1021/acs.jcim.6b00742] [PMID: 28398048]
[18]
Hornak V, Simmerling C. Generation of accurate protein loop conformations through low-barrier molecular dynamics. Proteins 2003; 51(4): 577-90.
[http://dx.doi.org/10.1002/prot.10363] [PMID: 12784217]
[19]
Karami Y, Guyon F, De Vries S, Tufféry P. DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins. Sci Rep 2018; 8(1): 13673.
[http://dx.doi.org/10.1038/s41598-018-32079-w] [PMID: 30209260]
[20]
Yasuda S, Yoshidome T, Harano Y, et al. Free-energy function for discriminating the native fold of a protein from misfolded decoys. Proteins 2011; 79(7): 2161-71.
[http://dx.doi.org/10.1002/prot.23036] [PMID: 21557318]
[21]
Bagler G, Sinha S. Network properties of protein structures. Stat Mech Appl 2005; 346(1-2): 27-33.
[http://dx.doi.org/10.1016/j.physa.2004.08.046]
[22]
Vishveshwara S, Ghosh A, Hansia P. Intra and inter-molecular communications through protein structure network. Curr Protein Pept Sci 2009; 10(2): 146-60.
[http://dx.doi.org/10.2174/138920309787847590] [PMID: 19355982]
[23]
Wang S, Li Z, Yu Y, Xu J. Folding membrane proteins by deep transfer learning. Cell Syst 2017; 5(3): 202-211.e3.
[http://dx.doi.org/10.1016/j.cels.2017.09.001] [PMID: 28957654]
[24]
Michalsky E, Goede A, Preissner R. Loops in proteins (LIP)--a comprehensive loop database for homology modelling. Protein Eng 2003; 16(12): 979-85.
[http://dx.doi.org/10.1093/protein/gzg119] [PMID: 14983078]
[25]
Snow CD, Sorin EJ, Rhee YM, Pande VS. How well can simulation predict protein folding kinetics and thermodynamics? Annu Rev Biophys Biomol Struct 2005; 34: 43-69.
[http://dx.doi.org/10.1146/annurev.biophys.34.040204.144447] [PMID: 15869383]
[26]
Saravanan KM, Krishnaswamy S. Analysis of dihedral angle preferences for alanine and glycine residues in alpha and beta transmembrane regions. J Biomol Struct Dyn 2015; 33(3): 552-62.
[http://dx.doi.org/10.1080/07391102.2014.895678]] [PMID: 24625248]
[27]
Saravanan KM, Selvaraj S. Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins. J Biol Phys 2017; 43(2): 265-78.
[http://dx.doi.org/10.1007/s10867-017-9451-x]] [PMID: 28577238]
[28]
Saravanan KM, Peng Y, Wei Y. Systematic analysis of NO regular secondary structural regions (NORS) in membrane and non-membrane proteins. J Biomol Struct Dyn 2020; 38(1): 268-74.
[http://dx.doi.org/10.1080/07391102.2019.1566092]] [PMID: 30616457]
[29]
Wei Y, Floudas CA. Enhanced inter-helical residue contact prediction in transmembrane proteins. Chem Eng Sci 2011; 66(19): 4356-69.
[http://dx.doi.org/10.1016/j.ces.2011.04.033] [PMID: 21892227]
[30]
Zhang H, Huang Q, Bei Z, Wei Y, Floudas CA. COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming. Proteins 2016; 84(3): 332-48.
[http://dx.doi.org/10.1002/prot.24979] [PMID: 26756402]
[31]
Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: An online force field. Nucleic Acids Res 2005; 33(Webserver): 382-8.
[http://dx.doi.org/10.1093/nar/gki387] [PMID: 15980494]
[32]
Kaminski GA, Friesner RA, Tirado-Rives J. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 2001; 105(28): 6474-87.
[http://dx.doi.org/10.1021/jp003919d]
[33]
Case DA, Cheatham TE III, Darden T, et al. The Amber biomolecular simulation programs. J Comput Chem 2005; 26(16): 1668-88.
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[34]
Tusnady GE, Dosztanyi Z, Simon I. PDB_TM: Selection and membrane localization of transmembrane proteins in the protein data bank. Nucleic Acids Res 2005; 33(Database): D275-278.
[http://dx.doi.org/10.1093/nar/gki002]
[35]
Lomize MA, Lomize AL, Pogozheva ID, Mosberg HI. OPM: orientations of proteins in membranes database. Bioinformatics 2006; 22(5): 623-5.
[http://dx.doi.org/10.1093/bioinformatics/btk023] [PMID: 16397007]
[36]
Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res 2000; 28(1): 235-42.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[37]
Hill JR, Kelm S, Shi J, Deane CM. Environment specific substitution tables improve membrane protein alignment. Bioinformatics 2011; 27(13): i15-23.
[http://dx.doi.org/10.1093/bioinformatics/btr230] [PMID: 21685065]
[38]
Tang K, Zhang J, Liang J. Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method. PLOS Comput Biol 2014; 10(4)e1003539
[http://dx.doi.org/10.1371/journal.pcbi.1003539] [PMID: 24763317]
[39]
Li SC, Ng YK. Calibur: a tool for clustering large numbers of protein decoys. BMC Bioinformatics 2010; 11: 25.
[http://dx.doi.org/10.1186/1471-2105-11-25] [PMID: 20070892]
[40]
Gromiha MM, Selvaraj S. Inter-residue interactions in protein folding and stability. Prog Biophys Mol Biol 2004; 86(2): 235-77.
[http://dx.doi.org/10.1016/j.pbiomolbio.2003.09.003] [PMID: 15288760]
[41]
Zhou HX, Pang X. Electrostatic interactions in protein structure, folding, binding, and condensation. Chem Rev 2018; 118(4): 1691-741.
[http://dx.doi.org/10.1021/acs.chemrev.7b00305] [PMID: 29319301]
[42]
Barratt E, Bingham RJ, Warner DJ, Laughton CA, Phillips SEV, Homans SW. Van der Waals interactions dominate ligand-protein association in a protein binding site occluded from solvent water. J Am Chem Soc 2005; 127(33): 11827-34.
[http://dx.doi.org/10.1021/ja0527525] [PMID: 16104761]
[43]
Davis CM, Gruebele M, Sukenik S. How does solvation in the cell affect protein folding and binding? Curr Opin Struct Biol 2018; 48: 23-9.
[http://dx.doi.org/10.1016/j.sbi.2017.09.003] [PMID: 29035742]
[44]
Caro JA, Harpole KW, Kasinath V, et al. Entropy in molecular recognition by proteins. Proc Natl Acad Sci USA 2017; 114(25): 6563-8.
[http://dx.doi.org/10.1073/pnas.1621154114] [PMID: 28584100]
[45]
Pace CN, Shirley BA, McNutt M, Gajiwala K. Forces contributing to the conformational stability of proteins. FASEB J 1996; 10(1): 75-83.
[http://dx.doi.org/10.1096/fasebj.10.1.8566551] [PMID: 8566551]
[46]
Senior AW, Evans R, Jumper J, et al. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins 2019; 87(12): 1141-8.
[http://dx.doi.org/10.1002/prot.25834] [PMID: 31602685]
[47]
Jorgensen WL, Maxwell DS, Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 1996; 118(45): 11225-36.
[http://dx.doi.org/10.1021/ja9621760]

Rights & Permissions Print Cite
Article Metrics
8
© 2024 Bentham Science Publishers | Privacy Policy