A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

Author(s): Elaheh Kashani-Amin, Ozra Tabatabaei-Malazy, Amirhossein Sakhteman, Bagher Larijani, Azadeh Ebrahim-Habibi*.

Journal Name: Current Drug Discovery Technologies

Volume 16 , Issue 2 , 2019

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Graphical Abstract:


Abstract:

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Keywords: Secondary structure prediction, systematic review, protein, PSIPRED, JPred, PHD.

[1]
Onuchic JN, Wolynes PG. Theory of protein folding. Curr Opin Struct Biol 2004; 14(1): 70-5.
[2]
Grinter S, Zou X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules 2014; 19(7): 10150.
[3]
Lee D, Redfern O, Orengo C. Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 2007; 8(12): 995-1005.
[4]
Pollastri G, McLysaght A. Porter: A new, accurate server for protein secondary structure prediction. Bioinformatics 2005; 21(8): 1719-20.
[5]
Yoo PD, Zhou BB, Zomaya AY. Machine learning techniques for protein secondary structure prediction: An overview and evaluation. Curr Bioinform 2008; 3(2): 74-86.
[6]
Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y. SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 2012; 33(3): 259-67.
[7]
Pauling L, Corey RB, Branson HR. The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc Natl Acad Sci 1951; 37(4): 205-11.
[8]
Joseph AP, de Brevern AG. From local structure to a global framework: recognition of protein folds. Interface Focus 2014; 11(95): 20131147.
[9]
Singh M. Predicting protein secondary and supersecondary structureHandbook of Computational Molecular Biology: Chapman and Hall/CRC; 2005 p 29-1 to 29-23.
[10]
Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen‐bonded and geometrical features. Biopolymers 1983; 22(12): 2577-637.
[11]
Yaseen A, Li YH. Template-based C8-SCORPION: A protein 8-state secondary structure prediction method using structural information and context-based features. BMC Bioinformatics 2014; 15(Suppl. 8): S3.
[12]
Chou PY, Fasman GD. Conformational parameters for amino acids in helical, β-sheet, and random coil regions calculated from proteins. Biochemistry 1974; 13(2): 211-22.
[13]
Garnier J, Osguthorpe DJ, Robson B. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 1978; 120(1): 97-120.
[14]
Lim V. Structural principles of the globular organization of protein chains. A stereochemical theory of globular protein secondary structure. J Mol Biol 1974; 88(4): 857IN9863-862872.
[15]
Garnier J, Gibrat J-F, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 1996; 266: 540-53.
[16]
Zemla A, Venclovas Č, Fidelis K, Rost B. A modified definition of Sov, a segment‐based measure for protein secondary structure prediction assessment. Proteins: Struct Funct Bioinf 1999; 34(2): 220-3.
[17]
Yaseen A, Li Y. Context-based features enhance protein secondary structure prediction accuracy. J Chem Inf Model 2014; 54(3): 992-1002.
[18]
Muto T, Tsuchiya D, Morikawa K, Jingami H. Structures of the extracellular regions of the group II/III metabotropic glutamate receptors. Proc Natl Acad Sci 2007; 104(10): 3759-64.
[19]
Wu H, Wang C, Gregory KJ, et al. Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator. Science 2014; 344(6179): 58-64.
[20]
Das S, Orengo CA. Protein function annotation using protein domain family resources. Methods 2016; 93: 24-34.
[21]
Friesner RA, Abel R, Goldfeld DA, Miller EB, Murrett CS. Computational methods for high resolution prediction and refinement of protein structures. Curr Opin Struct Biol 2013; 23(2): 177-84.
[22]
Schmidt T, Bergner A, Schwede T. Modelling three-dimensional protein structures for applications in drug design. Drug Discov Today 2014; 19(7): 890-7.
[23]
Pasotti L, Zucca S. Advances and computational tools towards predictable design in biological engineering. Comput Math Methods Med 2014; 2014: 369681.
[24]
Szilagyi A, Zhang Y. Template-based structure modeling of protein–protein interactions. Curr Opin Struct Biol 2014; 24: 10-23.
[25]
Dorn M, Silva MB, Buriol LS, Lamb LC. Three-dimensional protein structure prediction: Methods and computational strategies. ‎. Comput Biol Chem 2014; 53: 251-76.
[26]
Grant MJ, Booth A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Info Libr J 2009; 26(2): 91-108.
[27]
Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 2009; 6(7): e1000097.
[28]
Pathak Y, Rana PS, Singh PK, Saraswat M. Protein structure prediction (RMSD <= 5 angstrom) using machine learning models. Int J Data Min Bioinform 2016; 14(1): 71-85.
[29]
Kang Y, Fortmann CM. An alternative approach to protein folding. BioMed Res Int 2013; 2013: 583045.
[30]
Islam MN, Iqbal S, Katebi AR, Hogue MT. A balanced secondary structure predictor. J Theor Biol 2016; 389: 60-71.
[31]
Elbashir MK, Sheng Y, Wang JX, Wu FX, Li M. Predicting beta-turns in protein using kernel logistic regression. BioMed Res Int 2013; 2013: 870372.
[32]
Belushkin AA, Vinogradov DV, Gelfand MS, Osterman AL, Cieplak P, Kazanov MD. Sequence-derived structural features driving proteolytic processing. Proteomics 2014; 14(1): 42-50.
[33]
McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics 2000; 16(4): 404-5.
[34]
Jones D. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292: 195-202.
[35]
Waespy M, Gbem TT, Elenschneider L, et al. Carbohydrate recognition specificity of trans-sialidase lectin domain from trypanosoma congolense. PLoS Negl Trop Dis 2015; 9(10): e0004120.
[36]
van den Boom J, Trusch F, Hoppstock L, Beuck C, Bayer P. Structural characterization of the loop at the alpha-subunit C-terminus of the mixed lineage leukemia protein activating protease taspase1. PLoS One 2016; 11(3): e0151431.
[37]
Schaller A, Connors NK, Oelmeier SA, Hubbuch J, Middelberg APJ. Predicting recombinant protein expression experiments using molecular dynamics simulation. Chem Eng Sci 2015; 121: 340-50.
[38]
Krieger E, Vriend G. YASARA View-molecular graphics for all devices-from smartphones to workstations. Bioinformatics 2014; 30(20): 2981-2.
[39]
Buchan DWA, Minneci F, Nugent TCO, Bryson K, Jones DT. Scalable web services for the PSIPRED Protein Analysis Workbench Nucleic Acids Res 2013; 41(Web Server issue): W349- W57
[40]
Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993; 232(2): 584-99.
[41]
Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins: Struct Funct Bioinf 2000; 40(3): 502-11.
[42]
Drozdetskiy A, Cole C, Procter J, Barton GJ. JPred4: A protein secondary structure prediction server. Nucleic Acids Res 2015; 43(W1): W389-94.
[43]
Schneider R. Sekundärstrukturvorhersage Von Proteinen unter Berücksichtigung von TertiärstrukturaspektenDepartment of Biology, Univ Heidelberg, FRG, Diploma thesis 1989.
[44]
Rost B, Sander C, Schneider R. PHD-an automatic mail server for protein secondary structure prediction. Computer applications in the biosciences. CABIOS 1994; 10(1): 53-60.
[45]
Rost B. How to use protein 1-D structure predicted by PROFphd. The proteomics protocols handbook. 2005:875-901.
[46]
Rost B, Liu J. The predictprotein server. Nucleic Acids Res 2003; 31(13): 3300-4.
[47]
Hobbs JR, Munger SD, Conn GL. Monellin (MNEI) at 1.15 Å resolution. Acta Crystallogr Sect F Struct Biol Cryst Commun 2007; 63(3): 162-7.
[48]
Ulrich A, Wahl MC. Structure and evolution of the spliceosomal peptidyl-prolyl cis-trans isomerase Cwc27. Acta Crystallogr Sect D 2014; 70(Pt 12): 3110-23.
[49]
Saravanan KM, Selvaraj S. Performance of secondary structure prediction methods on proteins containing structurally ambivalent sequence fragments. Biopolymers 2013; 100(2): 148-53.
[50]
Li H, Yang B, Xie Y, Qian W. A new FCM classifier model based on KDTICM. J Inf Comput Sci 2013; 10(9): 2601-9.
[51]
Pollastri G, Martin AJ, Mooney C, Vullo A. Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics 2007; 8(1): 201.
[52]
Baú D, Martin AJM, Mooney C, Vullo A, Walsh I, Pollastri G. Distill: A suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins. BMC Bioinformatics 2006; 7: 402.
[53]
Faraggi E, Yang Y, Zhang S, Zhou Y. Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction. Structure 2009; 17(11): 1515-27.
[54]
Zhang T, Faraggi E, Zhou Y. Fluctuations of backbone torsion angles obtained from NMR‐determined structures and their prediction. Proteins: Struct Funct Bioinf 2010; 78(16): 3353-62.
[55]
Heffernan R, Dehzangi A, Lyons J, et al. Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins. Bioinformatics 2016; 32(6): 843-9.
[56]
Heffernan R, Paliwal K, Lyons J, et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci Rep 2015; 5: 11476.
[57]
Gao J, Yang Y, Zhou Y. Predicting the errors of predicted local backbone angles and non-local solvent-accessibilities of proteins by deep neural networks. Bioinformatics 2016; 32(24): 3768-73.
[58]
Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y. SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chem 2012; 33(3): 259-67.
[59]
Pollastri G, Przybylski D, Rost B, Baldi P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 2002; 47: 228-35.
[60]
Meena LS, Meena J. Cloning and characterization of a novel PE_PGRS60 protein (Rv3652) of Mycobacterium tuberculosis H37 Rv exhibit fibronectin-binding property. Biotechnol Appl Biochem 2016; 63(4): 525-31.
[61]
Kieslich CA, Smadbeck J, Khoury GA, Floudas CA. conSSert: Consensus SVM model for accurate prediction of ordered secondary structure. J Chem Inf Model 2016; 56(3): 455-61.
[62]
Cheng J, Randall AZ, Sweredoski MJ, Baldi P. SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 2005; 33(Suppl. 2): W72-6.
[63]
Magnan CN, Baldi P. SSpro/ACCpro 5: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 2014; 30(18): 2592-7.
[64]
Abarca F, Gutierrez-Maldonado SE, Parada P, Martinez P, Maass A, Perez-Acle T. Insights on the structure and stability of Licanantase: A trimeric acid-stable coiled-coil lipoprotein from Acidithiobacillus thiooxidans. PeerJ 2014; 2: e457.
[65]
Combet C, Blanchet C, Geourjon C, Deleage G. NPS@: network protein sequence analysis. Elsevier Current Trends 2000.
[66]
Geourjon C, Deleage G. SOPM: A self-optimized method for protein secondary structure prediction. Protein Eng 1994; 7(2): 157-64.
[67]
Geourjon C, Deleage G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 1995; 11(6): 681-4.
[68]
Guermeur Y, Geourjon C, Gallinari P. Improved performance in protein secondary structure prediction by inhomogeneous score combination. Bioinformatics 1999; 15(5): 413-21.
[69]
Deleage G, Roux B. An algorithm for protein secondary structure prediction based on class prediction. Protein Eng 1987; 1(4): 289-94.
[70]
Levin JM, Robson B, Garnier J. An algorithm for secondary structure determination in proteins based on sequence similarity. FEBS Lett 1986; 205(2): 303-8.
[71]
Guermeur Y. Combinaison de classifieurs statistiques, application à la prédiction de la structure secondaire des protéinesPhD Thesis, Université de Paris, 1997.
[72]
Mugilan A, Ajitha MC, Thinagar D. In silico Secondary Structure Prediction Method (Kalasalingam University Structure Prediction Method) using Comparative Analysis. Trends in Bioinformatics 2010; 3: 11-9.
[73]
Frishman D, Argos P. Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence. Protein Eng 1996; 9(2): 133-42.
[74]
Frishman D, Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins: Struct Funct Bioinf 1997; 27(3): 329-35.
[75]
Gibrat J-F, Garnier J, Robson B. Further developments of protein secondary structure prediction using information theory: New parameters and consideration of residue pairs. J Mol Biol 1987; 198(3): 425-43.
[76]
Garnier J. GOR secondary structure prediction method version IVMeth Enzym, RF Doolittle Ed 1998; 266: 540-53.
[77]
Sen TZ, Jernigan RL, Garnier J, Kloczkowski A. GOR V server for protein secondary structure prediction. Bioinformatics 2005; 21(11): 2787-8.
[78]
Kouza M, Faraggi E, Kolinski A, Kloczkowski A. The GOR method of protein secondary structure prediction and its application as a protein aggregation prediction tool. Methods Mol Biol 2017; 1484: 7-24.
[79]
King RD, Sternberg MJ. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci 1996; 5(11): 2298-310.
[80]
Lin K, Simossis VA, Taylor WR, Heringa J. A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 2005; 21(2): 152-9.
[81]
Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol 2009; 9(1): 51.
[82]
Yan R, Xu D, Yang J, Walker S, Zhang Y. A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Sci Rep 2013; 3: 2619.
[83]
Raghava G. APSSP2: A combination method for protein secondary structure prediction based on neural network and example based learning CASP5 2002; A-132
[84]
Wang S, Li W, Liu S, Xu J. RaptorX-Property: A web server for protein structure property prediction Nucleic Acids Res 2016; 44(Web Server issue): W430-W5
[85]
Wang ZY, Zhao F, Peng J, Xu JB. Protein 8-class secondary structure prediction using conditional neural fields. Proteomics 2011; 11(19): 3786-92.
[86]
Wang S, Peng J, Ma J, Xu J. Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 2016; 6: 18962.
[87]
Yang Y, Gao J, Wang J, et al. Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform 2016; bbw129.
[88]
Yaseen A, Li Y. Template-based C8-SCORPION: A protein 8-state secondary structure prediction method using structural information and context-based features. BMC Bioinformatics 2014; 15(8): S3.
[89]
Montgomerie S, Sundararaj S, Gallin WJ, Wishart DS. Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinformatics 2006; 7: 301.
[90]
Montgomerie S, Cruz JA, Shrivastava S, Arndt D, Berjanskii M, Wishart DS. PROTEUS2: A web server for comprehensive protein structure prediction and structure-based annotation. Nucleic Acids Res 2008; 36(Suppl. 2): W202-9.
[91]
Adamczak R, Porollo A, Meller J. Combining prediction of secondary structure and solvent accessibility in proteins. Proteins: Struct Funct Bioinf 2005; 59(3): 467-75.
[92]
Leman JK, Mueller R, Karakas M, Woetzel N, Meiler J. Simultaneous prediction of protein secondary structure and transmembrane spans. Proteins: Struct Funct Bioinf 2013; 81(7): 1127-40.
[93]
Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292(2): 195-202.
[94]
Green JR, Korenberg MJ. editors.Nonlinear System Identification Provides Insight Into Protein Folding. Electrical and Computer Engineering, 2006 CCECE'06 Canadian Conference on; 2006: IEEE.
[95]
Green JR, Korenberg MJ, Aboul-Magd MO. PCI-SS: MISO dynamic nonlinear protein secondary structure prediction. BMC Bioinformatics 2009; 10(1): 222.
[96]
Linnert M, Lin YJ, Manns A, et al. The FKBP-type domain of the human aryl hydrocarbon receptor-interacting protein reveals an unusual Hsp90 interaction. Biochemistry 2013; 52(12): 2097-107.
[97]
Karplus K. SAM-T08, HMM-based protein structure prediction. Nucleic Acids Res 2009; 37(suppl_2): W492-W7.
[98]
Montgomerie S, Sundararaj S, Gallin WJ, Wishart DS. Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinformatics 2006; 7(1): 301.
[99]
Pohane AA, Patidar ND, Jain V. Modulation of domain-domain interaction and protein function by a charged linker: A case study of mycobacteriophage D29 endolysin. FEBS Lett 2015; 589(6): 695-701.
[100]
Kang JW, Lee NY, Cho KC, et al. Analysis of nitrated proteins in Saccharomyces cerevisiae involved in mating signal transduction. Proteomics 2015; 15(2-3): 580-90.
[101]
Hauf W, Watzer B, Roos N, Klotz A, Forchhammer K. Photoautotrophic polyhydroxybutyrate granule formation is regulated by cyanobacterial phasin PhaP in Synechocystis sp. strain PCC 6803. Appl Environ Microbiol 2015; 81(13): 4411-22.
[102]
Frades I, Resjo S, Andreasson E. Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis. BMC Bioinformatics 2015; 16: 239.
[103]
Espinoza-Fonseca LM, Kelekar A. High-resolution structural characterization of Noxa, an intrinsically disordered protein, by microsecond molecular dynamics simulations. Mol Biosyst 2015; 11(7): 1850-6.
[104]
Dong SS, Abrol R, Goddard WA. The predicted ensemble of low-energy conformations of human somatostatin receptor subtype 5 and the binding of antagonists. ChemMedChem 2015; 10(4): 650-61.
[105]
Dahlstrom KM, Salminen TA. 3D model for Cancerous Inhibitor of Protein Phosphatase 2A armadillo domain unveils highly conserved protein-protein interaction characteristics. J Theor Biol 2015; 386: 78-88.
[106]
Balasco N, Barone D, Vitagliano L. Structural conversion of the transformer protein RfaH: New insights derived from protein structure prediction and molecular dynamics simulations. J Biomol Struct Dyn 2015; 33(10): 2173-9.
[107]
Wu HY, Cheng YS. Combining secondary-structure and protein solvent-accessibility predictions in methionine substitution for anomalous dispersion. Acta Crystallogr Sect F Struct Biol Cryst Commun 2014; 70: 378-83.
[108]
Fu X, Chang Z, Shi X, Bu D, Wang C. Multilevel structural characteristics for the natural substrate proteins of bacterial small heat shock proteins. Protein Sci 2014; 23(2): 229-37.
[109]
Oates ME, Romero P, Ishida T, et al. (DP2)-P-2: database of disordered protein predictions. Nucleic Acids Res 2013; 41(D1): D508-16.
[110]
Lin YC, Chen BM, Lu WC, et al. The B7-1 Cytoplasmic tail enhances intracellular transport and mammalian cell surface display of chimeric proteins in the absence of a linear ER export motif. PLoS One 2013; 8(9): e75084.
[111]
Klein SL, Neilson KM, Orban J, et al. Conserved structural domains in FoxD4L1, a neural forkhead box transcription factor, are required to repress or activate target genes. PLoS One 2013; 8(4): e61845.
[112]
Fleming JR, Morgan RE, Fyfe PK, Kelly SM, Hunter WN. The architecture of Trypanosoma brucei tubulin-binding cofactor B and implications for function. FEBS J 2013; 280(14): 3270-80.
[113]
Ahn KH, Scott CE, Abrol R, Goddard WA, Kendall DA. Computationally-predicted CB1 cannabinoid receptor mutants show distinct patterns of salt-bridges that correlate with their level of constitutive activity reflected in G protein coupling levels, thermal stability, and ligand binding. Proteins: Struct Funct Bioinf 2013; 81(8): 1304-17.
[114]
Tarhda Z, Semlali O, Kettani A, Moussa A, Abumrad NA, Ibrahimi A. Three dimensional structure prediction of fatty acid binding site on human transmembrane receptor CD36. Bioinform Biol Insights 2013; 7: 369-73.
[115]
Kim JH, Kim SK, Lee JH, Kim YJ, Goddard WA, Kim YC. Homology modeling and molecular docking studies of Drosophila and Aedes sex peptide receptors. J Mol Graph Model 2016; 66: 115-22.
[116]
Ray S, Sinha J. In silico structure analysis of potassium channel bgk toxin and its docking prediction with human voltage gated potassium (Kv) channel. J Chem Pharm Res 2015; 7(5): 451-9.
[117]
Awad W, Adamczyk B, Ornros J, Karlsson NG, Mani K, Logan DT. Structural aspects of N-glycosylations and the C-terminal Region in human glypican-1. J Biol Chem 2015; 290(38): 22991-3008.
[118]
Wang S, Peng J, Ma J, Xu J. Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 2016; 6: 18962.
[119]
Yan R, Wang X, Huang L, Yan F, Xue X, Cai W. Prediction of structural features and application to outer membrane protein identification. Sci Rep 2015; 5: 11586.
[120]
Keller RCA. The role and significance of potential lipid-binding regions in the mitochondrial protein import motor: An in-depth in silico study. 3 Biotech 2015; 5(6): 1041-51.
[121]
Feng YG, Luo LF. Using long-range contact number information for protein secondary structure prediction. Int J Biomath 2014; 7(5): 1450052.
[122]
Leman JK, Mueller R, Karakas M, Woetzel N, Meiler J. Simultaneous prediction of protein secondary structure and transmembrane spans. Proteins: Struct., Funct. Bioinf 2013; 81(7): 1127-40.
[123]
Wang S, Li W, Liu SW, Xu JB. RaptorX-Property: A web server for protein structure property prediction. Nucleic Acids Res 2016; 44(W1): W430-5.
[124]
Yaseen A, Li YH. Context-based features enhance protein secondary structure prediction accuracy. J Chem Inf Model 2014; 54(3): 992-1002.
[125]
Zhang SL. Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou’s general PseAAC. Chemom Intell Lab Syst 2015; 142: 28-35.
[126]
Zhang J, Chen WH, Sun PP, Zhao XW, Ma ZQ. Prediction of protein solvent accessibility using PSO-SVR with multiple sequence-derived features and weighted sliding window scheme. BioData Min 2015; 8: 3.
[127]
Yu DJ, Hu J, Li QM, Tang ZM, Yang JY, Shen HB. Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction. IEEE Trans Nanobioscience 2015; 14(1): 45-58.
[128]
Yu DJ, Li Y, Hu J, Yang XB, Yang JY, Shen HB. Disulfide connectivity prediction based on modelled protein 3D structural information and random forest regression. IEEE/ACM Trans Comput Biol Bioinform 2015; 12(3): 611-21.
[129]
Xiao F, Shen HB. 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 2015; 55(11): 2464-74.
[130]
Heinze S, Putnam DK, Fischer AW, Kohlmann T, Weiner BE, Meiler J. CASP10-BCL: Fold efficiently samples topologies of large proteins. Proteins: Struct Funct Bioinf 2015; 83(3): 547-63.
[131]
de Oliveira SHP, Shi JY, Deane CM. Building a better fragment library for de novo protein structure prediction. PLoS One 2015; 10(4): e0123998.
[132]
Zhang LC, Zhao XQ, Kong L. A protein structural class prediction method based on novel features. Biochimie 2013; 95(9): 1741-4.
[133]
Mechelke M, Habeck M. A probabilistic model for secondary structure prediction from protein chemical shifts. Proteins: Struct Funct Bioinform 2013; 81(6): 984-93.
[134]
Kalev I, Habeck M. Confidence-guided local structure prediction with HHfrag. PLoS One 2013; 8(10): e76512.
[135]
Liu BL, Zhu W, Li B, Cao Z. A combination of feature extraction methods with an ensemble of support vector machines for bacterial virulent proteins prediction. J Comput Theor Nanosci 2015; 12(8): 1813-7.
[136]
Fan C, Liu DW, Huang R, Chen ZG, Deng L. PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility. BMC Bioinformatics 2016; 17: 8.
[137]
Olyaee MH, Yaghoubi A, Yaghoobi M. Predicting protein structural classes based on complex networks and recurrence analysis. J Theor Biol 2016; 404: 375-82.
[138]
Zheng W, Zhang C, Hanlon M, Ruan JS, Gao JZ. An ensemble method for prediction of conformational B-cell epitopes from antigen sequences. ‎. Comput Biol Chem 2014; 49: 51-8.
[139]
Gao JZ, Cui W, Sheng YJ, Ruan JS, Kurgan L. PSIONplus: Accurate sequence-based predictor of ion channels and their types. PLoS One 2016; 11(4): e0152964.
[140]
Wang C, Dong XB, Han L, et al. Identification of WD40 repeats by secondary structure-aided profile-profile alignment. J Theor Biol 2016; 398: 122-9.
[141]
Li W, Kinch LN, Karplus PA, Grishin NV. ChSeq: A database of chameleon sequences. Protein Sci 2015; 24(7): 1075-86.
[142]
Kumari B, Kumar R, Kumar M. PalmPred: An SVM based palmitoylation prediction method using sequence profile information. PLoS One 2014; 9(2): e89246.
[143]
Zhang W, Yang J, He B, et al. Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11. Proteins 2016; 84(Suppl. 1): 76-86.
[144]
Shinkai-Ouchi F, Koyama S, Ono Y, et al. Predictions of cleavability of calpain proteolysis by quantitative structure-activity relationship analysis using newly determined cleavage sites and catalytic efficiencies of an oligopeptide array. Mol Cell Proteomics 2016; 15(4): 1262-80.
[145]
Kang H, Weiss TM, Bang I, Weis WI, Choi HJ. Structure of the intermediate filament-binding region of desmoplakin. PLoS One 2016; 11(1): e0147641.
[146]
Ye YT, Cheung DWL, Wang YD, et al. GLProbs: Aligning multiple sequences adaptively. IEEE/ACM Trans Comput Biol Bioinform 2015; 12(1): 67-78.
[147]
Scior T, Paiz-Candia B, Islas AA, et al. Predicting a double mutant in the twilight zone of low homology modeling for the skeletal muscle voltage-gated sodium channel subunit beta-1 (Na(v)1.4 beta 1). Comput Struct Biotechnol J 2015; 13: 229-40.
[148]
Faraj SE, Venturutti L, Roman EA, et al. The role of the N-terminal tail for the oligomerization, folding and stability of human frataxin. FEBS Open Bio 2013; 3: 310-20.
[149]
Wang JM, Li Y, Modis Y. Structural models of the membrane anchors of envelope glycoproteins E1 and E2 from pestiviruses. Virology 2014; 454: 93-101.
[150]
Raucci R, Colonna G, Giovane A, Castello G, Costantini S. N-terminal region of human chemokine receptor CXCR3: Structural analysis of CXCR3(1-48) by experimental and computational studies. Biochim Biophys Acta 2014; 1844(10): 1868-80.
[151]
Rana A, Rub A, Akhter Y. Proteome-scale identification of outer membrane proteins in Mycobacterium avium subspecies paratuberculosis using a structure based combined hierarchical approach. Mol Biosyst 2014; 10(9): 2329-37.
[152]
Lee WK, Ahn HJ, Yu YG, Nam HW. Rhoptry protein 6 from Toxoplasma gondii is an intrinsically disordered protein. Protein Expr Purif 2014; 101: 146-51.
[153]
Wu ZY, Han RPS. SAAS: Short amino acid sequence-a promising protein secondary structure prediction method of single sequence. Int J Bioautom 2013; 17(2): 65-72.
[154]
Trejo-Soto PJ, Aguayo-Ortiz R, Yepez-Mulia L, Hernandez-Campos A, Medina-Franco JL, Castillo R. Insights into the structure and inhibition of Giardia intestinalis arginine deiminase: Homology modeling, docking, and molecular dynamics studies. J Biomol Struct Dyn 2016; 34(4): 732-48.
[155]
Saw WG, Eisenhaber B, Eisenhaber F, Gruber G. Low-resolution structure of the soluble domain GPAA1 (yGPAA(170-247)) of the glycosylphosphatidylinositol transamidase subunit GPAA1 from Saccharomyces cerevisiae. Biosci Rep 2013; 33: 361-9.
[156]
Eskandari V, Yakhchali B, Sadeghi M, Karkhane AA. In silico design and construction of metal-binding hybrid proteins for specific removal of cadmium based on CS3 pili display on the surface of Escherichia coli. Biotechnol Appl Biochem 2013; 60(6): 564-72.
[157]
Patel MS, Mazumdar HS. Knowledge base and neural network approach for protein secondary structure prediction. J Theor Biol 2014; 361: 182-9.
[158]
Sakthivel S. S KMH. NNvPDB: Neural network based protein secondary structure prediction with PDB validation. Bioinformation 2015; 11(8): 416-21.
[159]
Mugilan A, Jemimah S, Jennifer P. Novel method of protein structure prediction (NPSPM) based on short range interactions between amino acids. Trends Bioinform 2014; 7(1): 1-6.
[160]
Yu JY, Xiang LJ, Hong J, Zhang WD. HMM-Based prediction for protein structural motifs’ two local properties: Solvent accessibility and backbone torsion angles. Protein Pept Lett 2013; 20(2): 156-64.
[161]
Hayat M, Iqbal N. Discriminating protein structure classes by incorporating pseudo average chemical shift to chou’s general PseAAC and support vector machine. Comput Methods Programs Biomed 2014; 116(3): 184-92.
[162]
Mooney C, Haslam NJ, Holton TA, Pollastri G, Shields DC. PeptideLocator: Prediction of bioactive peptides in protein sequences. Bioinformatics 2013; 29(9): 1120-6.
[163]
Cheung NJ, Ding XM, Shen HB. Protein folds recognized by an intelligent predictor based-on evolutionary and structural information. J Comput Chem 2016; 37(4): 426-36.
[164]
Zhang H, Kurgan L. Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models. Amino Acids 2014; 46(12): 2665-80.
[165]
Kong L, Kong LF, Jing R. improving the prediction of protein structural class for low-similarity sequences by incorporating evolutionary and structural information. JACIII 2016; 20(3): 402-11.
[166]
Maurice KJ. SSThread: Template-free protein structure prediction by threading pairs of contacting secondary structures followed by assembly of overlapping pairs. J Comput Chem 2014; 35(8): 644-56.
[167]
Lyons J, Dehzangi A, Heffernan R, et al. Predicting backbone C alpha angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J Comput Chem 2014; 35(28): 2040-6.
[168]
Dehzangi A, Paliwal K, Lyons J, Sharma A, Sattar A. Proposing a highly accurate protein structural class predictor using segmentation-based features. BMC Genomics 2014; 15(Suppl. 1): S2.
[169]
Paliwal KK, Sharma A, Lyons J, Dehzangi A. Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information. BMC Bioinformatics 2014; 15(Suppl. 16): S12.
[170]
Peng Y, Yu K, Zhang Y, Islam S, Sun D, Ma W. Two novel y-type high molecular weight glutenin genes in chinese wheat landraces of the yangtze-river region. PLoS One 2015; 10(11): e0142348.
[171]
Lin XY, Chen S, Xue XY, et al. Chimerically fused antigen rich of overlapped epitopes from latent membrane protein 2 (LMP2) of Epstein-Barr virus as a potential vaccine and diagnostic agent. Cell Mol Immunol 2016; 13(4): 492-501.
[172]
Hasan MA, Mazumder MHH, Chowdhury AS, Datta A, Khan MA. Molecular-docking study of malaria drug target enzyme transketolase in Plasmodium falciparum 3D7 portends the novel approach to its treatment. Source Code Biol Med 2015; 10: 7.
[173]
Ramalingam V, Rajaram R, Suresh V. Secondary structure prediction of scleractinia corals: A proteomic approach. Indian J Geo-Mar Sci 2013; 42(4): 503-9.
[174]
Bhati J, Chaduvula PK, Kumar S, Rai A. Phylogenetic analysis and secondary structure prediction for drought tolerant Cap binding proteins of plant species. Indian J Agric Sci 2013; 83(1): 21-5.
[175]
Sheoran S, Pandey B, Sharma P, et al. In silico comparative analysis and expression profile of antioxidant proteins in plants. Genet Mol Res: GMR 2013; 12(1): 537-51.
[176]
Ye WW, Wang Y, Wang YC. Bioinformatics analysis reveals abundant short alpha-helices as a common structural feature of oomycete RxLR Effector Proteins. PLoS One 2015; 10(8): e0135240.
[177]
Corradini E, Burgers PP, Plank M, Heck AJR, Scholten A. Huntingtin-associated Protein 1 (HAP1) Is a cGMP-dependent Kinase Anchoring Protein (GKAP) Specific for the cGMP-dependent Protein Kinase I beta Isoform. J Biol Chem 2015; 290(12): 7887-96.
[178]
Xu D, Zhang Y. Toward optimal fragment generations for ab initio protein structure assembly. Proteins: Struct Funct Bioinf 2013; 81(2): 229-39.
[179]
Elbashir MK, Wang JX, Wu FX, Wang LS. Predicting beta-turns in proteins using support vector machines with fractional polynomials. Proteome Sci 2013; 11(Suppl. 1): S5.
[180]
Chen SH, Meller J, Elber R. Comprehensive analysis of sequences of a protein switch. Protein Sci 2016; 25(1): 135-46.
[181]
Lin MH, Hsu HJ, Bartenschlager R, Fischer WB. Membrane undulation induced by NS4A of Dengue virus: A molecular dynamics simulation study. J Biomol Struct Dyn 2014; 32(10): f1552-62.


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VOLUME: 16
ISSUE: 2
Year: 2019
Page: [159 - 172]
Pages: 14
DOI: 10.2174/1570163815666180227162157
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