Abstract
Fold recognition is concerned with the prediction of protein three-dimensional structure from amino acid sequence by the detection of extremely remote homologous or analogous relationships to known structures. As such it lies midway between ab initio protein folding and close homology modelling. This chapter surveys both the history of the field and the current state-of-the art, focussing on approaches recently shown to be successful in international blind trials.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Altschul SF, Madden TL, Schaffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402
Benkert P, Tosatto SC, Schwede T (2009) Global and local model quality estimation at CASP8 using the scoring functions QMEAN and QMEANclust. Proteins 77(Suppl 9):173–180. doi:10.1002/prot.22532
Bennett-Lovsey RM, Herbert AD, Sternberg MJ et al (2008) Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre. Proteins 70(3):611–625. doi:10.1002/prot.21688
Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242
Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170
Cao R, Wang Z, Cheng J (2014) Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment. BMC Struct Biol 14:13. doi:10.1186/1472-6807-14-13
Chivian D, Kim DE, Malmstrom L et al (2005) Prediction of CASP6 structures using automated Robetta protocols. Proteins 61(Suppl 7):157–166. doi:10.1002/prot.20733
Chubb D, Jefferys BR, Sternberg MJ et al (2010) Sequencing delivers diminishing returns for homology detection: implications for mapping the protein universe. Bioinformatics 26(21):2664–2671. doi:10.1093/bioinformatics/btq527
Di Lena P, Fariselli P, Margara L et al (2010) Fast overlapping of protein contact maps by alignment of eigenvectors. Bioinformatics 26(18):2250–2258. doi:10.1093/bioinformatics/btq402
Ginalski K, Elofsson A, Fischer D et al (2003) 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19(8):1015–1018
Jefferys BR, Kelley LA, Sternberg MJ (2010) Protein folding requires crowd control in a simulated cell. J Mol Biol 397(5):1329–1338. doi:10.1016/j.jmb.2010.01.074
Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202. doi:10.1006/jmbi.1999.3091
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637. doi:10.1002/bip.360221211
Kallberg M, Margaryan G, Wang S et al (2014) RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol 1137:17–27. doi:10.1007/978-1-4939-0366-5_2
Kamisetty H, Ovchinnikov S, Baker D (2013) Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era. Proc Natl Acad Sci U S A 110(39):15674–15679. doi:10.1073/pnas.1314045110
Kelley LA, Mezulis S, Yates CM et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858. doi:10.1038/nprot.2015.053
Kim H, Park H (2004) Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. Proteins 54(3):557–562. doi:10.1002/prot.10602
Kryshtafovych A, Barbato A, Fidelis K et al (2014) Assessment of the assessment: evaluation of the model quality estimates in CASP10. Proteins 82(Suppl 2):112–126. doi:10.1002/prot.24347
Kumar M, Bhasin M, Natt NK et al (2005) BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques. Nucleic Acids Res 33 (Web Server issue):W154–W159
Larsson P, Skwark MJ, Wallner B et al (2011) Improved predictions by Pcons.net using multiple templates. Bioinformatics 27 (3):426–427. doi:10.1093/bioinformatics/btq664
Lewis TE, Sillitoe I, Andreeva A et al (2013) Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains. Nucleic Acids Res 41 (Database issue):D499–D507. doi:10.1093/nar/gks1266
Magner A, Szpankowski W, Kihara D (2015) On the origin of protein superfamilies and superfolds. Sci Rep 5:8166. doi:10.1038/srep08166
Marsden RL, Lee D, Maibaum M et al (2006) Comprehensive genome analysis of 203 genomes provides structural genomics with new insights into protein family space. Nucleic Acids Res 34(3):1066–1080. doi:10.1093/nar/gkj494
McGuffin LJ (2009) Prediction of global and local model quality in CASP8 using the ModFOLD server. Proteins 77(Suppl 9):185–190. doi:10.1002/prot.22491
Meier A, Soding J (2015a) Automatic prediction of protein 3D structures by probabilistic multi-template homology modeling. PLoS Comput Biol 11(10):e1004343. doi:10.1371/journal.pcbi.1004343
Meier A, Soding J (2015b) Context similarity scoring improves protein sequence alignments in the midnight zone. Bioinformatics 31(5):674–681. doi:10.1093/bioinformatics/btu697
Miyazawa S, Jernigan RL (1996) Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 256(3):623–644. doi:10.1006/jmbi.1996.0114
Moult J, Fidelis K, Kryshtafovych A et al (2014) Critical assessment of methods of protein structure prediction (CASP)–round x. Proteins 82(Suppl 2):1–6. doi:10.1002/prot.24452
Park J, Teichmann SA, Hubbard T et al (1997) Intermediate sequences increase the detection of homology between sequences. J Mol Biol 273(1):349–354. doi:10.1006/jmbi.1997.1288
Peng J, Xu J (2010) Low-homology protein threading. Bioinformatics 26(12):i294–i300. doi:10.1093/bioinformatics/btq192
Perdigao N, Heinrich J, Stolte C et al (2015) Unexpected features of the dark proteome. Proc Natl Acad Sci U S A 112(52):15898–15903. doi:10.1073/pnas.1508380112
Remmert M, Biegert A, Hauser A et al (2012) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9(2):173–175. doi:10.1038/nmeth.1818
Richmond TJ (1984) Solvent accessible surface area and excluded volume in proteins. Analytical equations for overlapping spheres and implications for the hydrophobic effect. J Mol Biol 178(1):63–89
Rohl CA, Strauss CE, Misura KM et al (2004) Protein structure prediction using Rosetta. Methods Enzymol 383:66–93. doi:10.1016/S0076-6879(04)83004-0
Rychlewski L, Jaroszewski L, Li W et al (2000) Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Sci 9(2):232–241. doi:10.1110/ps.9.2.232
Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815. doi:10.1006/jmbi.1993.1626
Seringhaus M, Gerstein M (2007) Chemistry nobel rich in structure. Science 315(5808):40–41. doi:10.1126/science.315.5808.40
Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15(11):2507–2524. doi:10.1110/ps.062416606
Siew N, Elofsson A, Rychlewski L et al (2000) MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics 16(9):776–785
Sillitoe I, Dawson N, Thornton J et al (2015) The history of the CATH structural classification of protein domains. Biochimie 119:209–217. doi:10.1016/j.biochi.2015.08.004
Sippl MJ (1990) Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J Mol Biol 213(4):859–883
Soding J (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21(7):951–960. doi:10.1093/bioinformatics/bti125
Soding J, Biegert A, Lupas AN (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 33 (Web Server issue):W244–W248. doi:10.1093/nar/gki408
Sutcliffe MJ, Haneef I, Carney D et al (1987) Knowledge based modelling of homologous proteins, part I: three-dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng 1(5):377–384
Tanaka S, Scheraga HA (1976) Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. Macromolecules 9(6):945–950
Van Noorden R, Maher B, Nuzzo R (2014) The top 100 papers. Nature 514(7524):550–553. doi:10.1038/514550a
Wallner B, Fang H, Elofsson A (2003) Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller. Proteins 53(Suppl 6):534–541. doi:10.1002/prot.10536
Wu S, Zhang Y (2007) LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 35(10):3375–3382. doi:10.1093/nar/gkm251
Xu D, Zhang Y (2011) Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 101(10):2525–2534. doi:10.1016/j.bpj.2011.10.024
Yang J, Yan R, Roy A et al (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods 12(1):7–8. doi:10.1038/nmeth.3213
Zhang C, Liu S, Zhou Y (2004) Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential. Protein Sci 13(2):391–399. doi:10.1110/ps.03411904
Zhang J, Zhang Y (2010) A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS ONE 5(10):e15386. doi:10.1371/journal.pone.0015386
Zhang Y, Skolnick J (2004) SPICKER: a clustering approach to identify near-native protein folds. J Comput Chem 25(6):865–871. doi:10.1002/jcc.20011
Zhou H, Skolnick J (2011) GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophys J 101(8):2043–2052. doi:10.1016/j.bpj.2011.09.012
Zhou H, Zhou Y (2004) Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition. Proteins 55(4):1005–1013. doi:10.1002/prot.20007
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Kelley, L.A. (2017). Fold Recognition. In: J. Rigden, D. (eds) From Protein Structure to Function with Bioinformatics. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1069-3_3
Download citation
DOI: https://doi.org/10.1007/978-94-024-1069-3_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-024-1067-9
Online ISBN: 978-94-024-1069-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)