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Chapter 12Prediction of Protein Functionfrom Theoretical ModelsIwona A. Cymerman, Daniel J. Rigden, and Janusz M. BujnickiAbstract Comparative protein modelling is a mature technique and template-freemodelling is already a valuable supplement for small proteins of novel or unrecognisablefolds. The developments in the area of structure-based function inference,stimulated by Structural Genomics projects, have resulted in a battery of methodsthat can be applied to models of all origins. There are important limitations in modelaccuracy and, partly as a consequence, in the performance of function predictionalgorithms when applied to models. Nevertheless, this chapter shows how diverseaspects of protein function can be illuminated by modelling of different kinds,frequently facilitating the planning and interpretation of experimental results.Important challenges remain in establishing a constructive dialogue between modellersand experimental biologists to allow an expansion of the practical applicationsof proteins models. Databases of protein models, containing indicators ofpredicted structural quality, may have an important future role.12.1 BackgroundThe rapid progress in computer sciences and speed up of data exchange capabilitiesover the past decades have enormously influenced the direction and methodologyof biological research. These advances enabled the development of large-scale initiativeslike genome sequencing projects, microarray methodology and structuralgenomics. These in turn reversed the direction of individual investigations. Thus,instead of searching for the genes and proteins responsible for observed pheno-I.A. CymermanInternational Institute of Molecular and Cell Biology, Trojdena 4, 02-109 Warsaw, PolandD.J. RigdenSchool of Biological Sciences, University of Liverpool, Liverpool L69 7ZB, UKJ.M. Bujnicki*Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam MickiewiczUniversity, Umultowska 89, 61-614 Poznań, Poland*Corresponding author: e-mail: iamb@genesilico.plD.J. Rigden (ed.) From Protein Structure to Function with Bioinformatics, 293© Springer Science + Business Media B.V. 2009
294 I.A. Cymerman et al.types, scientists often focus on seeking functions for the vast numbers of sequencescollected in the databases. Obviously, the change from the descriptive to the morepredictive applications requires the development of novel tools.The most basic information of interest regarding a particular gene or proteinis its associated function. The most common approach to function predictionstarts from the observation that proteins with similar sequences frequently havesimilar functions. Particularly with the recent increase in the number of availablesequences, related sequences can be aligned and grouped into families. If thefunction of one of the family members is known, the remaining sequences arehypothetically assigned with the same function “through inheritance”. This raisesthe question as to whether one needs a protein structure to predict protein functionalityor is sequence information sufficient. At first sight one could answer thatit depends on the sequence similarity between two compared protein sequences.It is widely accepted that sequence identity higher than about 30% is a strongindicator that the proteins may share a very similar structure that can be predictedby comparative approaches (see Chapter 3), yielding generally accurate models.Below this threshold, however, assignment of protein structure requires moresophisticated approaches (such as those outlined in Chapters 1 and 2) and is nolonger as accurate. Since function depends on structure, one could envisage thata similar dependency holds also for transfer of functional annotations. This, however,is not necessarily the case, as significant functional variation can beobserved even for proteins with very similar sequences and structures. Forinstance, functional annotations based on Gene Ontology are conserved in only80% of protein pairs even for proteins sharing 90–100% identity; this value dropsbelow 50% for proteins with less than 30% sequence identity. Some aspects offunction appear to be more conserved than others, e.g. if the enzymatic functionis considered according to the EC system, all four EC numbers tend to be conservedin nearly 100% cases above 70% pairwise sequence identity. For sequenceswith pairwise identity below 30%, the conservation of EC numbers also dropsbelow 50% (Tress et al. 2008).Conservation of function is more complex than conservation of structure,because functional overlap (e.g. identical function in two copies of one gene aftera duplication) is subjected to evolutionary rate acceleration, which howeverdepends on pre-existing functional utility of the protein encoded by the ancestralsingleton (Jordan et al. 2004). Thus, a duplication that gives rise to paralogousproteins with very similar sequences and structures either results in a loss, byinactivating mutation or deletion, of one the copies (thus reversal to the ancestralstate) or in functional divergence of one or both copies, as to reduce the overlap.On the other hand, orthologous sequences tend to retain identical function, oftendespite considerable sequence divergence. Pairwise sequence comparisons arehowever unable to distinguish between orthologous and paralogous sequences,thus arguing against its use for function annotation. There are a number of methodsthat carry out function prediction based on evolutionary analyses, and discriminationof paralogous vs orthologous sequences (e.g. FlowerPower(Krishnamurthy et al. 2007) ). However, these methods require the availability of
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Daniel John RigdenEditorFrom Protei
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ContentsSection I Generating and In
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Contentsxi4.2.1 Alpha-Helical Bundl
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Contentsxiii7.4.2 Predicting Bindin
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Contentsxv12.3 Accuracy and Added V
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4 J. Lee et al.about 5.3 million pr
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6 J. Lee et al.Table 1.1 A list of
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8 J. Lee et al.2000; Sorin and Pand
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10 J. Lee et al.Although most knowl
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12 J. Lee et al.Fig. 1.3 Two exampl
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14 J. Lee et al.rugged containing m
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16 J. Lee et al.1.3.4 Mathematical
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18 J. Lee et al.potentials, each re
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20 J. Lee et al.viewpoint of struct
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22 J. Lee et al.Hsieh MJ, Luo R (20
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24 J. Lee et al.Sippl MJ (1990) Cal
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Chapter 2Fold RecognitionLawrence A
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2 Fold Recognition 29It has long be
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2 Fold Recognition 31Fig. 2.2 Graph
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2 Fold Recognition 33distribute the
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2 Fold Recognition 35Table 2.1 (a)
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2 Fold Recognition 37dependent on t
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2 Fold Recognition 39based on the r
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2 Fold Recognition 41The idea of co
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2 Fold Recognition 43query sequence
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2 Fold Recognition 452.3.4 Consensu
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2 Fold Recognition 472.4 Alignment
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2 Fold Recognition 49statistical me
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2 Fold Recognition 51methods (e.g.
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2 Fold Recognition 53Berman HM, Wes
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2 Fold Recognition 55Tress ML, Jone
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58 A. Fiserwith more than 50% seque
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60 A. FiserIn contrast to ab initio
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62 A. FiserTable 3.1 (continued)SWI
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64 A. Fiserbetween fold recognition
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66 A. FiserFig. 3.1 Comparing accur
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68 A. Fiserinto conserved core regi
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70 A. Fiseralignments are built and
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72 A. Fiserfold, such as the hyperv
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74 A. Fiserfunctions delivers impro
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76 A. Fiserfrom efforts of genome s
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78 A. FiserA rigorous statistical e
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80 A. Fiser(Clore et al. 1993). Cha
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82 A. FiserImproved and new methods
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84 A. FiserClaessens M, Van Cutsem
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86 A. FiserHavel TF, Snow ME (1991)
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88 A. FiserPetrey D, Xiang Z, Tang
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90 A. Fiservan Vlijmen HW, Karplus
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92 T. Nugent and D.T. Jones4.2 Stru
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94 T. Nugent and D.T. JonesFig. 4.2
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96 T. Nugent and D.T. JonesTable 4.
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98 T. Nugent and D.T. Jones2000), d
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100 T. Nugent and D.T. JonesTable 4
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102 T. Nugent and D.T. JonesFig. 4.
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104 T. Nugent and D.T. JonesFig. 4.
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106 T. Nugent and D.T. Jonessubdivi
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108 T. Nugent and D.T. Jonescomplex
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110 T. Nugent and D.T. JonesMartell
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Chapter 5Bioinformatics Approaches
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5 Structure and Function of Intrins
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Chapter 6Function Diversity Within
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6 Function Diversity Within Folds a
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Chapter 7Predicting Protein Functio
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7 Predicting Protein Function from
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Table 7.1 Online resources and tool
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Chapter 83D MotifsElaine C. Meng, B
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8 3D Motifs 189clustering similar s
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8 3D Motifs 191To improve the signa
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8 3D Motifs 193structures together.
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8 3D Motifs 195Table 8.1 (continued
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8 3D Motifs 1978.3.1 User-Defined M
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8 3D Motifs 199Fig. 8.3 The FFF mot
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8 3D Motifs 2018.3.2 Motif Discover
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8 3D Motifs 203In addition to SITE
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8 3D Motifs 205folds; they shared a
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8 3D Motifs 207from a training set
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8 3D Motifs 209Table 8.3 Web server
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8 3D Motifs 211its greater overall
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8 3D Motifs 213Ideally, a 3D motif
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8 3D Motifs 215Ivanisenko VA, Pintu
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Chapter 9Protein Dynamics: From Str
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9 Protein Dynamics: From Structure
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Page 324 and 325: 320 IndexConsensus templates, 205Co
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