A structural perspective on genome evolution

Current Opinion in Structural Biology

Volumn 13, Issue 3, 2003, Pages 359-369

  Lee, David ^a   Grant, Alastair ^a   Buchan, Daniel ^a   Orengo, Christine ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; BACTERIAL GENOME; DATA ANALYSIS; GENE MAPPING; GENE SEQUENCE; GENE STRUCTURE; GENETIC DATABASE; GENETIC VARIABILITY; GENOME; GENOMICS; MOLECULAR EVOLUTION; NONHUMAN; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DOMAIN; PROTEIN FAMILY; PROTEIN FUNCTION; PROTEIN STRUCTURE; REGNUM; REVIEW; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; STRUCTURAL GENE; STRUCTURE ANALYSIS;

BACTERIA (MICROORGANISMS); NEMATODA;

EID: 0037832198     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0959-440X(03)00079-4     Document Type: Review

References (73)

1. Westbrook J., Feng Z., Chen L., Yang H., Berman H. The Protein Data Bank and structural genomics. Nucleic Acids Res. 31:2003;489-491.
2. Coulson A., Moult J. A unifold, mesofold, and superfold model of protein fold use. Proteins. 46:2002;61-71.
3. Koonin E., Wolf Y., Karev G. The structure of the protein universe and genome evolution. Nature. 420:2002;218-223 The authors review the state of current research in genome evolution. The presence of power law relationships within genome components is discussed. They also present a comprehensive discussion of the potential evolutionary forces driving the emergence of such relationships.
4. Pearl F., Bennett C., Bray J., Harrison A., Martin N., Shepherd A., Sillitoe I., Thornton J., Orengo C. The CATH database: an extended protein family resource for structural and functional genomics. Nucleic Acids Res. 31:2003;452-455.
5. Sali A. Target practice. Nat. Struct. Biol. 8:2001;482-484.
6. Baker D., Sali A. Protein structure prediction and structural genomics. Science. 294:2001;93-96.
7. Rost B. Did evolution leap to create the protein universe? Curr. Opin. Struct. Biol. 12:2002;409-416 A review of the observed protein structure content of complete genomes. Some marked differences between eukaryotes and prokaryotes are noted with respect to the nature of the folds observed. Rost notes that many proteins do not have close homologues within other genomes and may be orphans.
8. Liu J., Rost B. Comparing function and structure between entire proteomes. Protein Sci. 10:2001;1970-1999 Liu and Rost survey the state of the art in proteome annotation, focusing on broad functional features of proteins. They observe that, unlike bacteria and archaea, eukaryotic proteins are frequently multidomain and eukaryotes have twice as many coiled-coil proteins. They note that nearly a third of the observed proteins have transmembrane helices and up to a quarter of proteins are secreted. They estimate that there are likely to be between 1200 and 2600 protein folds.
9. Orengo C., Sillitoe I., Reeves G., Pearl F. Review: what can structural classifications reveal about protein evolution? J. Struct. Biol. 134:2001;145-165.
10. Grishin N. Review: Fold change in evolution of protein structures. J. Struct. Biol. 134:2001;167-185 An interesting review discussing the similarities between different fold groups and speculating on evolutionary mechanisms giving rise to fold changes.
11. Dokholyan N., Shakhnovich E. Understanding hierarchical protein evolution from first principles. J. Mol. Biol. 312:2001;289-307 Rigorous mathematical analysis of the relationships between protein families sharing common folds, proposing an elegant model for their evolution.
12. Pearl F, Orengo C: Protein structure classifications. In Bioinformatics: Genes, Proteins and, Computers. Edited by Orengo CA, Jones DT, Thornton JM. Abingdon, UK: Bios; 2003:103-111.
13. Lo Conte L., Brenner S., Hubbard T., Chothia C., Murzin A. SCOP database in 2002: refinements accommodate structural genomics. Nucleic Acids Res. 30:2002;264-267.
14. de Bakker P.I., Bateman A., Burke D., Miguel R., Mizuguchi K., Shi J., Shirai H., Blundell T. HOMSTRAD: adding sequence information to structure-based alignments of homologous protein families. Bioinformatics. 17:2001;748-749.
15. Dietmann S., Park J., Notredame C., Heger A., Lappe M., Holm L. A fully automatic evolutionary classification of protein folds: Dali Domain Dictionary version 3. Nucleic Acids Res. 29:2001;55-57.
16. Marchler-Bauer A., Anderson J., DeWeese-Scott C., Fedorova N., Geer L., He S., Hurwitz D., Jackson J., Jacobs A., Lanczycki C.et al. CDD: a curated Entrez database of conserved domain alignments. Nucleic Acids Res. 31:2003;383-387.
17. Harrison A., Pearl F., Mott R., Thornton J., Orengo C. Quantifying the similarities within fold space. J. Mol. Biol. 323:2002;909-926.
18. Gough J., Karplus K., Hughey R., Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J. Mol. Biol. 313:2001;903-919.
19. Pawlowski K., Rychlewski L., Zhang B., Godzik A. Fold predictions for bacterial genomes. J. Struct. Biol. 134:2001;219-231.
20. Krause A., Stoye J., Vingron M. The SYSTERS protein sequence cluster set. Nucleic Acids Res. 28:2000;270-272.
21. Yona G., Linial N., Linial M. ProtoMap: automatic classification of protein sequences and hierarchy of protein families. Nucleic Acids Res. 28:2000;49-55.
22. Pawlowski K., Rychlewski L., Zhang B., Godzik A. Fold predictions for bacterial genomes. J. Struct. Biol. 134:2001;219-231.
23. Liu J., Rost B. Target space for structural genomics revisited. Bioinformatics. 18:2002;922-933.
24. Vitkup D., Melamud E., Moult J., Sander C. Completeness in structural genomics. Nat. Struct. Biol. 8:2001;559-566.
25. Todd A., Orengo C., Thornton J. Evolution of function in protein superfamilies, from a structural perspective. J. Mol. Biol. 307:2001;1113-1143 Comprehensive analysis of the mechanisms by which biochemical functions have evolved in enzyme families with structural representatives.
26. Devos D., Valencia A. Practical limits of function prediction. Proteins. 41:2000;98-107.
27. Wilson C., Kreychman J., Gerstein M. Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores. J. Mol. Biol. 297:2000;233-249.
28. Todd A., Orengo C., Thornton J. Sequence and structural differences between enzyme and nonenzyme homologs. Structure. 10:2002;1435-1451.
29. Apic G., Gough J., Teichmann S. Domain combinations in archaeal, eubacterial and eukaryotic proteomes. J. Mol. Biol. 310:2001;311-325 Analysis of the SCOP database. The authors observe power law relationships in the networks of domain partners. They show that many domains do not have domain partners and those that do often only have partners from one or two families. They show that some domains are highly promiscuous and have many partners (e.g. the P-loop nucleotide triphosphate hydrolases).
30. Teichmann S., Murzin A., Chothia C. Determination of protein function, evolution and interactions by structural genomics. Curr. Opin. Struct. Biol. 11:2001;354-363.
31. Guo J., Xu D., Kim D., Xu Y. Improving the performance of DomainParser for structural domain partition using neural network. Nucleic Acids Res. 31:2003;944-952.
32. Servant F., Bru C., Carrere S., Courcelle E., Gouzy J., Peyruc D., Kahn D. ProDom: automated clustering of homologous domains. Brief Bioinform. 3:2002;246-251.
33. Buchan D., Shepherd A., Lee D., Pearl F., Rison S., Thornton J., Orengo C. Gene3D: structural assignment for whole genes and genomes using the CATH domain structure database. Genome Res. 12:2002;503-514 The authors present a database of structural assignments to genes of unknown structure within complete genomes. Structural assignments are based on sequence identity using PSI-BLAST. The database currently contains data for 66 genomes.
34. Sillitoe I, Orengo C: Protein structure comparison. In Bioinformatics: Genes, Proteins and Computers. Edited by Orengo CA, Jones DT, Thornton JM. Abingdon, UK: Bios; 2003:81-102.
35. Levitt M., Gerstein M. A unified statistical framework for sequence comparison and structure comparison. Proc. Natl. Acad. Sci. USA. 95:1998;5913-5920.
36. Dietmann S., Holm L. Identification of homology in protein structure classification. Nat. Struct. Biol. 8:2001;953-957.
37. Park J., Karplus K., Barrett C., Hughey R., Haussler D., Hubbard T., Chothia C. Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. J. Mol. Biol. 284:1998;1201-1210.
38. Madera M., Gough J. A comparison of profile hidden Markov model procedures for remote homology detection. Nucleic Acids Res. 30:2002;4321-4328.
39. Pearl F., Orengo C., Pearl F., Lee D., Bray J., Sillitoe I., Todd A., Harrison A., Thornton J., Orengo C. Assigning genomic sequences to CATH. Nucleic Acids Res. 28:2000;277-282.
40. Lindahl E., Elofsson A. Identification of related proteins on family, superfamily and fold level. J. Mol. Biol. 295:2000;613-625 The authors compare the performance of the predominant sequence searching methods for use in fold, family and superfamily recognition. PSI-BLAST, HMM and threading methods are compared.
41. Schaffer A., Aravind L., Madden T., Shavirin S., Spouge J., Wolf Y., Koonin E., Altschul S. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res. 29:2001;2994-3005.
42. Yona G., Levitt M. Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. J. Mol. Biol. 315:2002;1257-1275.
43. Karplus K, Karchin R, Barrett C, Tu S, Cline M, Diekhans M, Grate L, Casper J, Hughey R: What is the value added by human intervention in protein structure prediction? Proteins 2001, (suppl 5):86-91.
44. Hargbo J., Elofsson A. Hidden Markov models that use predicted secondary structures for fold recognition. Proteins. 36:1999;68-76.
45. Panchenko A., Marchler-Bauer A., Bryant S. Combination of threading potentials and sequence profiles improves fold recognition. J. Mol. Biol. 296:2000;1319-1331.
46. Rychlewski L., Jaroszewski L., Li W., Godzik A. Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Sci. 9:2000;232-241.
47. Jones DT: Protein structure prediction. In Bioinformatics: Genes, Proteins and Computers. Edited by Orengo CA, Jones DT, Thornton JM. Abingdon, UK: Bios; 2003:135-150.
48. Sippl M, Lackner P, Domingues F, Prlic A, Malik R, Andreeva A, Wiederstein M: Assessment of the CASP4 fold recognition category. Proteins 2001, (suppl 5):55-67.
49. Sanchez R., Pieper U., Melo F., Eswar N., Marti-Renom M., Madhusudhan M., Mirkovic N., Sali A. Protein structure modeling for structural genomics. Nat. Struct. Biol. 7(suppl):2000;986-990.
50. Marti-Renom M., Stuart A., Fiser A., Sanchez R., Melo F., Sali A. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29:2000;291-325.
51. Guex N., Diemand A., Peitsch M. Protein modelling for all. Trends Biochem. Sci. 24:1999;364-367.
52. Brooksbank C., Camon E., Harris M., Magrane M., Martin M., Mulder N., O'Donovan C., Parkinson H., Tuli M., Apweiler R.et al. The European Bioinformatics Institute's data resources. Nucleic Acids Res. 31:2003;43-50.
53. Mulder N., Apweiler R., Attwood T., Bairoch A., Barrell D., Bateman A., Binns D., Biswas M., Bradley P., Bork P.et al. The InterPro Database, 2003 brings increased coverage and new features. Nucleic Acids Res. 31:2003;315-318.
54. Hubbard T. Biological information: making it accessible and integrated (and trying to make sense of it). Bioinformatics. 18(suppl 2):2002;S140.
55. Gough J., Chothia C. SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments and genome assignments. Nucleic Acids Res. 30:2002;268-272 SUPERFAMILY is a database of HMM models of the SCOP structural domain families. Many of the SAMT98 HMM models are refined manually to generate highly sensitive and accurate models. Structural assignments to genes and genomes are available from the database.
56. Huynen M., van Nimwegen E. The frequency distribution of gene family sizes in complete genomes. Mol. Biol. Evol. 15:1998;583-589.
57. Qian J., Luscombe N., Gerstein M. Protein family and fold occurrence in genomes: power-law behaviour and evolutionary model. J. Mol. Biol. 313:2001;673-681 The authors show that the occurrence of protein folds and the use of protein families by extant genomes follow a power law behaviour. They show that, although the majority of families are utilised very seldom, some families are highly over used.
58. Wolf Y., Grishin N., Koonin E. Estimating the number of protein folds and families from complete genome data. J. Mol. Biol. 299:2000;897-905.
59. Luscombe N., Qian J., Zhang Z., Johnson T., Gerstein M. The dominance of the population by a selected few: power-law behaviour applies to a wide variety of genomic properties. Genome Biol. 3:2002;40.
60. Shakhnovich B., Dokholyan N., DeLisi C., Shakhnovich E. Functional fingerprints of folds: evidence for correlated structure-function evolution. J. Mol. Biol. 326:2003;1-9.
61. Rost B. Enzyme function less conserved than anticipated. J. Mol. Biol. 318:2002;595-608 Rost demonstrates that recent work implying that enzyme function is highly conserved within protein superfamilies may overestimate the level of conservation.
62. Anantharaman V., Aravind L., Koonin E.V. Emergence of diverse biochemical activities in evolutionarily conserved structural scaffolds of proteins. Curr. Opin. Chem. Biol. 7:2003;12-20 A comprehensive review of current work in assessing the structural basis of functional evolution. Common folds are observed to play roles in a variety of biochemical reactions and such ubiquity appears to involve generic, symmetrical substrate-binding sites.
63. Todd A.E., Orengo C.A., Thornton J.M. Plasticity of enzyme active sites. Trends Biochem. Sci. 27:2002;419-426.
64. Bashton M., Chothia C. The geometry of domain combination in proteins. J. Mol. Biol. 315:2002;927-939 This paper presents an analysis of the conservation of domain order within multidomain proteins. The authors observe that sequential order is the same in 98% of domain pairings. They note that sequential order in protein sequence appears not to affect the spatial arrangement of the protein structure. They conclude that sequential order is most probably conserved as it represents fewer chromosomal recombination events.
65. Rison S., Teichmann S., Thornton J. Homology, pathway distance and chromosomal localization of the small molecule metabolism enzymes in Escherichia coli. J. Mol. Biol. 318:2002;911-932 This paper deals with a subset of enzymatic E. coli genes. The thorough analysis shows that domains within a pathway are unlikely to be homologues of each another. Domains are most likely to be recruited between pathways and are recruited on the basis of reaction mechanism, rather than substrate-binding characteristics.
66. Teichmann S., Rison S., Thornton J., Riley M., Gough J., Chothia C. Small-molecule metabolism: an enzyme mosaic. Trends Biotechnol. 19:2001;482-486.
67. Tsoka S., Ouzounis C. Functional versatility and molecular diversity of the metabolic map of Escherichia coli. Genome Res. 11:2001;1503-1510.
68. Alves R., Chaleil R., Sternberg M. Evolution of enzymes in metabolism: a network perspective. J. Mol. Biol. 320:2002;751-770.
69. Jardine O., Gough J., Chothia C., Teichmann S. Comparison of the small molecule metabolic enzymes of Escherichia coli and Saccharomyces cerevisiae. Genome Res. 12:2002;916-929 The authors compare the small-molecule enzymes of E. coli and S. cerevisiae. They note that 271 families are common to both genomes. These involve 384 gene products and 390 gene products in E. coli and S. cerevisiae, respectively. This accounts for half of the E. coli and two-thirds of the S. cerevisiae small-molecule metabolic enzymes. Of the 271 common enzymes, over 66% have 30-50% sequence identity. Around this core set of enzymes, the organisms have very different extensions to small-molecule metabolism.
70. Babu M., Teichmann S. Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res. 31:2003;1234-1244.
71. Enright A.J., Van Dongen S., Ouzounis C.A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30:2002;1575-1584.
72. Heger A., Holm L. Exhaustive enumeration of protein domain families. J. Mol. Biol. 328:2003;749-767 An algorithm, ADDA, is presented for domain decomposition and clustering of protein domain families. This is applicable on the scale of all available protein sequences and allows a global survey of protein domain space.
73. Luscombe N.M., Qian J., Zhang Z., Johnson T., Gerstein M. The dominance of the population by a selected few: power-law behaviour applies to a wide variety of genomic properties. Genome Biol. 3:2002;0040.