Structural and Functional Bioinformatics
Affiliation
Department of Biotechnology, Faculty of Life Sciences and Informatics, Balochistan University of Information Technology Engineering and Management Sciences, (BUITEMS), Quetta, Pakistan
Corresponding Author
Nida Tabassum Khan, Department of Biotechnology, Faculty of Life Sciences and Informatics, Balochistan University of Information Technology Engineering and Management Sciences, (BUITEMS), Quetta, Pakistan, Tel: 03368164903; E-mail: nidatabassumkhan@yahoo.com
Citation
Khan, N.T. Structural and Functional Bioinformatics. (2018) Lett Health Biol Sci 3(1): 7- 11.
Copy rights
© 2018 Khan, N.T. This is an Open access article distributed under the terms of Creative Commons Attribution 4.0 International License.
Keywords
Cath; Scop; Haddock; Prosite; Vanted; Brenda
Abstract
Structural and functional bioinformatics help us to design and formulate prognostic computational models and frameworks that exploit our growing knowledge of biological macromolecules in terms of their structural organization and functional capabilities. Integration of structural and functional biochemistry of macromolecules with informatics empowers significant progress in understanding the fundamentals of biology.
Introduction
Structural and functional bioinformatics aimed to unravel biological problems by analyzing sequences of biological molecules such as DNA and protein using computational algorithms, informatics tools and software’s to assess molecular data[1]. Some of the applications of this novel field are given below:
Prediction of protein structure: Understanding the correlation between amino acid sequence and the three dimensional structure of protein, it can be helpful for determining protein structure from its amino acid sequence[2]. Numerous bioinformatics tools could be utilized for protein structure and function prediction including secondary structure prediction[3], homology modeling[4], protein threading[5], ab initio methods[6], prediction of motif[7], domain[8], transmembrane helix[9], signal peptide[10] etc. Some of these tools and databases used are summarized below in Table 1.
Table 1: Protein structure/function prediction software’s and databases
|
S.no |
Tools and Databases |
Purpose |
|
1 |
PDB (Protein Data Bank) |
Universal storage of 3D structure data of macromolecules. Providing methods for visualizing the structure and downloading structural information[11] |
|
2 |
MMDB (NCBI Structure Database) |
Includes database of 3D structure of bimolecules. Provides information on biological functions of proteins, on mechanisms related to their functions and on relationship between biomolecules and their evolutionary history[12] |
|
3 |
BLAST (Basic Local Alignment Search Tool) |
Searches for regions of local similarity in a protein or DNA sequence[13] |
|
4 |
SWISS PDB Viewer |
Analyze structural alignment of proteins and provide comparison of their active sites, their amino acid mutations angles, distances and H bonds between their atoms[14] |
|
5 |
PDB sum |
Consists of images of structure, detailed structural analysis derived from PROMOTIF program, schematic graphs of interactions, summary PROCHEK results etc[15] |
|
6 |
Ligplot |
Determine interaction between proteins, ligands, hydrogen atoms, hydrophobic interactions etc[16] |
|
7 |
SCOP |
Database containing detailed information of protein structure and phylogenetics[17] |
|
8 |
CATH (Class, Architecture, Topology and Homologous super family) |
Database that stores hierarchical classification for domain structures of proteins[18] |
|
9 |
Composition Profiler |
Bioinformatics tool for determining amino acids enrichment or depletion based on their physicochemical or structural features[19] |
|
10 |
Prosite/ Interpro |
Determines protein families, conserved domains, motifs etc[20] |
|
11 |
Pfam |
Determines protein families[21] |
|
12 |
SWISS PROT |
Database of annotated protein sequences[22] |
|
13 |
PRIDE (Proteomics Identifications Database) |
Contain information on functional characterization and post-translation modification of proteins and peptides[23] |
|
14 |
SGMP (Signaling Gateway Molecule Pages) |
Database contains information on functional states of proteins involved in signal transduction pathways[24] |
|
15 |
Prot Param |
Tool to find proteins physico-chemical properties[25] |
|
18 |
SMART (Simple Modular Architecture Retrieval Tool) |
Defines multiple information about the query protein[26] |
|
19 |
Auto Dock |
Determines protein-ligand interaction[27] |
|
20 |
HADDOCK |
Determines modeling and bio-molecular[28] |
|
21 |
BIND |
Database that provides information on molecular interaction of biological molecules[29] |
|
22 |
APSSP2 |
Determines proteins secondary structure[30] |
|
23 |
MODELLER |
Determines3D structure of protein[31] |
|
24 |
Phyre and Phyre2 |
Find protein structure[32] |
Few bioinformatics tools and databases for functional analysis of large gene are summarized below in Table 2.
Table 2: Tools for structural and functional analysis of gene.
|
S.no |
Tools and Databases |
Purpose |
|
1 |
Gene Ontology |
Systematically dissect large gene lists[33] |
|
2 |
Onto-Express, MAP Finder, Go Miner, DAVID, EASE, Gene Merge Func Associate etc |
Analysis of gene-annotation enrichment[34-40] |
|
3 |
REPAIRtoire (Examples Repair GENES, Human DNA Repair Genes, Repair-Fun Map and GeneSNPs etc) |
Contains data on all DNA repair systems and proteins from model organisms[41,42] |
|
4 |
BRENDA (BRaunschweig Enzyme Database) |
Database contains information on enzymes properties etc[43] |
|
5 |
Pathway Commons |
Contains data on biological pathways including macromolecule interactions, biochemical reactions, complex assembly, transport, catalysis events, etc[44] |
|
6 |
OriDB (DNA replication), Data base (Replication Domain, apoptosis), Telomerase database (telomere maintenance), REBASE (DNA restriction and modification), and DAnCER (epigenetic/chromatin modification ) |
Databases contains data on DNA metabolism[45-49] |
|
7 |
JIGSAW |
Determine genes, splicing sites etc[50] |
|
8 |
novoSNP |
Find point mutation in DNA sequence[51] |
|
9 |
PPP (Prokaryotic promoter prediction) |
Tool to determine promoter in a gene[52] |
|
10 |
WebGeSTer |
Database to find the termination sites during transcription in the genes[53] |
|
11 |
Genscan |
Determine exon-intron sites in sequences[54] |
Few bioinformatics tools and databases for analysis of lipids are summarized below in Table 3.
Table 3: Tools for analysis of lipids.
|
S.no |
Tools and Databases |
Purpose |
|
1 |
magnet |
Software enables retrieval and visualization of biological relationships across heterogeneous data sources from an integrated database[55] |
|
2 |
VANTED |
Enable importing and customization of KEGG lipid-specific pathways[56] |
|
3 |
GOLD LIPID MAPS Proteome Database (LMPD), LIPID BANK, LIPIDAT and LMSD |
Database of genomics of lipid-associated disorders[57-60] |
Conclusion
Progress in the field of structural and functional bioinformatics includes future contribution to structural and functional understanding of the macromolecules such as DNA, proteins, lipids etc for the better apprehension of the biological processes and pathways on which the origin of life rely.
References
- 1. Chou, K.C. Structural bioinformatics and its impact to biomedical science. (2004) Current medicinal chemistry 11(16): 2105-2134.
- 2. Rokde,C.N., Kshirsagar, M. Bioinformatics: Protein structure prediction.(2013) In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on IEEE: (1-5).
- 3. Barton, G.J. Protein secondary structure prediction. (1995) Current opinion in structural biology 5(3): 372-376.
- 4. Bordoli, L., Kiefer, F., Arnold, K., et al. Protein structure homology modeling using SWISS-MODEL workspace. (2009) Nat Protoc 4(1): 1-13.
- 5. Cristobal, S., Zemla, A., Fischer, D., et al. A study of quality measures for protein threading models. (2001) BMC bioinformatics 2(1): 5.
- 6. Chivian, D., Robertson, T., Bonneau, R., et al. Ab initio methods. (2003) Structural Bioinformatics: 547-557.
Pubmed || Crossref || Others
- 7. Rost, B., Liu,J., Nair,R., et al. Automatic prediction of protein function. (2003) Cell Mol Life Sci 60(12): 2637-2650.
- 8. Rost,B., Sander,C. Prediction of protein secondary structure at better than 70% accuracy. (1993) J Mol Biol 232(2): 584-599.
- 9. Krogh, A., Larsson, B., Von Heijne, G., et al. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. (2001) J Mol Biol 305(3): 567-580.
- 10. Emanuelsson, O., Nielsen, H., Brunak, S., et al. Predicting sub cellular localization of proteins based on their N-terminal amino acid sequence. (2000) J Mol Biol 300(4): 1005-1016.
- 11. Berman, H.M., Westbrook, J., Feng, Z., et al. The Protein Data Bank, 1999–. (2006) In International Tables for Crystallography Volume F: Crystallography of biological macromolecules: 675-684.
Pubmed || Crossref || Others
- 12. Chen, J., Anderson, J.B., DeWeese-Scott, C., et al. MMDB: Entrez’s 3D-structure database. (2003)Nucleic Acids Res 31(1): 474-477.
- 13. Altschul, S.F., Gish, W., Miller, W., et al. Basic local alignment search tool. (1990) J Mol Biol 215(3): 403-410.
- 14. Kaplan, W., Littlejohn, T.G. Swiss-PDB viewer (deep view). (2001) Brief Bioinform 2(2): 195-197.
- 15. Laskowski, R.A. PDBsum: summaries and analyses of PDB structures. (2001) Nucleic Acids Res 29(1): 221-222.
- 16. Wallace, A.C., Laskowski, R.A., Thornton, J.M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. (1995) Protein Eng 8(2): 127-134.
- 17. Murzin, A.G., Brenner, S.E., Hubbard, T., et al. SCOP: a structural classification of proteins database for the investigation of sequences and structures. (1995) J Mol Biol 247(4): 536-540.
- 18. Orengo, C.A., Pearl, F.M., Thornton, J.M.The CATH domain structure database. (2005) Structural Bioinformatics 44: 249-271.
- 19. Vacic,V., Uversky, V.N., Dunker, A.K., et al. Composition Profiler: a tool for discovery and visualization of amino acid composition differences.(2007)BMC Bioinformatics 8: 211.
- 20. Hulo,N., Sigrist, C.J., Le Saux,V., et al. Recent improvements to the PROSITE database. (2004) Nucleic Acids Res 32: 134-137.
- 21. Bateman, A., Birney, E., Cerruti, L., et al. The Pfam protein families’ database. (2002) Nucleic acids research 30(1): 276-280.
- 22. Boeckmann, B., Bairoch, A., Apweiler, R., et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. (2003) Nucleic Acids Res 31(1): 365-370.
- 23. Martens, L., Hermjakob, H., Jones, P., et al. PRIDE: the proteomics identifications database. (2005) Proteomics 5(13): 3537-3545.
- 24. Dinasarapu, A.R., Saunders, B., Ozerlat, I., et al. Signaling gateway molecule pages—a data model perspective. (2011) Bioinformatics 27(12): 1736-1738.
- 25. Bendtsen, J.D., Jensen, L.J., Blom, N., et al. Feature-based prediction of non-classical and leaderless protein secretion. (2004) Protein Eng Des Sel 17(4): 349-356.
- 26. Schultz, J., Copley, R.R., Doerks, T., et al. SMART: a web-based tool for the study of genetically mobile domains. (2000) Nucleic Acids Res 28(1): 231-234.
- 27. Goodsell, D.S., Morris, G.M., Olson, A.J. Automated docking of flexible ligands: applications of AutoDock. (1996) J Mol Recognit 9(1): 1-5.
- 28. De Vries, S.J., Van Dijk, M., Bonvin, A.M. The HADDOCK web server for data-driven bimolecular docking. (2010) Nat Protoc 5(5): 883-897.
- 29. Bader, G.D., Betel, D., Hogue, C.W. BIND: the biomolecular interaction network database. (2003) Nucleic acids research 31(1): 248-250.
- 30. Zhang, H., Tang, H., Zhang, L., et al. Evaluation on prediction methods of protein secondary structure. (2003) Computers and Applied Chemistry 20(6): 735-740.
Pubmed || Crossref || Others
- 31. Webb, B., Sali, A. Protein structure modeling with MODELLER. (2014) Protein Structure Prediction: 1-15.
Pubmed || Crossref || Others
- 32. Kelley, L.A., Mezulis, S., Yates, C.M., et al. The Phyre2 web portal for protein modeling, prediction and analysis. (2015) Nat Protoc 10(6): 845-858.
- 33. Harris, M.A., Clark, J., Ireland, A., et al. Gene Ontology Consortium. The Gene Ontology (GO) database and informatics resource. (2004) Nucleic Acids Res 32(1): 258-261.
- 34. Khatri, P., Draghici, S., Ostermeier, G.C., et al. Profiling gene expression using onto-express. (2002) Genomics 79(2): 266-270.
- 35. Doniger, S.W., Salomonis, N., Dahlquist, K.D., et al. MAPP Finder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. (2003) Genome Biol 4(1): R7.
- 36. Zeeberg, B.R., Feng, W., Wang, G., et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. (2003) Genome Biol 4(4): R28.
- 37. Huang da,W., Sherman, B.T., Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.(2009) Nat Protoc 4(1): 44-57.
- 38. Gentleman, R.C., Carey, V.J., Bates, D.M., et al. Bioconductor: open software development for computational biology and bioinformatics.(2004) Genome Biol 5(10): R80.
- 39. Castillo-Davis, C.I., Hartl, D.L. GeneMerge—post-genomic analysis, data mining, and hypothesis testing.(2003) Bioinformatics 19(7): 891-892.
- 40. Berriz, G.F., King, O.D., Bryant, B., et al. characterizing gene sets with FuncAssociate. (2003) Bioinformatics 19(18): 2502-2504.
- 41. Milanowska, K., Krwawicz, J., Papaj, G., et al. REPAIRtoire—a database of DNA repair pathways. (2010) Nucleic acids res 39(1): 788-792.
- 42. Milanowska, K., Rother, K., Bujnicki, J.M. Databases and bioinformatics tools for the study of DNA repair. (2011) Mol biol int: 2011: 475718.
- 43. Schomburg, I., Chang, A., Ebeling, C., et al. BRENDA, the enzyme database: updates and major new developments. (2004) Nucleic Acids Res 32(1): 431-433.
- 44. Cerami, E.G., Gross, B.E., Demir, E., et al. Pathway Commons, a web resource for biological pathway data.(2011) Nucleic acids res 39(1): 685-690.
- 45. Nieduszynski, C.A., Hiraga, S.I., Ak, P., et al. OriDB: a DNA replication origin database.(2006) Nucleic Acids Res 35(1): 40-46.
- 46. Milanowska, K., Rother, K., Bujnicki, J.M. Databases and bioinformatics tools for the study of DNA repair. (2011) Mol biol int 2011: 475718.
- 47. Podlevsky, J.D., Bley, C.J., Omana, R.V., et al. The telomerase database. (2008) Nucleic Acids Res 36(1): 339-343.
- 48. Roberts, R.J., Vincze, T., Posfai, J., et al. REBASE—a database for DNA restriction and modification: enzymes, genes and genomes. (2009) Nucleic Acids Res 38(1): 234-236.
- 49. Rother, M.B., van Attikum, H. DNA repair goes hip-hop: SMARCA and CHD chromatin remodellers join the break dance. (2017) Philos Trans R Soc Lond B Biol Sci 372(1731): 20160285.
- 50. Allen, J.E., Salzberg, S.L. JIGSAW: integration of multiple sources of evidence for gene prediction. (2005) Bioinformatics 21(18): 3596-3603.
- 51. Doran, A.G., Creevey, C.J. Snpdat: easy and rapid annotation of results from de novo snp discovery projects for model and non-model organisms. (2013) BMC bioinformatics14 (1): 45.
- 52. Sun,P., Ju, H., Liu, Z., et al. Bioinformatics resources and tools for conformational B-cell epitope prediction.(2013) Comput Math methods med: 2013: 943636.
- 53. Kumar,V., Sharma,R.M., Thakur,R.S. Big Data Analytics: Bioinformatics Perspective.(2016) Inte Jo Inno Adv Com Sci 5(6): 8-14.
Pubmed || Crossref || Others
- 54. Kruglyak, L., Nickerson, D.A. Variation is the spice of life. (2001) Nat genet 27(3): 234-236.
- 55. Sharman, J.L., Gerloff, D.L. MaGnET: a software tool for integrated visualization of functional genomic data relating to the malaria parasite. (2007) BMC Systems Bio1 (1): 32.
- 56. Junker,B.H., Klukas, C., Schreiber, F. VANTED: a system for advanced data analysis and visualization in the context of biological networks.(2006) BMC bioinformatics7 (1): 109.
- 57. Howard, K. The bioinformatics gold rush. (2000) Sci Am 283(1): 58-63.
- 58. Sud, M., Fahy, E., Cotter, D., et al. Lmsd: lipid maps structure database. (2007) Nucleic acids Res 35(1): 527-532.
- 59. Watanabe, K., Yasugi, E., Oshima, M. How to search the glycolipid data in “LIPIDBANK for Web”, the newly developed lipid database in Japan. (2000) Trends in Glycoscience and Glycotechnology 12(65): 175-184.
Pubmed || Crossref || Others
- 60. Yetukuri, L., Ekroos, K., Vidal-Puig, A., et al. Informatics and computational strategies for the study of lipids. (2008) Mol BioSyst 4(2): 121-127.
