Letters in Health and Biological Sciences | Volume 3 | Issue 1 | 2018-06-26

Structural and Functional Bioinformatics

NIDA KHAN

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.

Keywords: Cath, Scop, Haddock, Prosite, Vanted, Brenda

Full text

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. Pubmed || Crossref || Others 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). Pubmed || Crossref || Others 3. Barton, G.J. Protein secondary structure prediction. (1995) Current opinion in structural biology 5(3): 372-376. Pubmed || Crossref || Others 4. Bordoli, L., Kiefer, F., Arnold, K., et al. Protein structure homology modeling using SWISS-MODEL workspace. (2009) Nat Protoc 4(1): 1-13. Pubmed || Crossref || Others 5. Cristobal, S., Zemla, A., Fischer, D., et al. A study of quality measures for protein threading models. (2001) BMC bioinformatics 2(1): 5. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 8. Rost,B., Sander,C. Prediction of protein secondary structure at better than 70% accuracy. (1993) J Mol Biol 232(2): 584-599. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 13. Altschul, S.F., Gish, W., Miller, W., et al. Basic local alignment search tool. (1990) J Mol Biol 215(3): 403-410. Pubmed || Crossref || Others 14. Kaplan, W., Littlejohn, T.G. Swiss-PDB viewer (deep view). (2001) Brief Bioinform 2(2): 195-197. Pubmed || Crossref || Others 15. Laskowski, R.A. PDBsum: summaries and analyses of PDB structures. (2001) Nucleic Acids Res 29(1): 221-222. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 18. Orengo, C.A., Pearl, F.M., Thornton, J.M.The CATH domain structure database. (2005) Structural Bioinformatics 44: 249-271. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 20. Hulo,N., Sigrist, C.J., Le Saux,V., et al. Recent improvements to the PROSITE database. (2004) Nucleic Acids Res 32: 134-137. Pubmed || Crossref || Others 21. Bateman, A., Birney, E., Cerruti, L., et al. The Pfam protein families database. (2002) Nucleic acids research 30(1): 276-280. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 23. Martens, L., Hermjakob, H., Jones, P., et al. PRIDE: the proteomics identifications database. (2005) Proteomics 5(13): 3537-3545. Pubmed || Crossref || Others 24. Dinasarapu, A.R., Saunders, B., Ozerlat, I., et al. Signaling gateway molecule pages a data model perspective. (2011) Bioinformatics 27(12): 1736-1738. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 29. Bader, G.D., Betel, D., Hogue, C.W. BIND: the biomolecular interaction network database. (2003) Nucleic acids research 31(1): 248-250. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 34. Khatri, P., Draghici, S., Ostermeier, G.C., et al. Profiling gene expression using onto-express. (2002) Genomics 79(2): 266-270. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 39. Castillo-Davis, C.I., Hartl, D.L. GeneMerge post-genomic analysis, data mining, and hypothesis testing.(2003) Bioinformatics 19(7): 891-892. Pubmed || Crossref || Others 40. Berriz, G.F., King, O.D., Bryant, B., et al. characterizing gene sets with FuncAssociate. (2003) Bioinformatics 19(18): 2502-2504. Pubmed || Crossref || Others 41. Milanowska, K., Krwawicz, J., Papaj, G., et al. REPAIRtoire a database of DNA repair pathways. (2010) Nucleic acids res 39(1): 788-792. Pubmed || Crossref || Others 42. Milanowska, K., Rother, K., Bujnicki, J.M. Databases and bioinformatics tools for the study of DNA repair. (2011) Mol biol int: 2011: 475718. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 46. Milanowska, K., Rother, K., Bujnicki, J.M. Databases and bioinformatics tools for the study of DNA repair. (2011) Mol biol int 2011: 475718. Pubmed || Crossref || Others 47. Podlevsky, J.D., Bley, C.J., Omana, R.V., et al. The telomerase database. (2008) Nucleic Acids Res 36(1): 339-343. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 50. Allen, J.E., Salzberg, S.L. JIGSAW: integration of multiple sources of evidence for gene prediction. (2005) Bioinformatics 21(18): 3596-3603. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others 57. Howard, K. The bioinformatics gold rush. (2000) Sci Am 283(1): 58-63. Pubmed || Crossref || Others 58. Sud, M., Fahy, E., Cotter, D., et al. Lmsd: lipid maps structure database. (2007) Nucleic acids Res 35(1): 527-532. Pubmed || Crossref || Others 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. Pubmed || Crossref || Others