Structural Bioinformatics

  • Bhumi Patel
  • Vijai Singh
  • Dhaval Patel


The scientific fraternity is enthusiastic to identify how molecular sequence could reveal unexplained mysteries in biology. This includes evolutionary studies, gaining insights into cellular biology, identifying virulence factors in pathogens affecting animals and plant crops, and revealing essential drug targets. Eventually, a need for experimental scientists to adopt the computational or the bioinformatic domain has been rising. Biological macromolecules follow a function: an observed function requires a structural basis, or, the inverse of the structure, in turn, can be seen to influence function. Thus, if we identify the structure or hypothesize a structural model, we can infer many aspects of biochemical function and support it experimentally. One can use/develop better tools to model a more statistically valid structure for predicting a biological function, hence reducing the search space and resources usage. This will help to design experiments on testing only the most likely functions. Remarkable progress has been made in structure prediction algorithm which has somewhat decreased the clear demarcation between homology modeling, fold recognition, and threading approach. The convergence of advances in understanding physiochemical principles of protein folding, efficient algorithms, and high-end computational resources is driving the accurate prediction of protein structure and function.


Protein structure prediction Homology modeling Fold recognition Ab initio prediction Structure-based drug discovery Model validation Protein superposition 


  1. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410. Scholar
  2. Altschul SF, Madden TL, Schäffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402PubMedPubMedCentralGoogle Scholar
  3. Andreoli F, Del Rio A (2015) Computer-aided molecular Design of Compounds Targeting Histone Modifying Enzymes. Comput Struct Biotechnol J 13:358–365. Scholar
  4. Anfinsen CB, Haber E, Sela M, White FH (1961) The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc Natl Acad Sci U S A 47:1309–1314PubMedPubMedCentralGoogle Scholar
  5. Benkert P, Tosatto SCE, Schomburg D (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins 71:261–277. Scholar
  6. Biasini M, Bienert S, Waterhouse A et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42:W252–W258. Scholar
  7. Blikstad C, Dahlström KM, Salminen TA, Widersten M (2014) Substrate scope and selectivity in offspring to an enzyme subjected to directed evolution. FEBS J 281:2387–2398. Scholar
  8. Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170PubMedGoogle Scholar
  9. Boyle J (2005) In: Nelson D, Cox M (eds). Biochemistry and Molecular Biology EducationLehninger principles of biochemistry, vol 33, 4th edn, pp 74–75. Scholar
  10. Chan AWE, Hutchinson EG, Harris D, Thornton JM (1993) Identification, classification, and analysis of beta-bulges in proteins. Protein Sci 2:1574–1590. Scholar
  11. Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823–826PubMedPubMedCentralGoogle Scholar
  12. Dalton J, Kalid O, Schushan M et al (2012) New model of cystic fibrosis transmembrane conductance regulator proposes active channel-like conformation. J Chem Inf Model 52:1842–1853. Scholar
  13. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797. Scholar
  14. Gu J, Bourne PE (2009) Structural bioinformatics. Wiley-BlackwellGoogle Scholar
  15. Heinke R, Carlino L, Kannan S et al (2011) Computer- and structure-based lead design for epigenetic targets. Bioorg Med Chem 19:3605–3615. Scholar
  16. Hermann JC, Marti-Arbona R, Fedorov AA et al (2007) Structure-based activity prediction for an enzyme of unknown function. Nature 448:775–779. Scholar
  17. Hillisch A, Pineda LF, Hilgenfeld R (2004) Utility of homology models in the drug discovery process. Drug Discov Today 9:659–669. Scholar
  18. Holm L, Sander C (1995) Dali: a network tool for protein structure comparison. Trends Biochem Sci 20:478–480PubMedGoogle Scholar
  19. Huggins ML (1943) The structure of fibrous proteins. Chem Rev 32:195–218. Scholar
  20. Jayachandran G, Vishal V, Pande VS (2006) Using massively parallel simulation and Markovian models to study protein folding: examining the dynamics of the villin headpiece. J Chem Phys 124:164902. Scholar
  21. Jones DT, Taylort WR, Thornton JM (1992) A new approach to protein fold recognition. Nature 358:86–89. Scholar
  22. Jones DT, Tress M, Bryson K, Hadley C (1999) Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure. Proteins Suppl 3:104–111PubMedGoogle Scholar
  23. Kendrew JC, Bodo G, Dintzis HM et al (1958) A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature 181:662–666. Scholar
  24. Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32:W526–W531. Scholar
  25. Kmiecik S, Gront D, Kolinski M et al (2016) Coarse-grained protein models and their applications. Chem Rev 116:7898–7936. Scholar
  26. Kopp J, Schwede T (2004) Automated protein structure homology modeling: a progress report. Pharmacogenomics 5:405–416. Scholar
  27. Kuroda D, Shirai H, Jacobson MP, Nakamura H (2012) Computer-aided antibody design. Protein Eng Des Sel 25:507–522. Scholar
  28. Low BW, Baybutt RB (1952) The π helix—a hydrogen bonded configuration of the polypeptide chain. J Am Chem Soc 74:5806–5807. Scholar
  29. Maffeo C, Bhattacharya S, Yoo J et al (2012) Modeling and simulation of ion channels. Chem Rev 112:6250–6284. Scholar
  30. Medina-Franco JL, Caulfield T (2011) Advances in the computational development of DNA methyltransferase inhibitors. Drug Discov Today 16:418–425. Scholar
  31. Michalsky E, Goede A, Preissner R (2003) Loops in proteins (LIP)--a comprehensive loop database for homology modelling. Protein Eng 16:979–985. Scholar
  32. Mirsky AE, Pauling L (1936) On the structure of native, denatured, and coagulated proteins. Proc Natl Acad Sci U S A 22:439–447PubMedPubMedCentralGoogle Scholar
  33. Moult J, Fidelis K, Kryshtafovych A et al (2018) Critical assessment of methods of protein structure prediction (CASP)-round XII. Proteins 86:7–15. Scholar
  34. Notredame C, Higgins DG, Heringa J (2000) T-coffee: a novel method for fast and accurate multiple sequence alignment 1 1Edited by J. Thornton. J Mol Biol 302:205–217. Scholar
  35. Pauling L, Corey RB, Branson HR (1951) The structure of proteins; two hydrogen-bonded helical configurations of the polypeptide chain. Proc Natl Acad Sci U S A 37:205–211. Scholar
  36. Perutz MF, Rossmann MG, Cullis AF et al (1960) Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5-a. resolution, obtained by X-ray analysis. Nature 185:416–422PubMedGoogle Scholar
  37. Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99PubMedGoogle Scholar
  38. Richardson JS, Getzoff ED, Richardson DC (1978) The beta bulge: a common small unit of nonrepetitive protein structure. Proc Natl Acad Sci U S A 75:2574–2578PubMedPubMedCentralGoogle Scholar
  39. Rödel W (1974) J. S. Fruton: molecules and life – historical essays on the interplay of chemistry and biology. 579 Seiten, vol 18. Wiley-Interscience, New York/London/Sydney/Toronto 1972. Preis: 8,95 £. Food / Nahrung, pp 471–472. Scholar
  40. Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815. Scholar
  41. Shen M, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524. Scholar
  42. Shindyalov IN, Bourne PE (1998) Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 11:739–747PubMedGoogle Scholar
  43. Sillitoe I, Lewis TE, Cuff A et al (2015) CATH: comprehensive structural and functional annotations for genome sequences. Nucleic Acids Res 43:D376–D381. Scholar
  44. Svedberg T, Fåhraeus R (1926) A new method for the determination of the molecular weight of the proteins. J Am Chem Soc 48:430–438. Scholar
  45. Taylor WR (1986a) The classification of amino acid conservation. J Theor Biol 119:205–218. Scholar
  46. Taylor WR (1986b) Identification of protein sequence homology by consensus template alignment. J Mol Biol 188:233–258PubMedGoogle Scholar
  47. Taylor HS Large molecules through atomic spectacles. Proc Am Philos Soc 85:1–12Google Scholar
  48. Taylor WR, Orengo CA (1989) Protein structure alignment. J Mol Biol 208:1–22PubMedGoogle Scholar
  49. Xiang Z, Soto CS, Honig B (2002) Evaluating conformational free energies: the colony energy and its application to the problem of loop prediction. Proc Natl Acad Sci 99:7432–7437. Scholar
  50. 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 101:2525–2534. Scholar
  51. Xu D, Zhang Y (2013) Toward optimal fragment generations for ab initio protein structure assembly. Proteins 81:229–239. Scholar
  52. Zhang J, Liang Y, Zhang Y (2011) Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 19:1784–1795. Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bhumi Patel
    • 1
  • Vijai Singh
    • 2
  • Dhaval Patel
    • 1
  1. 1.Department of Bioinformatics & Structural Biology, School of Biological Sciences and BiotechnologyInstitute of Advanced ResearchGandhinagarIndia
  2. 2.School of Biological Sciences and BiotechnologyInstitute of Advanced ResearchGandhinagarIndia

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