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MaxMod: a hidden Markov model based novel interface to MODELLER for improved prediction of protein 3D models

  • Bikram K. Parida
  • Prasanna K. Panda
  • Namrata Misra
  • Barada K. Mishra
Original Paper

Abstract

Modeling the three-dimensional (3D) structures of proteins assumes great significance because of its manifold applications in biomolecular research. Toward this goal, we present MaxMod, a graphical user interface (GUI) of the MODELLER program that combines profile hidden Markov model (profile HMM) method with Clustal Omega program to significantly improve the selection of homologous templates and target-template alignment for construction of accurate 3D protein models. MaxMod distinguishes itself from other existing GUIs of MODELLER software by implementing effortless modeling of proteins using templates that bear modified residues. Additionally, it provides various features such as loop optimization, express modeling (a feature where protein model can be generated directly from its sequence, without any further user intervention) and automatic update of PDB database, thus enhancing the user-friendly control of computational tasks. We find that HMM-based MaxMod performs better than other modeling packages in terms of execution time and model quality. MaxMod is freely available as a downloadable standalone tool for academic and non-commercial purpose at http://www.immt.res.in/maxmod/.

Graphical Abstract

Overview of steps involved in protein modeling using MaxMod

Keywords

Clustal omega Graphical user interface Hidden markov model Homology modeling Modified residues 

Notes

Acknowledgments

NM is grateful to Council of Scientific and Industrial Research, Govt. of India for the award of Senior Research Fellowship. The authors would also like to thank the members of CNeM department, CSIR-IMMT for providing server space and hosting the website of MaxMod.

Supplementary material

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References

  1. 1.
    Cavasotto CN, Patak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14(13–14):676–683CrossRefGoogle Scholar
  2. 2.
    Barton GJ (1998) Protein Sequence alignment techniques. Acta Cryst D 54:1139–1146CrossRefGoogle Scholar
  3. 3.
    Vyas VK, Ukawala RD, Ghate M, Chintha C (2012) Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 74(1):1–17CrossRefGoogle Scholar
  4. 4.
    Dalton JA, Jackson RM (2007) An evaluation of automated homology modeling methods at low target-template sequence similarity. Bioinformatics 23((5):1901–1908CrossRefGoogle Scholar
  5. 5.
    Sali A, Blundell TL (1993) Comparative protein Ssructure modelling by satisfaction of spacial restrants. J Mol Biol 234(3):779–815CrossRefGoogle Scholar
  6. 6.
    Kuntal BK, Aporoy P, Reddanna P (2010) EasyModeller: a graphical interface to MODELLER. BMC Res Notes 3:226–230Google Scholar
  7. 7.
    Mathur A, Vidyarthi AS (2011) SWIFT MODELLER: a JAVA based GUI for molecular modeling. J Mol Model 17(10):2601–2607Google Scholar
  8. 8.
    Bramucci E, Paiardini A, Bossa F, Pascarella S (2012) PyMod: sequence similarity searches, multiple sequence-structure alignments, and homology modeling within PyMOL. BMC Bioinformatics 13: Suppl 4:S2Google Scholar
  9. 9.
    Saxena A, Sangwan RS, Mishra A (2013) Fundamentals of homology modelling steps and comparison among important bioinformatics tools: an overview. Sci Int 1(7):237–252Google Scholar
  10. 10.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410CrossRefGoogle Scholar
  11. 11.
    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic. Acids Res 25(17)):3389–3402Google Scholar
  12. 12.
    Pearson WR (2000) (2000) Flexible sequence similarity searching with the FASTA3 program package. Methods Mol Biol 132:185–219Google Scholar
  13. 13.
    Eddy SR (2011) Accelerated Profile HMM Searches. PLoS Comput Biol 7(10):e1002195Google Scholar
  14. 14.
    Krogh A, Brown M, Mian IS, Sjolander K, Haussler D (1994) Hidden Markov models in computational biology: applications to protein modeling. J Mol Biol 235(5):1501–1531Google Scholar
  15. 15.
    Eddy SR (1998) Profile hidden markov models. Bioinformatics 4(9):755–763CrossRefGoogle Scholar
  16. 16.
    Yan R, Xu D, Yang J, Walker S, Zhang Y (2013) A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Sci Rep 3:2619–2627Google Scholar
  17. 17.
    Sauder JM, Arthur JW, Dunbrack RL Jr (2000) Large-scale comparison of protein sequence alignment algorithms with structure alignments. Proteins 40(1):6–22CrossRefGoogle Scholar
  18. 18.
    Edgar RC, Sjolander KA (2004) Comparison of scoring functions for protein sequence profile alignment. Bioinformatics 20(8):1301–1308CrossRefGoogle Scholar
  19. 19.
    Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539Google Scholar
  20. 20.
    Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291CrossRefGoogle Scholar
  21. 21.
    Fetrow JS (1995) Omega loops: nonregular secondary structures significant in protein function and stability. FASEB J 9(9)):708–717Google Scholar
  22. 22.
    Fiser A, Do RK, Sali A (2000) Modeling of loops in protein structures. Protein Sci 9(9):1753–1773CrossRefGoogle Scholar
  23. 23.
    Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170Google Scholar
  24. 24.
    Luthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83–85CrossRefGoogle Scholar
  25. 25.
    Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucl Acids Res 35:W407–410CrossRefGoogle Scholar
  26. 26.
    Sippl MJ (1995) Knowledge-based potentials for proteins. Curr Opin Struct Biol 5(2):229–235CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Bikram K. Parida
    • 1
  • Prasanna K. Panda
    • 1
    • 2
  • Namrata Misra
    • 2
  • Barada K. Mishra
    • 1
    • 2
  1. 1.Bioresources Engineering DepartmentCSIR-Institute of Minerals & Materials TechnologyBhubaneswarIndia
  2. 2.Academy of Scientific & Innovative ResearchCSIR- CSIR-Institute of Minerals & Materials TechnologyBhubaneswarIndia

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