Predicting Real-Valued Protein Residue Fluctuation Using FlexPred

  • Lenna Peterson
  • Michal Jamroz
  • Andrzej Kolinski
  • Daisuke Kihara
Part of the Methods in Molecular Biology book series (MIMB, volume 1484)


The conventional view of a protein structure as static provides only a limited picture. There is increasing evidence that protein dynamics are often vital to protein function including interaction with partners such as other proteins, nucleic acids, and small molecules. Considering flexibility is also important in applications such as computational protein docking and protein design. While residue flexibility is partially indicated by experimental measures such as the B-factor from X-ray crystallography and ensemble fluctuation from nuclear magnetic resonance (NMR) spectroscopy as well as computational molecular dynamics (MD) simulation, these techniques are resource-intensive. In this chapter, we describe the web server and stand-alone version of FlexPred, which rapidly predicts absolute per-residue fluctuation from a three-dimensional protein structure. On a set of 592 nonredundant structures, comparing the fluctuations predicted by FlexPred to the observed fluctuations in MD simulations showed an average correlation coefficient of 0.669 and an average root mean square error of 1.07 Å. FlexPred is available at

Key words

Bioinformatics Computational biology Support vector machine Support vector regression Protein residue fluctuation Protein flexibility Protein conformational flexibility Protein structure Protein design Molecular dynamics 



This work was partly supported by the National Institute of General Medical Sciences of the National Institutes of Health (R01GM097528) and the National Science Foundation (IIS1319551, DBI1262189, IOS1127027).


  1. 1.
    Teilum K, Olsen JG, Kragelund BB (2009) Functional aspects of protein flexibility. Cell Mol Life Sci 66:2231–2247CrossRefPubMedGoogle Scholar
  2. 2.
    Hammes GG, Benkovic SJ, Hammes-Schiffer S (2011) Flexibility, diversity, and cooperativity: pillars of enzyme catalysis. Biochemistry 50:10422–10430CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Zacharias M (2010) Accounting for conformational changes during protein–protein docking. Curr Opin Struct Biol 20:180–186CrossRefPubMedGoogle Scholar
  4. 4.
    Mandell DJ, Kortemme T (2009) Backbone flexibility in computational protein design. Curr Opin Biotechnol 20:420–428CrossRefPubMedGoogle Scholar
  5. 5.
    Lassila JK (2010) Conformational diversity and computational enzyme design. Curr Opin Chem Biol 14:676–682CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lill MA (2011) Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry 50:6157–6169CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Debye P (1913) Interferenz von Röntgenstrahlen und Wärmebewegung. Ann Phys 348:49–92CrossRefGoogle Scholar
  8. 8.
    Eastman P, Pellegrini M, Doniach S (1999) Protein flexibility in solution and in crystals. J Chem Phys 110:10141CrossRefGoogle Scholar
  9. 9.
    Ishima R, Torchia DA (2000) Protein dynamics from NMR. Nat Struct Biol 7:740–743CrossRefPubMedGoogle Scholar
  10. 10.
    Baldwin AJ, Kay LE (2009) NMR spectroscopy brings invisible protein states into focus. Nat Chem Biol 5:808–814CrossRefPubMedGoogle Scholar
  11. 11.
    Nilges M, Habeck M, O’Donoghue SI, Rieping W (2006) Error distribution derived NOE distance restraints. Proteins 64:652–664CrossRefPubMedGoogle Scholar
  12. 12.
    Chalaoux F-R, O’Donoghue SI, Nilges M (1999) Molecular dynamics and accuracy of NMR structures: effects of error bounds and data removal. Proteins 34:453–463CrossRefPubMedGoogle Scholar
  13. 13.
    Wang Q, Matsui T, Domitrovic T, Zheng Y, Doerschuk PC, Johnson JE (2013) Dynamics in cryo EM reconstructions visualized with maximum-likelihood derived variance maps. J Struct Biol 181:195–206CrossRefPubMedGoogle Scholar
  14. 14.
    Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19:120–127CrossRefPubMedGoogle Scholar
  15. 15.
    Liwo A, Oldziej S, Pincus MR, Wawak RJ, Rackovsky S, Scheraga HA (1997) A united-residue force field for off-lattice protein-structure simulations. I. Functional forms and parameters of long-range side-chain interaction potentials from protein crystal data. J Comput Chem 18:849–873CrossRefGoogle Scholar
  16. 16.
    Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51:349–371PubMedGoogle Scholar
  17. 17.
    Jamroz M, Kolinski A, Kmiecik S (2013) CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res 41:W427–W431CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Jamroz M, Orozco M, Kolinski A, Kmiecik S (2013) Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J Chem Theory Comput 9:119–125CrossRefPubMedGoogle Scholar
  19. 19.
    Jamroz M, Kolinski A, Kmiecik S (2014) CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics 30:2150–2154CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Brooks B, Karplus M (1983) Harmonic dynamics of proteins: normal modes and fluctuations in bovine pancreatic trypsin inhibitor. Proc Natl Acad Sci U S A 80:6571–6575CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Haliloglu T, Bahar I, Erman B (1997) Gaussian dynamics of folded proteins. Phys Rev Lett 79:3090–3093CrossRefGoogle Scholar
  22. 22.
    Tirion MM (1996) Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys Rev Lett 77:1905–1908CrossRefPubMedGoogle Scholar
  23. 23.
    Bahar I, Erman B, Haliloglu T, Jernigan RL (1997) Efficient characterization of collective motions and interresidue correlations in proteins by low-resolution simulations. Biochemistry 36:13512–13523CrossRefPubMedGoogle Scholar
  24. 24.
    Yang L, Song G, Jernigan RL (2009) Protein elastic network models and the ranges of cooperativity. Proc Natl Acad Sci U S A 106:12347–12352CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Kondrashov DA, Cui Q, Phillips GN Jr (2006) Optimization and evaluation of a coarse-grained model of protein motion using X-ray crystal data. Biophys J 91:2760–2767CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Lin T-L, Song G (2010) Generalized spring tensor models for protein fluctuation dynamics and conformation changes. BMC Struct Biol 10:S3CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Micheletti C, Carloni P, Maritan A (2004) Accurate and efficient description of protein vibrational dynamics: comparing molecular dynamics and Gaussian models. Proteins 55:635–645CrossRefPubMedGoogle Scholar
  28. 28.
    Canino LS, Shen T, McCammon JA (2002) Changes in flexibility upon binding: application of the self-consistent pair contact probability method to protein-protein interactions. J Chem Phys 117:9927CrossRefGoogle Scholar
  29. 29.
    Opron K, Xia K, Wei G-W (2015) Communication: capturing protein multiscale thermal fluctuations. J Chem Phys 142:211101CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Pandey BP, Zhang C, Yuan XZ, Zi J, Zhou YQ (2005) Protein flexibility prediction by an all-atom mean-field statistical theory. Protein Sci 14:1772–1777CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Zhang H, Kurgan L (2014) Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models. Amino Acids 46:2665–2680CrossRefPubMedGoogle Scholar
  32. 32.
    Chen P, Wang B, Wong H-S, Huang D-S (2007) Prediction of protein B-factors using multi-class bounded SVM. Protein Pept Lett 14:185–190CrossRefPubMedGoogle Scholar
  33. 33.
    Schlessinger A, Yachdav G, Rost B (2006) PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 22:891–893CrossRefPubMedGoogle Scholar
  34. 34.
    Hirose S, Yokota K, Kuroda Y, Wako H, Endo S, Kanai S, Noguchi T (2010) Prediction of protein motions from amino acid sequence and its application to protein-protein interaction. BMC Struct Biol 10:20CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Gu J, Gribskov M, Bourne PE (2006) Wiggle—predicting functionally flexible regions from primary sequence. PLoS Comput Biol 2:e90CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    de Brevern AG, Bornot A, Craveur P, Etchebest C, Gelly J-C (2012) PredyFlexy: flexibility and local structure prediction from sequence. Nucleic Acids Res 40:W317–W322CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Jamroz M, Kolinski A, Kihara D (2012) Structural features that predict real-value fluctuations of globular proteins. Proteins 80:1425–1435CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Shih C-H, Huang S-W, Yen S-C, Lai Y-L, Yu S-H, Hwang J-K (2007) A simple way to compute protein dynamics without a mechanical model. Proteins 68:34–38CrossRefPubMedGoogle Scholar
  39. 39.
    Kloczkowski A, Jernigan RL, Wu Z, Song G, Yang L, Kolinski A, Pokarowski P (2009) Distance matrix-based approach to protein structure prediction. J Struct Funct Genomics 10:67–81CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Lin C-P, Huang S-W, Lai Y-L, Yen S-C, Shih C-H, Lu C-H, Huang C-C, Hwang J-K (2008) Deriving protein dynamical properties from weighted protein contact number. Proteins 72:929–935CrossRefPubMedGoogle Scholar
  41. 41.
    Halle B (2002) Flexibility and packing in proteins. Proc Natl Acad Sci U S A 99:1274–1279CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132CrossRefPubMedGoogle Scholar
  43. 43.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637CrossRefPubMedGoogle Scholar
  44. 44.
    Miller S, Janin J, Lesk AM, Chothia C (1987) Interior and surface of monomeric proteins. J Mol Biol 196:641–656CrossRefPubMedGoogle Scholar
  45. 45.
    Chakravarty S, Varadarajan R (1999) Residue depth: a novel parameter for the analysis of protein structure and stability. Structure 7:723–732CrossRefPubMedGoogle Scholar
  46. 46.
    Hamelryck T (2005) An amino acid has two sides: a new 2D measure provides a different view of solvent exposure. Proteins 59:38–48CrossRefPubMedGoogle Scholar
  47. 47.
    Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27Google Scholar
  48. 48.
    Meyer T, D’Abramo M, Hospital A et al (2010) MoDEL (molecular dynamics extended library): a database of atomistic molecular dynamics trajectories. Structure 18:1399–1409CrossRefPubMedGoogle Scholar
  49. 49.
    Shamoo Y, Friedman AM, Parsons MR, Konigsberg WH, Steitz TA (1995) Crystal structure of a replication fork single-stranded DNA binding protein (T4 gp32) complexed to DNA. Nature 376:362–366CrossRefPubMedGoogle Scholar
  50. 50.
    Kurokawa H, Osawa M, Kurihara H, Katayama N, Tokumitsu H, Swindells MB, Kainosho M, Ikura M (2001) Target-induced conformational adaptation of calmodulin revealed by the crystal structure of a complex with nematode Ca2+/calmodulin-dependent kinase kinase peptide. J Mol Biol 312:59–68CrossRefPubMedGoogle Scholar
  51. 51.
    Wolfe SA, Zhou P, Dötsch V, Chen L, You A, Ho SN, Crabtree GR, Wagner G, Verdine GL (1997) Unusual Rel-like architecture in the DNA-binding domain of the transcription factor NFATc. Nature 385:172–176CrossRefPubMedGoogle Scholar
  52. 52.
    Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372:774–797CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Lenna Peterson
    • 1
  • Michal Jamroz
    • 2
  • Andrzej Kolinski
    • 2
  • Daisuke Kihara
    • 1
    • 3
  1. 1.Department of Biological Sciences, College of SciencePurdue UniversityWest LafayetteUSA
  2. 2.Laboratory of Theory of Biopolymers, Faculty of ChemistryUniversity of WarsawWarszawaPoland
  3. 3.Department of Computer Science, College of SciencePurdue UniversityWest LafayetteUSA

Personalised recommendations