Protein Structure Prediction Using Coarse-Grained Models

  • Maciej Blaszczyk
  • Dominik Gront
  • Sebastian Kmiecik
  • Mateusz Kurcinski
  • Michal Kolinski
  • Maciej Pawel Ciemny
  • Katarzyna Ziolkowska
  • Marta Panek
  • Andrzej KolinskiEmail author
Part of the Springer Series on Bio- and Neurosystems book series (SSBN, volume 8)


The knowledge of the three-dimensional structure of proteins is crucial for understanding many important biological processes. Most of the biologically relevant protein systems are too large for classical, atomistic molecular modeling tools. In such cases, coarse-grained (CG) models offer various opportunities for efficient conformational sampling and thus prediction of the three-dimensional structure. A variety of CG models have been proposed, each based on a similar framework consisting of a set of conceptual components such as protein representation, force field, sampling, etc. In this chapter we discuss these components, highlighting ideas which have proven to be the most successful. As CG methods are usually part of multistage procedures, we also describe approaches used for the incorporation of homology data and all-atom reconstruction methods.



Maciej Blaszczyk, Sebastian Kmiecik, Katarzyna Ziolkowska and Marta Panek acknowledge support from Foundation for Polish Science TEAM project (TEAM/2011-7/6) co-financed by the European Regional Development Fund operated within the Innovative Economy Operational Program. We also acknowledge support from the National Science Center (NCN Poland) Grant (MAESTRO2014/14/A/ST6/00088).


  1. 1.
    Abagyan, R.A., Mazur, A.K.: New methodology for computer-aided modelling of biomolecular structure and dynamics. 2. Local Deformations Cycles J. Biomol. Struct. Dyn. 6, 833–845 (1989). doi: citeulike-article-id:673543Google Scholar
  2. 2.
    Adcock, S.A.: Peptide backbone reconstruction using dead-end elimination and a knowledge-based forcefield. J. Comput. Chem. 25, 16–27 (2004). Scholar
  3. 3.
    Altschul, M., Simpson, K.W., Dykes, N.L., Mauldin, E.A., Reubi, J.C., Cummings, J.F.: Evaluation of somatostatin analogues for the detection and treatment of gastrinoma in a dog. J. Small Anim. Pract. 38, 286–291 (1997)CrossRefGoogle Scholar
  4. 4.
    Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997b). doi: gka562 [pii]Google Scholar
  5. 5.
    Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181, 223–230 (1973)CrossRefGoogle Scholar
  6. 6.
    Anfinsen, C.B., Haber, E., Sela, M., White Jr., F.H.: The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc. Natl. Acad. Sci. USA 47, 1309–1314 (1961)CrossRefGoogle Scholar
  7. 7.
    Berman, H., Henrick, K., Nakamura, H.: Announcing the worldwide protein data bank. Nat. Struct. Biol. 10, 980 (2003). nsb1203-980 [pii]CrossRefGoogle Scholar
  8. 8.
    Betancourt, M.: A reduced protein model with accurate native-structure identification ability. Proteins 53, 889–907 (2003). doi: citeulike-article-id:5200969Google Scholar
  9. 9.
    Blaszczyk, M., Kurcinski, M., Kouza, M., Wieteska, L., Debinski, A., Kolinski, A., Kmiecik, S.: Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods 93, 72–83 (2016). Scholar
  10. 10.
    Blundell, T., et al.: 18th Sir Hans Krebs lecture. Knowl.-Based Protein Model. Design Eur. J. Biochem. 172, 513–520 (1988)Google Scholar
  11. 11.
    Boniecki, M., Rotkiewicz, P., Skolnick, J., Kolinski, A.: Protein fragment reconstruction using various modeling techniques. J. Comput. Aided Mol. Des. 17, 725–738 (2003). doi: citeulike-article-id:668480Google Scholar
  12. 12.
    Buchete, N.V., Straub, J.E., Thirumalai, D.: Orientation-dependent coarse-grained potentials derived by statistical analysis of molecular structural databases Polymer 45, 597–608 (2004). doi: citeulike-article-id:10750645Google Scholar
  13. 13.
    Bystroff, C., Baker, D.: Prediction of local structure in proteins using a library of sequence-structure motifs. J. Mol. Biol. 281, 565–577 (1998). doi: citeulike-article-id:669894Google Scholar
  14. 14.
    Camproux, A.C., Gautier, R., Tuffery, P.: A hidden markov model derived structural alphabet for proteins. J. Mol. Biol. 339, 591–605 (2004). [pii]
  15. 15.
    Ciemny, M.P., Kurcinski, M., Blaszczyk, M., Kolinski, A., Kmiecik, S.: Modeling EphB4-EphrinB2 protein-protein interaction using flexible docking of a short linear motif. Biomed. Eng. Online 16, 71 (2017). Scholar
  16. 16.
    Ciemny, M.P., Kurcinski, M., Kozak, K.J., Kolinski, A., Kmiecik, S.: Highly flexible protein-peptide docking using CABS-Dock. Methods Mol. Biol. 1561, 69–94 (2017). Scholar
  17. 17.
    Ciemny, M.P., Debinski, A., Paczkowska, M., Kolinski, A., Kurcinski, M., Kmiecik, S.: Protein-peptide molecular docking with large-scale conformational changes: the p 53-MDM2 interaction. Sci. Rep. 6, 37532 (2016).
  18. 18.
    Ciemny, M., Kurcinski, M., Kamel, K., Kolinski, A., Alam, N., Schueler-Furman, O., Kmiecik, S.: Protein–peptide docking: opportunities and challenges. Drug Discov. Today 23(8), 1530–1537, ISSN 1359-6446 (2018). Scholar
  19. 19.
    Covell, D.G.: Folding protein alpha-carbon chains into compact forms by Monte Carlo methods. Proteins 14, 409–420 (1992). Scholar
  20. 20.
    Czaplewski, C., Liwo, A., Makowski, M., Ołdziej, S., Scheraga, H.A.: Coarse-grained models of proteins: theory and applications. In: Kolinski, A. (ed.) Multiscale approaches to protein modeling, pp. 85–109. Springer, New York (2011)Google Scholar
  21. 21.
    Czaplewski, C., Rodziewicz-Motowidlo, S., Liwo, A., Ripoll, D.R., Wawak, R.J., Scheraga, H.A.: Molecular simulation study of cooperativity in hydrophobic association. Protein Sci. 9, 1235–1245 (2000). Scholar
  22. 22.
    Dashevskii, V.G.: [Lattice model for globular protein three-dimensional structure] Mol. Biol. (Mosk) 14, 105–117 (1980)Google Scholar
  23. 23.
    Dawid, A.E., Gront, D., Kolinski, A.: SURPASS low-resolution coarse-grained protein modeling. J. Chem. Theor. Comput. 13, 5766–5779 (2017). Scholar
  24. 24.
    De Sancho, D., Rey, A.: Evaluation of coarse grained models for hydrogen bonds in proteins. J. Comput. Chem. 28 (2007). doi: citeulike-article-id:1127406Google Scholar
  25. 25.
    Eswar, N., Eramian, D., Webb, B., Shen, M.Y., Sali, A.: Protein structure modeling with MODELLER. Methods Mol. Biol. 426, 145–159 (2008). Scholar
  26. 26.
    Feig, M., Mirjalili, V.: Protein structure refinement via molecular-dynamics simulations: what works and what does not? Proteins 84(Suppl 1), 282–292 (2016). Scholar
  27. 27.
    Ferrenberg, A., Landau, D.P., Swendsen, R.: Statistical errors in histogram reweighting. Phys. Rev. E 51, 5092 (1995). doi:citeulike-article-id:875595Google Scholar
  28. 28.
    Ferrenberg, A., Swendsen, R.: Optimized Monte Carlo data analysis. Phys. Rev. Lett. 63, 1195–1198 (1989). doi:citeulike-article-id:774372Google Scholar
  29. 29.
    Fosgerau, K., Hoffmann, T.: Peptide therapeutics: current status and future directions. Drug Discov. Today 20, 122–128 (2015). Scholar
  30. 30.
    Gautier, R., Camproux, A.C., Tuffery, P.: SCit: web tools for protein side chain conformation analysis. Nucleic Acids Res. 32, W508–511 (2004). [pii]
  31. 31.
    Geyer, C.J.: Markov chain Monte Carlo maximum likelihood. In: Computing Science and Statistics: Proceedings of 23rd Symposium on the Interface Interface Foundation. Fairfax Station, pp. 156–163 (1991). doi: citeulike-article-id:606345Google Scholar
  32. 32.
    Go, N., Scheraga, H.: Ring closure and local conformational deformations of chain molecules. Macromolecules 3, 178–187 (1970)CrossRefGoogle Scholar
  33. 33.
    Go, N., Scheraga, H.A.: Ring-Closure in Chain Molecules with Cn, I, or S2n Symmetry. Macromolecules 6, 273–281 (1973)CrossRefGoogle Scholar
  34. 34.
    Godzik, A., Kolinski, A., Skolnick, J.: Lattice representations of globular proteins: how good are they? J. Comput. Chem. 14, 1194–1202 (1993). Scholar
  35. 35.
    Grishaev, A., Bax, A.: An empirical backbone–backbone hydrogen-bonding potential in proteins and its applications to NMR structure refinement and validation. J. Am. Chem. Soc. 126, 7281–7292 (2004). doi: citeulike-article-id:1896684Google Scholar
  36. 36.
    Gront, D., Kmiecik, S., Blaszczyk, M., Ekonomiuk, D., Koliński, A.: Optimization of protein models Wiley interdisciplinary reviews: computational molecular. Science 2, 479–493 (2012). Scholar
  37. 37.
    Gront, D., Kmiecik, S., Kolinski, A.: Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J. Comput. Chem. 28, 1593–1597 (2007). Scholar
  38. 38.
    Gront, D., Kolinski, A., Skolnick, J.: Comparison of three Monte Carlo conformational search strategies for a proteinlike homopolymer model: Folding thermodynamics and identification of low-energy structures. J. Chem. Phys. 113, 5065–5071 (2000). doi: citeulike-article-id:606324Google Scholar
  39. 39.
    Gront, D., Kolinski, A., Skolnick, J.: A new combination of replica exchange Monte Carlo and histogram analysis for protein folding and thermodynamics. J. Chem. Phys. 115, 1569–1574 (2001). doi: citeulike-article-id:876359Google Scholar
  40. 40.
    Gront, D., Kulp, D., Vernon, R., Strauss, C., Baker, D.: Generalized fragment picking in rosetta: design, protocols and applications. PLoS ONE 6, e23294 (2011). doi: citeulike-article-id:9705043Google Scholar
  41. 41.
    Guardiani, C., Livi, R., Cecconi, F.: Coarse Grained Modeling and Approaches to Protein Folding. Curr. Bioinform. 5, 217–240 (2010)CrossRefGoogle Scholar
  42. 42.
    Hansmann, U.: parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 281, 140–150 (1997). doi: citeulike-article-id:715765Google Scholar
  43. 43.
    Heath, A.P., Kavraki, L.E., Clementi, C.: From coarse-grain to all-atom: toward multiscale analysis of protein landscapes. Proteins 68, 646–661 (2007). Scholar
  44. 44.
    Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89, 10915–10919 (1992)CrossRefGoogle Scholar
  45. 45.
    Hinds, D.A., Levitt, M.: A lattice model for protein structure prediction at low resolution. Proc. Natl. Acad. Sci. USA 89, 2536–2540 (1992)CrossRefGoogle Scholar
  46. 46.
    Holm, L., Sander, C.: Database algorithm for generating protein backbone and side-chain co-ordinates from a C alpha trace application to model building and detection of co-ordinate errors. J. Mol. Biol. 218, 183–194 (1991). doi: 0022-2836(91)90883-8 [pii]Google Scholar
  47. 47.
    Illergard, K., Ardell, D.H., Elofsson, A.: Structure is three to ten times more conserved than sequence–a study of structural response in protein cores. Proteins 77, 499–508 (2009). Scholar
  48. 48.
    Irbäck, A., Mohanty, S.: PROFASI: A Monte Carlo simulation package for protein folding and aggregation. J. Comput. Chem. 27, 1548–1555 (2006). doi: citeulike-article-id:7290910Google Scholar
  49. 49.
    Jamroz, M., Kolinski, A.: Modeling of loops in proteins: a multi-method approach. BMC Struct. Biol. 10, 5+ (2010)Google Scholar
  50. 50.
    Jones, T.A., Thirup, S.: Using known substructures in protein model building and crystallography. EMBO J. 5, 819–822 (1986). doi: citeulike-article-id:705742Google Scholar
  51. 51.
    Karplus, M., McCammon, J.A.: Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9, 646–652 (2002). [pii]
  52. 52.
    Kazmierkiewicz, R., Liwo, A., Scheraga, H.A.: Energy-based reconstruction of a protein backbone from its alpha-carbon trace by a Monte-Carlo method. J. Comput. Chem. 23, 715–723 (2002). [pii]
  53. 53.
    Kazmierkiewicz, R., Liwo, A., Scheraga, H.A.: Addition of side chains to a known backbone with defined side-chain centroids. Biophys. Chem. 100, 261–280 (2003). doi: S0301462202002855 [pii]Google Scholar
  54. 54.
    Kelley, L.A., Mezulis, S., Yates, C.M., Wass, M.N., Sternberg, M.J.: The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015). [pii]
  55. 55.
    Kim, H., Kihara, D.: Protein structure prediction using residue- and fragment-environment potentials in CASP11. Proteins 84(Suppl 1), 105–117 (2016). Scholar
  56. 56.
    Kinch, L.N., Li, W., Monastyrskyy, B., Kryshtafovych, A., Grishin, N.V.: Evaluation of free modeling targets in CASP11 and ROLL. Proteins 84(Suppl 1), 51–66 (2016). Scholar
  57. 57.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983). doi: citeulike-article-id:379797Google Scholar
  58. 58.
    Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A.E., Kolinski, A.: Coarse-grained protein models and their applications. Chem. Rev. 116, 7898–7936 (2016). Scholar
  59. 59.
    Kmiecik, S., Jamroz, M., Kolinski, M.: Structure prediction of the second extracellular loop in G-protein-coupled receptors. Biophys. J. 106, 2408–2416 (2014). Scholar
  60. 60.
    Kolinski, A., Betancourt, M.R., Kihara, D., Rotkiewicz, P., Skolnick, J.: Generalized comparative modeling (GENECOMP): a combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement. Proteins 44, 133–149 (2001)CrossRefGoogle Scholar
  61. 61.
    Kolinski, M., Filipek, S.: Study of a structurally similar kappa opioid receptor agonist and antagonist pair by molecular dynamics simulations. J. Mol. Model. 16, 1567–1576 (2010). Scholar
  62. 62.
    Kolinski, A., Galazka, W., Skolnick, J.: Computer design of idealized beta-motifs. J. Chem. Phys. 103, 10286–10297 (1995)CrossRefGoogle Scholar
  63. 63.
    Kolinski, A., Ilkowski, B., Skolnick, J.: Dynamics and thermodynamics of beta-hairpin assembly: insights from various simulation techniques. Biophys. J. 77, 2942–2952 (1999)CrossRefGoogle Scholar
  64. 64.
    Kolinski, A., Milik, M., Rycombel, J., Skolnick, J.: A reduced model of short-range interactions in polypeptide-chains. J. Chem. Phys. 103, 4312–4323 (1995)CrossRefGoogle Scholar
  65. 65.
    Kolinski, A., Milik, M., Skolnick, J.: Static and dynamic properties of a new lattice model of polypeptide-chains. J. Chem. Phys. 94, 3978–3985 (1991)CrossRefGoogle Scholar
  66. 66.
    Kolinski, A., Skolnick, J.: Monte Carlo simulations of protein folding. I. Lattice model and interaction scheme. Proteins 18, 338–352 (1994). Scholar
  67. 67.
    Kolinski, A., Skolnick, J.: Reduced models of proteins and their applications. Polymer 45, 511–524 (2004). Scholar
  68. 68.
    Kolinski, A.: Protein modeling and structure prediction with a reduced representation. Acta Biochimica. Polonica 51, 349–371 (2004). doi: citeulike-article-id:606304Google Scholar
  69. 69.
    Kolinski, A., Gront, D.: Comparative modeling without implicit sequence alignments. Bioinformatics 23, 2522–2527 (2007). doi: btm380 [pii]
  70. 70.
    Kolinski, A., Rotkiewicz, P., Ilkowski, B., Skolnick, J.: A method for the improvement of threading-based protein models. Proteins 37, 592–610 (1999b).;2-2 [pii]CrossRefGoogle Scholar
  71. 71.
    Kolinski, A., Skolnick, J.: Lattice Models of Protein Folding, Dynamics and Thermodynamics. Landes (1996). doi: citeulike-article-id:877252Google Scholar
  72. 72.
    Kolinski, A., Skolnick, J.: Assembly of protein structure from sparse experimental data: an efficient Monte Carlo model. Proteins 32, 475–494 (1998).;2-f [pii]CrossRefGoogle Scholar
  73. 73.
    Kortemme, T., Morozov, A.V., Baker, D.: An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. J. Mol. Biol. 326, 1239–1259 (2003). doi: citeulike-article-id:556189Google Scholar
  74. 74.
    Krigbaum, W.R., Lin, S.F.: Monte-Carlo simulation of protein folding using a lattice model. Macromolecules 15, 1135–1145 (1982)CrossRefGoogle Scholar
  75. 75.
    Krivov, G.G., Shapovalov, M.V., Dunbrack Jr., R.L.: Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77, 778–795 (2009). Scholar
  76. 76.
    Krupa, P., Mozolewska, M.A., Joo, K., Lee, J., Czaplewski, C., Liwo, A.: Prediction of protein structure by template-based modeling combined with the UNRES force field. J. Chem. Inf. Model. 55, 1271–1281 (2015). Scholar
  77. 77.
    Krupa, P., Sieradzan, A.K., Mozolewska, M.A., Li, H., Liwo, A., Scheraga, H.A.: Dynamics of disulfide-bond disruption and formation in the thermal unfolding of ribonuclease A. J. Chem. Theor. Comput. 13, 5721–5730 (2017). Scholar
  78. 78.
    Krupa, P., et al.: Performance of protein-structure predictions with the physics-based UNRES force field in CASP11. Bioinformatics 32, 3270–3278 (2016). doi:btw404 [pii]
  79. 79.
    Kryshtafovych, A., Fidelis, K., Moult, J.: CASP9 results compared to those of previous CASP experiments. Proteins 79(Suppl 10), 196–207 (2011). Scholar
  80. 80.
    Kumar, S., Rosenberg, J., Bouzida, D., Swendsen, R., Kollman, P.: Multidimensional free-energy calculations using the weighted histogram analysis method. J. Comput. Chem. 16, 1339–1350 (1995). doi: citeulike-article-id:774417Google Scholar
  81. 81.
    Kurcinski, M., Jamroz, M., Blaszczyk, M., Kolinski, A., Kmiecik, S.: CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res. 43, W419–424 (2015). [pii]
  82. 82.
    Kwak, W., Hansmann, U.H.: Efficient sampling of protein structures by model hopping. Phys. Rev. Lett. 95, 138102 (2005). Scholar
  83. 83.
    Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157, 105–132 (1982). doi: 0022-2836(82)90515-0 [pii]Google Scholar
  84. 84.
    Lee, H., Heo, L., Lee, M.S., Seok, C.: GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res. 43, W431–W435 (2015). Scholar
  85. 85.
    Lee, J., Scheraga, H.A., Rackovsky, S.: New optimization method for conformational energy calculations on polypeptides: conformational space annealing. J. Comput. Chem. 18, 1222–1232 (1997)CrossRefGoogle Scholar
  86. 86.
    Levitt, M.: A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 104, 59–107 (1976). doi: citeulike-article-id:4000523Google Scholar
  87. 87.
    Levitt, M., Warshel, A.: Computer simulation of protein folding. Nature 253, 694–698 (1975). doi: citeulike-article-id:4275709Google Scholar
  88. 88.
    Levy-Moonshine, A., Amir, E-a. D., Keasar, C.: Enhancement of beta-sheet assembly by cooperative hydrogen bonds potential. Bioinformatics 25, 2639–2645 (2009). doi: citeulike-article-id:7012194Google Scholar
  89. 89.
    Li, Z., Scheraga, H.A.: Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc. Natl. Acad. Sci. USA 84, 6611–6615 (1987)MathSciNetCrossRefGoogle Scholar
  90. 90.
    Liwo, A., He, Y., Scheraga, H.A.: Coarse-grained force field: general folding theory. Phys. Chem. Chem. Phys. 13, 16890–16901 (2011). Scholar
  91. 91.
    Liwo, A., et al.: Simulation of Protein Structure and Dynamics with the Coarse-Grained UNRES Force Field. Coarse-Graining of Condensed Phase and Biomolecular Systems. CRC Press (2008). doi: citeulike-article-id:3822586Google Scholar
  92. 92.
    Liwo, A., Czaplewski, C., Pillardy, J., Scheraga, H.: Cumulant-based expressions for the multibody terms for the correlation between local and electrostatic interactions in the united-residue force field. J. Chem. Phys. 115, 2323–2347 (2001). doi: citeulike-article-id:715745Google Scholar
  93. 93.
    Liwo, A., Khalili, M., Scheraga, H.: Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains. Proc. Natl. Acad. Sci. U.S.A. 102, 2362–2367 (2005). doi: citeulike-article-id:1365687Google Scholar
  94. 94.
    Liwo, A., Pincus, M.R., Wawak, R.J., Rackovsky, S., Scheraga, H.A.: Prediction of protein conformation on the basis of a search for compact structures: test on avian pancreatic polypeptide. Protein Sci.: Publ. Protein Soc. 2, 1715–1731 (1993). doi: citeulike-article-id:7558759Google Scholar
  95. 95.
    London, N., Raveh, B., Cohen, E., Fathi, G., Schueler-Furman, O.: Rosetta FlexPepDock web server–high resolution modeling of peptide-protein interactions. Nucleic Acids Res. 39, W249–W253 (2011). Scholar
  96. 96.
    Maupetit, J., Gautier, R., Tuffery, P.: SABBAC: Online structural alphabet-based protein BackBone reconstruction from alpha-carbon trace. Nucleic Acids Res. 34, W147–151 (2006). doi: 34/suppl_2/W147 [pii]
  97. 97.
    Mazur, A.K., Dorofeev, V.E., Abagyan, R.A.: Derivation and testing of explicit equations of motion for polymers described by internal coordinates. J. Comput. Phys. 92, 261–272 (1991). doi: citeulike-article-id:10750684Google Scholar
  98. 98.
    Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953). doi: citeulike-article-id:531300Google Scholar
  99. 99.
    Metropolis, N., Ulam, S.: The Monte Carlo method. J. Am. Stat. Assoc. 44, 335–341 (1949). doi: citeulike-article-id:1886002Google Scholar
  100. 100.
    Milik, M., Kolinski, A., Skolnick, J.: Algorithm for rapid reconstruction of protein backbone from alpha carbon coordinates. J. Comput. Chem. 18, 80–85 (1997)CrossRefGoogle Scholar
  101. 101.
    Mitsutake, A., Sugita, Y., Okamoto, Y.: Generalized-ensemble algorithms for molecular simulations of biopolymers. Biopolymers 60, 96–123 (2001).;2-f [pii];2-F
  102. 102.
    Morozov, A., Lin, S.: Accuracy and convergence of the Wang-Landau sampling algorithm. Phys. Rev. E (Statistical, Nonlinear, and Soft Matter Physics) 76 (2007). doi: citeulike-article-id:3802626Google Scholar
  103. 103.
    Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A.: Critical assessment of methods of protein structure prediction (CASP)-round XII. Proteins (2017). Scholar
  104. 104.
    Moult, J., Fidelis, K., Kryshtafovych, A., Tramontano, A.: Critical assessment of methods of protein structure prediction (CASP)–round IX. Proteins 79(Suppl 10), 1–5 (2011). Scholar
  105. 105.
    Mozolewska, M.A., Krupa, P., Zaborowski, B., Liwo, A., Lee, J., Joo, K., Czaplewski, C.: Use of restraints from consensus fragments of multiple server models to enhance protein-structure prediction capability of the UNRES force field. J. Chem. Inf. Model. 56, 2263–2279 (2016). Scholar
  106. 106.
    Park, B.H., Levitt, M.: The complexity and accuracy of discrete state models of protein structure. J. Mol. Biol. 249, 493–507 (1995). doi: citeulike-article-id:5845728Google Scholar
  107. 107.
    Parsons, J., Holmes, B., Rojas, M., Tsai, J., Strauss, C.: Practical conversion from torsion space to Cartesian space forin silico protein synthesis. J. Comput. Chem. 26, 1063–1068 (2005). doi: citeulike-article-id:1036763Google Scholar
  108. 108.
    Payne, P.W.: Reconstruction of protein conformations from estimated positions of the C-alpha coordinates. Protein Sci. 2, 315–324 (1993)CrossRefGoogle Scholar
  109. 109.
    Peterson, L.X., et al.: Modeling the assembly order of multimeric heteroprotein complexes. PLoS Comput. Biol. 14, e1005937 (2018). [pii]
  110. 110.
    Pruitt, K.D., Tatusova, T., Maglott, D.R.: NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–65 (2007). doi: gkl842 [pii]
  111. 111.
    Pundir, S., Martin, M.J., O’Donovan, C.: UniProt protein knowledgebase methods. Mol. Biol. 1558, 41–55 (2017). Scholar
  112. 112.
    Raveh, B., London, N., Zimmerman, L., Schueler-Furman, O.: Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS ONE 6, e18934 (2011). Scholar
  113. 113.
    Rohl, C., Strauss, C., Misura, K., Baker, D.: Protein structure prediction using Rosetta. In: Numerical Computer Methods, Part D, vol. 383, pp. 66–93. Elsevier (2004). doi: citeulike-article-id:441859Google Scholar
  114. 114.
    Rose, P.W., et al.: The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 45, D271–D281 (2017). Scholar
  115. 115.
    Rotkiewicz, P., Skolnick, J.: Fast procedure for reconstruction of full-atom protein models from reduced representations. J. Comput. Chem. 29, 1460–1465 (2008). Scholar
  116. 116.
    Sali, A., et al.: Outcome of the first wwPDB hybrid/integrative methods task force workshop. Structure 23, 1156–1167 (2015). Scholar
  117. 117.
    Sali, A., Blundell, T.L.: Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993). doi: S0022-2836(83)71626-8 [pii]
  118. 118.
    Scheraga, H.A., Khalili, M., Liwo, A.: Protein-folding dynamics: overview of molecular simulation techniques. Annu. Rev. Phys. Chem. 58, 57–83 (2007). Scholar
  119. 119.
    Schindler, C.E., de Vries, S.J., Zacharias, M.: iATTRACT: simultaneous global and local interface optimization for protein-protein docking refinement. Proteins 83, 248–258 (2015). Scholar
  120. 120.
    Schindler, C.E., de Vries, S.J., Zacharias, M.: Fully blind peptide-protein docking with pepATTRACT. Structure 23, 1507–1515 (2015a). [pii]
  121. 121.
    Shenoy, S.R., Jayaram, B.: Proteins: sequence to structure and function–current status. Curr. Protein Pept. Sci. 11, 498–514 (2010)CrossRefGoogle Scholar
  122. 122.
    Shi, J., Blundell, T.L., Mizuguchi, K.: FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J. Mol. Biol. 310, 243–257 (2001). [pii]
  123. 123.
    Shin, W.H., Christoffer, C.W., Kihara, D.: In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 131, 22–32 (2017). doi: S1046-2023(17)30208-6 [pii]
  124. 124.
    Sippl, M.J.: Boltzmann’s principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures. J. Comput. Aided Mol. Des. 7, 473–501 (1993)CrossRefGoogle Scholar
  125. 125.
    Skolnick, J., Kolinski, A.: Dynamic Monte Carlo simulations of globular protein folding/unfolding pathways. I. Six-member, Greek key beta-barrel proteins. J. Mol. Biol. 212, 787–817 (1990a). doi:0022-2836(90)90237-G [pii]Google Scholar
  126. 126.
    Skolnick, J., Kolinski, A.: Simulations of the folding of a globular protein. Science 250, 1121–1125 (1990b). doi: 250/4984/1121 [pii]
  127. 127.
    Skolnick, J., Kolinski, A., Brooks III, C.L., Godzik, A., Rey, A.: A method for predicting protein structure from sequence. Curr. Biol. 3, 414–423 (1993). doi:0960-9822(93)90348-R [pii]Google Scholar
  128. 128.
    Soding, J.: Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951–960 (2005). doi: bti125 [pii]
  129. 129.
    Stein, A., Kortemme, T.: Improvements to robotics-inspired conformational sampling in rosetta. PLoS ONE 8, e63090 (2013). [pii]
  130. 130.
    Stumpff-Kane, A.W., Maksimiak, K., Lee, M.S., Feig, M.: Sampling of near-native protein conformations during protein structure refinement using a coarse-grained model, normal modes, and molecular dynamics simulations. Proteins 70, 1345–1356 (2008). Scholar
  131. 131.
    Sugita, Y., Okamoto, Y.: Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999). doi:citeulike-article-id:197524Google Scholar
  132. 132.
    Swendsen, R., Wang, J.: Replica Monte Carlo simulation of spin-glasses. Phys. Rev. Lett. 57, 2607–2609 (1986). doi: citeulike-article-id:773436Google Scholar
  133. 133.
    Tai, C.H., Bai, H., Taylor, T.J., Lee, B.: Assessment of template-free modeling in CASP10 and ROLL. Proteins 82(Suppl 2), 57–83 (2014). Scholar
  134. 134.
    Thompson, J., Baker, D.: Incorporation of evolutionary information into Rosetta comparative modeling. Proteins 79, 2380–2388 (2011). Scholar
  135. 135.
    Trabuco, L.G., Lise, S., Petsalaki, E., Russell, R.B.: PepSite: prediction of peptide-binding sites from protein surfaces. Nucleic Acids Res. 40, W423–W427 (2012). Scholar
  136. 136.
    Trojanowski, S., Rutkowska, A., Kolinski, A.: TRACER. A new approach to comparative modeling that combines threading with free-space conformational sampling. Acta Biochim. Pol. 57, 125–133 (2010)Google Scholar
  137. 137.
    UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158-D169 (2017)
  138. 138.
    Vendruscolo, M., Najmanovich, R., Domany, E.: Can a pairwise contact potential stabilize native protein folds against decoys obtained by threading? Proteins 38, 134–148 (2000).;2-a [pii]CrossRefGoogle Scholar
  139. 139.
    Vinals, J., Kolinski, A., Skolnick, J.: Numerical study of the entropy loss of dimerization and the folding thermodynamics of the GCN4 leucine zipper. Biophys. J. 83, 2801–2811 (2002). doi: S0006-3495(02)75289-2 [pii]
  140. 140.
    Voth, G. (ed): Coarse-Graining of Condensed Phase and Biomolecular Systems. CRC Press Taylor & Francis, Farmington, CT (2008)Google Scholar
  141. 141.
    Wabik, J., Kurcinski, M., Kolinski, A.: Coarse-grained modeling of peptide docking associated with large conformation transitions of the binding protein: Troponin I fragment-Troponin C system. Molecules 20, 10763–10780 (2015). Scholar
  142. 142.
    Wales, D.: Energy Landscapes: Applications to Clusters, Biomolecules and Glasses (Cambridge Molecular Science). Cambridge University Press (2004). doi: citeulike-article-id:755112Google Scholar
  143. 143.
    Wang, T., Wu, M.B., Zhang, R.H., Chen, Z.J., Hua, C., Lin, J.P., Yang, L.R.: Advances in computational structure-based drug design and application in drug discovery. Curr. Top Med. Chem. 16, 901–916 (2016). doi: CTMC-EPUB-69847 [pii]Google Scholar
  144. 144.
    Wedemeyer, W.J., Scheraga, H.A.: Exact analytical loop closure in proteins using polynomial equations. J. Comput. Chem. 20, 819–844 (1999)CrossRefGoogle Scholar
  145. 145.
    Xu, D., Zhang, J., Roy, A., Zhang, Y.: Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins 79(Suppl 10), 147–160 (2011). Scholar
  146. 146.
    Yan, C.H., et al.: Minimal residual disease- and graft-vs.-host disease-guided multiple consolidation chemotherapy and donor lymphocyte infusion prevent second acute leukemia relapse after allotransplant. J. Hematol. Oncol. 9, 87 (2016). Scholar
  147. 147.
    Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., Zhang, Y.: The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12(1), 7–8 (2015). Scholar
  148. 148.
    Zhang, J., He, Z., Wang, Q., Barz, B., Kosztin, I., Shang, Y., Xu, D.: Prediction of protein tertiary structures using MUFOLD methods. Mol. Biol. 815, 3–13 (2012). Scholar
  149. 149.
    Zhang, Y.: Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10. Proteins 82(Suppl 2), 175–187 (2014). Scholar
  150. 150.
    Zheng, W.: Accurate flexible fitting of high-resolution protein structures into cryo-electron microscopy maps using coarse-grained pseudo-energy minimization. Biophys. J. 100, 478–488 (2011). doi: S0006-3495(10)05186-6 [pii]Google Scholar
  151. 151.
    Zhou, H., Zhou, Y.: Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11, 2714–2726 (2002). Scholar
  152. 152.
    Zhou, R., et al.: Folding kinetics of WW domains with the united residue force field for bridging microscopic motions and experimental measurements. Proc. Natl. Acad. Sci. U.S.A. 111, 18243–18248 (2014). [pii]

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maciej Blaszczyk
    • 1
  • Dominik Gront
    • 1
  • Sebastian Kmiecik
    • 1
  • Mateusz Kurcinski
    • 1
  • Michal Kolinski
    • 2
  • Maciej Pawel Ciemny
    • 1
    • 3
  • Katarzyna Ziolkowska
    • 1
  • Marta Panek
    • 1
  • Andrzej Kolinski
    • 1
    Email author
  1. 1.Faculty of Chemistry, Biological and Chemical Research CentreUniversity of WarsawWarsawPoland
  2. 2.Bioinformatics LaboratoryMossakowski Medical Research Centre, Polish Academy of SciencesWarsawPoland
  3. 3.Faculty of PhysicsUniversity of WarsawWarsawPoland

Personalised recommendations