Computational Modelling of Protein Complex Structure and Assembly

  • Jonathan N. Wells
  • L. Therese Bergendahl
  • Joseph A. Marsh
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1764)

Abstract

Sequence and structure space are nowadays sufficiently large that we can use computational methods to model the structure of proteins based on sequence similarity alone. Not only useful as a standalone tool, homology modelling has also had a transformative effect on the ease with which we can solve crystal structures and electron density maps. Another technique—molecular dynamics—aims to model protein structures from first principles and, thanks to increases in computational power, is slowly becoming a viable tool for studying protein complexes. Finally, the prediction of protein assembly pathways from three-dimensional structures of complexes is also now becoming possible.

Key words

Protein interactions Template-based modelling Docking Molecular dynamics Assembly 

Notes

Acknowledgment

J.M. is supported by a Medical Research Council Career Development Award (MR/M02122X/1).

References

  1. 1.
    Anfinsen CB (1973) Principles that govern the folding of protein chains. Science 181:223–230. https://doi.org/10.1126/science.181.4096.223 CrossRefPubMedGoogle Scholar
  2. 2.
    Moult J, Pedersen JT, Judson R, Fidelis K (1995) A large-scale experiment to assess protein structure prediction methods. Proteins Struct Funct Genet 23:ii–iv. https://doi.org/10.1002/prot.340230303 CrossRefPubMedGoogle Scholar
  3. 3.
    Janin J, Henrick K, Moult J et al (2003) CAPRI: a Critical Assessment of PRedicted Interactions. Proteins Struct Funct Genet 52:2–9. https://doi.org/10.1002/prot.10381 CrossRefPubMedGoogle Scholar
  4. 4.
    Haas J, Roth S, Arnold K et al (2013) The protein model portal–a comprehensive resource for protein structure and model information. Database 2013:bat031–bat031. https://doi.org/10.1093/database/bat031 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Moult J, Fidelis K, Kryshtafovych A et al (2016) Critical assessment of methods of protein structure prediction: progress and new directions in round XI. Proteins 84:4–14. https://doi.org/10.1002/prot.25064 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Jiang Z-Y, Chu H-X, Xi M-Y et al (2013) Insight into the intermolecular recognition mechanism between Keap1 and IKKβ combining homology modelling, protein-protein docking, molecular dynamics simulations and virtual alanine mutation. PLoS One 8:e75076. https://doi.org/10.1371/journal.pone.0075076 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Rajapaksha H, Petrovsky N (2014) In silico structural homology modelling and docking for assessment of pandemic potential of a novel H7N9 influenza virus and its ability to be neutralized by existing anti-hemagglutinin antibodies. PLoS One 9:e102618. https://doi.org/10.1371/journal.pone.0102618 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Agostino M, Mancera RL, Ramsland PA, Fernández-Recio J (2016) Optimization of protein-protein docking for predicting Fc-protein interactions. J Mol Recognit 29:555–568. https://doi.org/10.1002/jmr.2555 CrossRefPubMedGoogle Scholar
  9. 9.
    Lensink MF, Velankar S, Kryshtafovych A et al (2016) Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment. Proteins 84:323–348. https://doi.org/10.1002/prot.25007 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5(4):823–826PubMedPubMedCentralGoogle Scholar
  11. 11.
    Chen H, Skolnick J (2008) M-TASSER: an algorithm for protein quaternary structure prediction. Biophys J 94:918–928. https://doi.org/10.1529/biophysj.107.114280 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Tuncbag N, Gursoy A, Nussinov R, Keskin O (2011) Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat Protoc 6:1341–1354. https://doi.org/10.1038/nprot.2011.367 CrossRefPubMedGoogle Scholar
  13. 13.
    Guerler A, Govindarajoo B, Zhang Y (2013) Mapping monomeric threading to protein-protein structure prediction. J Chem Inf Model 53:717–725. https://doi.org/10.1021/ci300579r CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Bowie J, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170. https://doi.org/10.1126/science.1853201 CrossRefPubMedGoogle Scholar
  15. 15.
    Lu L, Lu H, Skolnick J (2002) Multiprospector: an algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins Struct Funct Genet 49:350–364. https://doi.org/10.1002/prot.10222 CrossRefPubMedGoogle Scholar
  16. 16.
    Szilagyi A, Zhang Y (2014) Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 24:10–23. https://doi.org/10.1016/j.sbi.2013.11.005 CrossRefPubMedGoogle Scholar
  17. 17.
    Huang S-Y (2014) Search strategies and evaluation in protein–protein docking: principles, advances and challenges. Drug Discov Today 19:1081–1096. https://doi.org/10.1016/j.drudis.2014.02.005 CrossRefPubMedGoogle Scholar
  18. 18.
    Katchalski-Katzir E, Shariv I, Eisenstein M et al (1992) Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc Natl Acad Sci U S A 89:2195–2199. https://doi.org/10.1073/pnas.89.6.2195 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Hart TN, Read RJ (1992) A multiple-start Monte Carlo docking method. Proteins Struct Funct Genet 13:206–222. https://doi.org/10.1002/prot.340130304 CrossRefPubMedGoogle Scholar
  20. 20.
    Zacharias M (2005) ATTRACT: protein-protein docking in CAPRI using a reduced protein model. Proteins 60:252–256. https://doi.org/10.1002/prot.20566 CrossRefPubMedGoogle Scholar
  21. 21.
    Lyskov S, Gray JJ (2008) The RosettaDock server for local protein-protein docking. Nucleic Acids Res 36:W233–W238. https://doi.org/10.1093/nar/gkn216 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Zhang Z, Schindler CEM, Lange OF, Zacharias M (2015) Application of enhanced sampling Monte Carlo methods for high-resolution protein-protein docking in Rosetta. PLoS One 10:e0125941. https://doi.org/10.1371/journal.pone.0125941 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Dominguez C, Boelens R, Bonvin AMJJ (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737. https://doi.org/10.1021/ja026939x CrossRefPubMedGoogle Scholar
  24. 24.
    van Zundert GCP, Rodrigues JPGLM, Trellet M et al (2016) The HADDOCK2.2 Web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol 428:720–725. https://doi.org/10.1016/j.jmb.2015.09.014 CrossRefPubMedGoogle Scholar
  25. 25.
    Kynast P, Derreumaux P, Strodel B (2016) Evaluation of the coarse-grained OPEP force field for protein-protein docking. BMC Biophys 9:4. https://doi.org/10.1186/s13628-016-0029-y CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Böhm H-J (1998) Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des 12:309–309. https://doi.org/10.1023/A:1007999920146 CrossRefPubMedGoogle Scholar
  27. 27.
    Sasse A, de Vries SJ, Schindler CEM et al (2017) Rapid design of knowledge-based scoring potentials for enrichment of near-native geometries in protein-protein docking. PLoS One 12:e0170625. https://doi.org/10.1371/journal.pone.0170625 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43:W389–W394. https://doi.org/10.1093/nar/gkv332 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Finn RD, Coggill P, Eberhardt RY et al (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44:D279–D285. https://doi.org/10.1093/nar/gkv1344 CrossRefPubMedGoogle Scholar
  30. 30.
    The UniProt Consortium (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45:D158–D169. https://doi.org/10.1093/nar/gkw1099 CrossRefGoogle Scholar
  31. 31.
    Altschuh D, Lesk AM, Bloomer AC, Klug A (1987) Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. J Mol Biol 193:693–707. https://doi.org/10.1016/0022-2836(87)90352-4 CrossRefPubMedGoogle Scholar
  32. 32.
    Weigt M, White RA, Szurmant H et al (2009) Identification of direct residue contacts in protein-protein interaction by message passing. Proc Natl Acad Sci U S A 106:67–72. https://doi.org/10.1073/pnas.0805923106 CrossRefPubMedGoogle Scholar
  33. 33.
    Lunt B, Szurmant H, Procaccini A et al (2010) Inference of direct residue contacts in two-component signaling. Methods Enzymol 471:17–41. https://doi.org/10.1016/S0076-6879(10)71002-8 CrossRefPubMedGoogle Scholar
  34. 34.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x CrossRefGoogle Scholar
  35. 35.
    Marks DS, Colwell LJ, Sheridan R et al (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS One 6:e28766. https://doi.org/10.1371/journal.pone.0028766 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Marks DS, Hopf TA, Sander C (2012) Protein structure prediction from sequence variation. Nat Biotechnol 30:1072–1080. https://doi.org/10.1038/nbt.2419 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Hopf TA, Schärfe CPI, Rodrigues JPGLM et al (2014) Sequence co-evolution gives 3D contacts and structures of protein complexes. Elife. https://doi.org/10.7554/eLife.03430
  38. 38.
    Wagner A (2001) The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes. Mol Biol Evol 18:1283–1292. https://doi.org/10.1093/oxfordjournals.molbev.a003913 CrossRefPubMedGoogle Scholar
  39. 39.
    Wagner A (2003) How the global structure of protein interaction networks evolves. Proc R Soc B Biol Sci 270:457–466. https://doi.org/10.1098/rspb.2002.2269 CrossRefGoogle Scholar
  40. 40.
    Fokkens L, Hogeweg P, Snel B (2012) Gene duplications contribute to the overrepresentation of interactions between proteins of a similar age. BMC Evol Biol 12:99. https://doi.org/10.1186/1471-2148-12-99 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Brum JR, Ignacio-Espinoza JC, Kim E-H et al (2016) Illuminating structural proteins in viral “dark matter” with metaproteomics. Proc Natl Acad Sci U S A 113:2436–2441. https://doi.org/10.1073/pnas.1525139113 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Mukherjee S, Seshadri R, Varghese NJ et al (2017) 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nat Biotechnol. https://doi.org/10.1038/nbt.3886
  43. 43.
    Maddox J (1989) Towards the calculation of DNA. Nature 339:577. https://doi.org/10.1038/339577a0 CrossRefPubMedGoogle Scholar
  44. 44.
    Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci U S A 108:10184–10189. https://doi.org/10.1073/pnas.1103547108 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Zhao G, Perilla JR, Yufenyuy EL et al (2013) Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics. Nature 497:643–646. https://doi.org/10.1038/nature12162 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120:11919–11929. https://doi.org/10.1063/1.1755656 CrossRefPubMedGoogle Scholar
  47. 47.
    Friedrichs MS, Eastman P, Vaidyanathan V et al (2009) Accelerating molecular dynamic simulation on graphics processing units. J Comput Chem 30:864–872. https://doi.org/10.1002/jcc.21209 CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Plattner N, Doerr S, De Fabritiis G, Noé F (2017) Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat Chem 9:1005–1011. https://doi.org/10.1038/nchem.2785 CrossRefPubMedGoogle Scholar
  49. 49.
    Levy ED, Boeri Erba E, Robinson CV, Teichmann SA (2008) Assembly reflects evolution of protein complexes. Nature 453:1262–1265. https://doi.org/10.1038/nature06942 CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Marsh JA, Hernández H, Hall Z et al (2013) Protein complexes are under evolutionary selection to assemble via ordered pathways. Cell 153:461–470CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Hall Z, Hernández H, Marsh JA et al (2013) The role of salt bridges, charge density, and subunit flexibility in determining disassembly routes of protein complexes. Structure 21:1325–1337. https://doi.org/10.1016/j.str.2013.06.004 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Wells JN, Bergendahl LT, Marsh JA (2016) Operon gene order is optimized for ordered protein complex assembly. Cell Rep 14:679–685. https://doi.org/10.1016/j.celrep.2015.12.085 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    McShane E, Sin C, Zauber H et al (2016) Kinetic analysis of protein stability reveals age-dependent degradation. Cell 167:803–815.e21. https://doi.org/10.1016/j.cell.2016.09.015 CrossRefPubMedGoogle Scholar
  54. 54.
    Ahnert SE, Marsh JA, Hernández H et al (2015) Principles of assembly reveal a periodic table of protein complexes. Science 350:aaa2245. https://doi.org/10.1126/science.aaa2245 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jonathan N. Wells
    • 1
  • L. Therese Bergendahl
    • 1
  • Joseph A. Marsh
    • 1
  1. 1.MRC Human Genetics Unit, Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK

Personalised recommendations