Abstract
Recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low- to medium-resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modeling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, this often restricts the modeling to a unique source of data, the EM density map.
In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modeling process. In the first step, rigid-body fitting is performed with PowerFit in order to identify the most likely locations of the molecules into the map. These are then used as centroids to which distance restraints are defined from the center of mass of the components of the complex for the initial rigid-body docking. The EM density is then directly used as an additional restraint energy term, which can be combined with all the other types of data supported by HADDOCK. This protocol relies on the new version 2.4 of both the HADDOCK webserver and software. Preparation steps consisting of cropping the EM map and rigid-body fitting of the atomic structure are explained. Then, the EM-driven docking protocol using HADDOCK is illustrated.
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References
Bai X, McMullan G, Scheres SH (2015) How cryo-EM is revolutionizing structural biology. Trends Biochem Sci 40:49–57
Kimanius D, Forsberg BO, Scheres SH et al (2016) Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. Elife 5. https://doi.org/10.7554/eLife.18722
Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera – a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612
Baker TS, Johnson JE (1996) Low resolution meets high: towards a resolution continuum from cells to atoms. Curr Opin Struct Biol 6:585–594
Esquivel-Rodríguez J, Kihara D (2013) Computational methods for constructing protein structure models from 3D electron microscopy maps. J Struct Biol 184:93–102
McGreevy R, Teo I, Singharoy A et al (2016) Advances in the molecular dynamics flexible fitting method for cryo-EM modeling. Methods 100:50–60
van Zundert GCP, Melquiond ASJ, Bonvin AMJJ (2015) Integrative modeling of biomolecular complexes: HADDOCKing with cryo-electron microscopy data. Structure 23:949–960
van Dijk AD, Boelens R, Bonvin AMJJ (2005) Data-driven docking for the study of biomolecular complexes. FEBS J 272:293–312
Melquiond ASJ, Bonvin AMJJ (2010) Data-driven docking: using external information to spark the biomolecular rendez-vous. In: Zacharias M (ed) Protein-protein complexes. Imperial College Press, London, pp 182–208
Karaca E, Bonvin AMJJ (2013) Advances in integrative modeling of biomolecular complexes. Methods 59:372–381
Rodrigues JPGLM, Bonvin AMJJ (2014) Integrative computational modelling of protein interactions. FEBS J 281:1988–2003
van Zundert GCP, Bonvin AMJJ (2015) Fast and sensitive rigid-body fitting into cryo-EM density maps with PowerFit. AIMS Biophys 2:73–87
van Zundert GCP, Trellet M, Schaarschmidt J et al (2017) The DisVis and PowerFit web servers: explorative and integrative modeling of biomolecular complexes. J Mol Biol 429:399–407
Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737
de Vries SJ, van Dijk AD, Krzeminski M et al (2007) HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins 69:726–733
de Vries SJ, Melquiond ASJ, Kastritis PL et al (2010) Strengths and weaknesses of data-driven docking in critical assessment of prediction of interactions. Proteins 78:3242–3249
Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935
Jorgensen WL, Tirado-Rives J (1988) The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110:1657–1666
Fernández-Recio J, Totrov M, Abagyan R (2004) Identification of protein–protein interaction sites from docking energy landscapes. J Mol Biol 335:843–865
Rodrigues JPGLM, Trellet M, Schmitz C et al (2012) Clustering biomolecular complexes by residue contacts similarity. Proteins 80:1810–1817
Goddard TD, Huang CC, Ferrin TE (2005) Chimera documentation – subregions. https://www.cgl.ucsf.edu/chimera/docs/UsersGuide/midas/mask.html
Brünger AT, Adams PD, Clore GM et al (1998) Crystallography & NMR system: a new software suite for macromolecular structure determination. Acta Crystallogr D Biol Crystallogr 54:905–921
Martin ACR, Porter C (2010) ProFit. http://www.bioinf.org.uk/software/profit/
Guo Q, Yuan Y, Xu Y et al (2011) Structural basis for the function of a small GTPase RsgA on the 30S ribosomal subunit maturation revealed by cryoelectron microscopy. Proc Natl Acad Sci U S A 108:13100–13105
Acknowledgments
This work is supported by the European H2020 e-Infrastructure grants (West-Life grant no. 675858 and BioExcel grants no. 675728 and 823830).
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Trellet, M., van Zundert, G., Bonvin, A.M.J.J. (2020). Protein–Protein Modeling Using Cryo-EM Restraints. In: Gáspári, Z. (eds) Structural Bioinformatics. Methods in Molecular Biology, vol 2112. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0270-6_11
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DOI: https://doi.org/10.1007/978-1-0716-0270-6_11
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