Integrative Modeling of Biomacromolecular Complexes

Essential biological processes are carried out by proteins and their complexes. Understanding the role that these complexes play in both health and disease requires knowledge of their three-dimensional structure. For challenging targets such as multi-subunit complexes or flexible complexes, the major technologies in structural biology – X-ray crystallography, NMR spectroscopy, and cryoelectron microscopy – struggle to obtain experimental data that unambiguously define atomic detail. Increasingly, structural models of protein complexes are obtained by integrative approaches which combine measured experimental data with computational modeling.

Components of a data-driven modeling workflow. Experimental data obtained with NMR spin-labels, FRET probes, or MS crosslinks are translated into spatial restraints to bias the conformational search. Coarse-grained modeling efficiently explores the vast search space before the model or parts of it are switched to all-atom mode for high-resolution refinement. Integrative models are written in IHM format and stored in PDB-Dev. Modeling components are developed in Rosetta as C++ libraries, and connected with each other and with other applications (e.g. from IMP) through their common Python interface to build complete protocols. 

Our research group uses Rosetta modeling and different kinds of experimental data (e.g. NMR, EPR, XL-MS) to develop models of biologically important proteins. In the past, these models helped to elucidate the basis for ligand specificity of Bradykinin G-protein-coupled receptors, identified interdomain contacts in the nuclear receptor LRH-1 important for allosteric communication, and demonstrated that transmembrane signaling in the histidine kinase NasS involves a metastable coiled-coil linker. These integrative models are disseminated to the scientific community through their deposition in the PDB-Dev database. In addition, our lab develops integrative modeling methods for the Rosetta software, such as tools for modeling spin-labels and chemical probes, which are used as reporter groups in several spectroscopic techniques (NMR, FRET, chemical crosslinking).

Selected Publications:

  • Leman JK, et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods. 2020. 17(7):665-680. doi: 10.1038/s41592-020-0848-2
  • Seacrist CD, Kuenze G, Hoffmann RM, Moeller BE, Burke JE, Meiler J, Blind RD. Integrated Structural Modeling of Full-Length LRH-1 Reveals Inter-domain Interactions Contribute to Receptor Structure and Function. Structure. 2020. 28(7):830-846. doi: 10.1016/j.str.2020.04.020
  • Kuenze G, Bonneau R, Leman JK, Meiler J. Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints. Structure. 2019. 27(11):1721-1734.e5. doi: 10.1016/j.str.2019.08.012
  • Kuenze G, Meiler J. Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13. Proteins. 2019. 87(12):1341-1350. doi: 10.1002/prot.25769
  • Joedicke L, Mao J, Kuenze G, Reinhart C, Kalavacherla T, Jonker HRA, Richter C, Schwalbe H, Meiler J, Preu J, Michel H, Glaubitz C. The molecular basis of subtype selectivity of human kinin G-protein-coupled receptors. Nat Chem Biol. 2018. 14(3):284-290. doi: 10.1038/nchembio.2551
  • Bhate MP, Lemmin T, Kuenze G, Mensa B, Ganguly S, Peters JM, Schmidt N, Pelton JG, Gross CA, Meiler J, DeGrado WF. Structure and Function of the Transmembrane Domain of NsaS, an Antibiotic Sensing Histidine Kinase in Staphylococcus aureus. J Am Chem Soc. 2018. 140(24):7471-7485. doi: 10.1021/jacs.7b09670

Collaborations:

Prof. Dr. Panagiotis Kastritis, Institute of Biochemistry and Biotechnology, Martin-Luther University Halle-Wittenberg