Abstract
Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev (https://pdb-dev.wwpdb.org) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.
Highlights
Interactions among molecules lead to the emergence of biological phenomena—most in the forms of macromolecular machines and dynamic liaisons that transmit information and control behaviors
Integrative modeling is similar to protein structure determination by nuclear magnetic resonance (NMR) spectroscopic methods in which spatial restraints implied by the NMR data, such as nuclear overhauser effects (NOE) and J-coupling constants, must be satisfied
To maximize the impact of integrative structures, the coordinates should be made publicly available, at least upon publication, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and 3DEM maps
Summary
Interactions among molecules lead to the emergence of biological phenomena—most in the forms of macromolecular machines and dynamic liaisons that transmit information and control behaviors. Much of the input information about the modeled system is encoded into data-based restraints comprising a scoring function ((ii) above) used to evaluate candidate models produced by structural sampling ((iii) above). In this regard, integrative modeling is similar to protein structure determination by nuclear magnetic resonance (NMR) spectroscopic methods in which spatial restraints implied by the NMR data, such as nuclear overhauser effects (NOE) and J-coupling constants, must be satisfied. The integrative approach can be extended from modeling a single static structure to computing models of multiple structural states in a heterogeneous sample (e.g., the two states in the functional cycle of PhoQ kinase (Molnar et al 2014)), spatiotemporal models of dynamic processes (e.g., macromolecular transport through the NPC (Raveh et al 2016; Timney et al 2016)), and models of molecular networks (e.g., metabolic pathway for gulonate synthesis (Calhoun et al 2018))
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