@article {153394, title = {Frontiers in integrative structural modeling of macromolecular assemblies}, journal = {QRB Discovery}, volume = {6}, year = {2025}, month = {22 January 2025}, pages = {e3}, type = {Research Paper}, abstract = {

Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based structure prediction and cryo electron-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with\ in situ\ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell{\textendash}cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and AlphaFold structure, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, these assemblies contained significant fractions of disordered regions, necessitating the development of new methods for modeling disordered regions in the context of ordered regions. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards\ in situ\ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight other opportunities for method development in the context of integrative modeling.

}, keywords = {Conformational ensembles, Electron cryo-tomography, Generative modeling, Integrative modeling, Intrinsically disordered proteins, Macromolecular assemblies, Protein language models}, doi = {https://doi.org/10.1017/qrd.2024.15}, url = {https://www.cambridge.org/core/journals/qrb-discovery/article/frontiers-in-integrative-structural-modeling-of-macromolecular-assemblies/BCBF1930939C6E8114D5DC718197C6D2}, author = {Kartik Majila and Shreyas Arvindekar and Muskaan Jindal and Shruthi Viswanath} } @article {157494, title = {A New Discrete-Geometry Approach for Integrative Docking of Proteins Using Chemical Cross-Links}, journal = {Journal of Chemical Information and Modeling}, volume = {65}, year = {2025}, month = {29 April 2025}, pages = {44576-4592}, chapter = {4576}, abstract = {

The structures of protein complexes allow us to understand and modulate the biological functions of the proteins. Integrative docking is a computational method to obtain the structures of a protein complex, given the atomic structures of the constituent proteins along with other experimental data on the complex, such as chemical cross-links or SAXS profiles. Here, we develop a new discrete geometry-based method, wall-EASAL, for integrative rigid docking of protein pairs given the structures of the constituent proteins and chemical cross-links. The method is an adaptation of efficient atlasing and search of assembly landscapes (EASAL), a state-of-the-art discrete geometry method for efficient and exhaustive sampling of macromolecular configurations under pairwise intermolecular distance constraints. We provide a mathematical proof that the method finds a structure satisfying the cross-link constraints under a natural condition satisfied by energy landscapes. We compare wall-EASAL with integrative modeling platform (IMP), a commonly used integrative modeling method, on a benchmark, varying the numbers, types, and sources of input cross-links, and sources of monomer structures. The wall-EASAL method performs similarly to IMP in terms of the average satisfaction of the configurations to the input cross-links and the average similarity of the configurations to their corresponding native structures. But wall-EASAL is more efficient than IMP and more robust against false positive cross-links in the context of binary integrative rigid docking. Although the current study uses cross-links, the method is general and any source of distance constraints can be used for integrative docking with wall-EASAL. However, the current implementation only supports binary rigid protein docking, i.e., assumes that the monomer structures are known and remain rigid. Additionally, the current implementation is deterministic, i.e., it does not account for some uncertainties in the cross-linking data, such as noise in the cross-link distances. Neither of these appears to be a theoretical or algorithmic limitation of the EASAL methodology. Structures from wall-EASAL can be incorporated in methods for modeling large macromolecular assemblies, for example by suggesting rigid bodies or restraints for use in these methods. This will facilitate the characterization of assemblies and cellular neighborhoods at increased efficiency, accuracy, and precision

}, doi = {https://doi.org/10.1021/acs.jcim.4c02412}, url = {https://pubs.acs.org/doi/full/10.1021/acs.jcim.4c02412}, author = {Yichi Zhang and Muskaan Jindal and Shruthi Viswanath and Meera Sitharam} }