@article {164003, title = {BPS2026 {\textendash} PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms}, journal = {Biophysical Journal}, volume = {125}, year = {2026}, month = {19 February 2026}, chapter = {121a}, author = {Shreyas M. Arvindekar and Omkar Golatkar and Shruthi Viswanath} } @inbook {163314, title = {Recent Methods from Statistical Inference for Integrative Structural Modeling}, booktitle = {Springer Handbook of Chem- and Bioinformatics }, year = {2026}, pages = {1075{\textendash}1103}, publisher = {Springer, Cham}, organization = {Springer, Cham}, abstract = {
Integrative modeling of macromolecular assemblies allows for structural characterization of large assemblies that are recalcitrant to direct experimental observation. A Bayesian inference approach facilitates combining data from complementary experiments along with physical principles, statistics of known structures, and prior models, for structure determination. Here, we review recent methods for integrative modeling based on statistical inference and machine learning. These methods improve over the current state-of-the-art by improving data collection, optimizing coarse-grained model representations, making scoring functions more accurate, sampling more efficient, and model analysis more rigorous. We also discuss three new frontiers in integrative modeling: incorporating recent deep learning-based methods, integrative modeling with in situ data, and metamodeling.
}, keywords = {AlphaFold, Crosslinking coupled with mass spectrometry, Deep learning, Dynamical Integrative Structure Modeling, Electron cryo-microscopy, Electron cryo-tomography, Integrative structure modeling, Whole-cell modeling}, isbn = {Print ISBN 978-3-031-81727-4}, issn = {Online ISBN 978-3-031-81728-1}, doi = {https://doi.org/10.1007/978-3-031-81728-1_47}, url = {https://link.springer.com/chapter/10.1007/978-3-031-81728-1_47}, author = {Shreyas Arvindekar and Kartik Majila and Shruthi Viswanath} } @article {154351, title = {BPS2025 - Recent developments in integrative structural modeling of macromolecular assemblies}, journal = {Biophysical Journal}, volume = {124}, year = {2025}, month = {13 February 2025}, chapter = {323A}, abstract = {

Integrative structure determination allows us to determine the structures of large macromolecular assemblies by combining data from complementary experimental methods with physical principles, statistical inference, and prior models. This talk will focus on efforts towards improving integrative modeling by my group in the last few years. Specifically, I will describe methods to optimize coarse-grained representations, annotate precision for integrative models, and obtain binding sites for intrinsically disordered regions. First, we developed methods to optimize the coarse-graining of subunits for an integrative modeling system, based on the amount of input information. The optimization is based on Bayesian model selection and we demonstrate that the optimized representations enable efficient sampling and result in models that fit the input data well. Second, we developed an efficient method to annotate regions of high- and low-precision in an integrative model ensemble. Low-precision regions can suggest where the next set of experimental data would be most impactful and high-precision regions can be used for identifying binding interfaces and suggesting new mutations. Finally, we developed a method to identify binding sites for intrinsically disordered regions (IDRs) of proteins in assemblies. Often, IDRs are challenging to localize in macromolecular assemblies, since they are associated with sparse structural as well as other interaction data. I will discuss a recent deep-learning method that we developed to predict binding regions for IDRs in large assemblies; the method outperforms AlphaFold3 on this task on benchmarks. Finally, I will also discuss emerging frontiers in the field: methods for utilizing data from cryo-electron tomography and methods that build upon AI-based structure predictions. In all, these methods will result in an improved spatio-temporal characterization of macormolecular structures in cells.

}, doi = {10.1016/j.bpj.2024.11.1782}, url = {https://www.cell.com/biophysj/fulltext/S0006-3495(24)02510-4}, author = {Shruthi Viswanath} } @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} }