@article {163320, title = {Disobind: A sequence-based, partner-dependent contact map and interface residue predictor for intrinsically disordered regions.}, journal = {Cell Syst}, volume = {17}, year = {2026}, month = {2026 Jan 21}, pages = {101486}, abstract = {

Intrinsically disordered proteins or regions (IDPs or IDRs) adopt diverse binding modes with different partners, ranging from coupled folding and binding to fuzzy binding and fully disordered binding. Characterizing IDR interfaces is challenging both experimentally and computationally. State-of-the-art tools such as AlphaFold multimer and AlphaFold3 can be used to predict IDR binding sites, although they are less accurate at their benchmarked confidence cutoffs. Here, we developed Disobind, a deep-learning method that predicts inter-protein contact maps and interface residues for an IDR and its partner, given their sequences. It uses sequence embeddings from the ProtT5 protein language model. Disobind outperforms state-of-the-art interface predictors for IDRs. It also outperforms AlphaFold multimer and AlphaFold3 at multiple confidence cutoffs. Combining Disobind and AlphaFold-multimer predictions further improves performance. In contrast to current methods, Disobind considers the context of the binding partner and does not depend on structures and multiple sequence alignments. Its predictions can be used to localize IDRs in large assemblies and characterize IDR-mediated interactions.

}, keywords = {Amino Acid Sequence, Binding Sites, Computational Biology, Deep learning, Humans, Intrinsically disordered proteins, Protein Binding, Protein Folding}, issn = {2405-4720}, doi = {10.1016/j.cels.2025.101486}, url = {https://pubmed.ncbi.nlm.nih.gov/41534519/}, author = {Majila, Kartik and Ullanat, Varun and Viswanath, Shruthi} } @article {164794, title = {Integrative modeling with AlphaFold.}, journal = {Curr Opin Struct Biol}, volume = {98}, year = {2026}, month = {2026 Mar 10}, pages = {103239}, abstract = {

Macromolecular assemblies underpin essential cellular processes, yet their structural characterization remains challenging. Integrative modeling provides an approach for determining structures of macromolecular assemblies, combining diverse experimental data with physical principles, the statistics of previous structures, and prior models. There is a growing interest in leveraging the implicit structural knowledge learned by artificial intelligence-based structure-prediction methods such as AlphaFold (AF), for integrative modeling. Here, we discuss recent methods that combine AF with experimental data for integrative modeling in four ways: validating AF-based ensembles with experimental data; combining structural priors from AF with experimental data; fine-tuning AF with experimental data; and incorporating experimental data at inference time. We also outline key challenges in integrative structure determination using AF.

}, issn = {1879-033X}, doi = {10.1016/j.sbi.2026.103239}, url = {https://www.sciencedirect.com/science/article/pii/S0959440X26000217?via\%3Dihub}, author = {Majila, Kartik and Golatkar, Omkar and Viswanath, Shruthi} } @article {162693, title = {IHMValidation: Assessment of Integrative Structure Models Deposited to the Protein Data Bank.}, journal = {J Mol Biol}, year = {2025}, month = {2025 Dec 18}, pages = {169598}, abstract = {

PDB-IHM is a branch of the Protein Data Bank (PDB), a Worldwide Protein Data Bank (wwPDB) Core Archive, that expands its scope by allowing for additional biomolecular structure representations and types of experimental information (i.e., integrative/hybrid structure models). As of October 2025, PDB-IHM contained 374 entries, benefitting from multi-scale and multi-state representations and 17 types of experimental data. These structure models are assigned PDB accession codes and are archived alongside other experimental structures in the PDB. Rigorous interpretation of a structure model requires assessment of underlying data quality, consistency with the input data, and estimates of positional uncertainty of its components. Herein, we present the IHMValidation pipeline (https://validate.pdb-ihm.org; https://github.com/salilab/IHMValidation) based on recommendations from the wwPDB Integrative Methods Task Force plus the small-angle scattering (SAS), chemical crosslinking mass spectrometry (crosslinking-MS), and cryo-electron microscopy and tomography (3DEM) communities. The IHMValidation report (available in both PDF and HTML formats) comprises six sections: (i) overview; (ii) model details; (iii) data quality assessments; (iv) local geometry assessments (i.e., model quality); (v) fit of the model to the data used to generate it; and (vi) fit of the model to the data used for validation. Future expansions of the IHMValidation pipeline will: (i) reflect recommendations coming from additional experimental communities, including F{\"o}rster resonance energy transfer (FRET) and hydrogen/deuterium exchange MS (HDX-MS); (ii) include other validation criteria, such as Bayesian likelihoods for the data; and (iii) represent estimates of structure model uncertainty based on the variation among alternative models satisfying input data.

}, issn = {1089-8638}, doi = {10.1016/j.jmb.2025.169598}, author = {Zalevsky, Arthur O and Vallat, Brinda and Webb, Benjamin M and Tangmunarunkit, Hongsuda and Sekharan, Monica R and Shafaeibejestan, Aref and Ganesan, Sai and Sagendorf, Jared and Jeffries, Cy M and Trewhella, Jill and Graziadei, Andrea and Vizca{\'\i}no, Juan Antonio and Leitner, Alexander and Rappsilber, Juri and Peisach, Ezra and Flatt, Justin W and Young, Jasmine Y and Majila, Kartik and Viswanath, Shruthi and Kesselman, Carl and Hoch, Jeffrey C and Kurisu, Genji and Morris, Kyle L and Velankar, Sameer and Berman, Helen M and Burley, Stephen K and Sali, Andrej} } @article {152166, title = {Multiscale simulations reveal architecture of NOTCH protein and ligand specific features.}, journal = {Biophys J}, volume = {124}, year = {2025}, month = {2024 Dec 13}, pages = {393-407}, abstract = {

NOTCH, a single-pass transmembrane protein, plays a crucial role in cell fate determination through cell-to-cell communication. It interacts with two canonical ligands, Delta-like (DLL) and Jagged (JAG), located on neighboring cells to regulate diverse cellular processes. Despite extensive studies on the functional roles of NOTCH and its ligands in cellular growth, the structural details of full-length NOTCH and its ligands remain poorly understood. In this study, we employed fragment-based modeling and multiscale simulations to study the full-length structure of the human NOTCH ectodomain, comprising 1756 amino acids. We performed coarse-grained dynamics simulations of NOTCH in both glycosylated and nonglycosylated forms to investigate the role\ of\ glycosylation in modulating its conformational dynamics. In apo form, coarse-grained simulations revealed that glycosylated NOTCH protein can transition from an elongated structure of \~{}86\ nm from the membrane surface to a semicompact state (\~{}23.81 {\textpm} 9.98\ nm), which aligns with cryo-EM data. To transition from the apo form to ligand-bound forms of NOTCH, we followed an atomistic and integrative modeling approach to model the interactions between NOTCH-DLL4 and NOTCH-JAG1. Atomistic simulations of the smaller bound fragment EGF8-13 patch revealed conformational plasticity critical for NOTCH binding, while integrative modeling of full-length complexes suggested a larger binding surface than reported previously. Simulations of pathogenic mutations revealed that E360K and R448Q disrupted the NOTCH-ligand interaction surfaces, causing dissociation. In contrast, C1133Y in the Abruptex domain compromised protein stability by disrupting the domain{\textquoteright}s interaction with the ligand-binding domain in the apo form of NOTCH-ECD. These findings provide a detailed molecular understanding of NOTCH and its ligands, offering insights that could enable the development of novel therapeutic approaches to selectively target pathogenic NOTCH signaling.

}, issn = {1542-0086}, doi = {10.1016/j.bpj.2024.12.014}, author = {Rathore, Surabhi and Gahlot, Deepanshi and Castin, Jesu and Pandey, Arastu and Arvindekar, Shreyas and Viswanath, Shruthi and Thukral, Lipi} } @article {150947, title = {AlphaFold opens the doors to deorphanizing secreted proteins.}, journal = {Cell Syst}, volume = {15}, year = {2024}, month = {2024 Nov 20}, pages = {1000-1001}, abstract = {

Danneskiold-Sams{\o}e and coworkers have developed an in silico screening pipeline based on AlphaFold2 for identifying single-pass transmembrane receptors for secreted peptides that play important roles in cell-cell signaling. Their approach can be used to deorphanize a diverse range of ligands. The overall strategy can be valuable in screening for weak and transient interactions.

}, keywords = {Computer Simulation, Humans, Ligands, Proteins, Signal Transduction}, issn = {2405-4720}, doi = {10.1016/j.cels.2024.10.010}, author = {Viswanath, Shruthi} } @article {147301, title = {An integrated structural model of the DNA damage-responsive H3K4me3 binding WDR76:SPIN1 complex with the nucleosome.}, journal = {Proc Natl Acad Sci U S A}, volume = {121}, year = {2024}, month = {2024 Aug 13}, pages = {e2318601121}, abstract = {

Serial capture affinity purification (SCAP) is a powerful method to isolate a specific protein complex. When combined with cross-linking mass spectrometry and computational approaches, one can build an integrated structural model of the isolated complex. Here, we applied SCAP to dissect a subpopulation of WDR76 in complex with SPIN1, a histone reader that recognizes trimethylated histone H3 lysine4 (H3K4me3). In contrast to a previous SCAP analysis of the SPIN1:SPINDOC complex, histones and the H3K4me3 mark were enriched with the WDR76:SPIN1 complex. Next, interaction network analysis of copurifying proteins and microscopy analysis revealed a potential role of the WDR76:SPIN1 complex in the DNA damage response. Since we detected 149 pairs of cross-links between WDR76, SPIN1, and histones, we then built an integrated structural model of the complex where SPIN1 recognized the H3K4me3 epigenetic mark while interacting with WDR76. Finally, we used the powerful Bayesian Integrative Modeling approach as implemented in the Integrative Modeling Platform to build a model of WDR76 and SPIN1 bound to the nucleosome.

}, keywords = {ATPases Associated with Diverse Cellular Activities, Carrier Proteins, Cell Cycle Proteins, DNA Damage, DNA Helicases, Histones, Humans, Models, Molecular, Nucleosomes, Protein Binding}, issn = {1091-6490}, doi = {10.1073/pnas.2318601121}, author = {Liu, Xingyu and Zhang, Ying and Wen, Zhihui and Hao, Yan and Banks, Charles A S and Cesare, Joseph and Bhattacharya, Saikat and Arvindekar, Shreyas and Lange, Jeffrey J and Xie, Yixuan and Garcia, Benjamin A and Slaughter, Brian D and Unruh, Jay R and Viswanath, Shruthi and Florens, Laurence and Workman, Jerry L and Washburn, Michael P} } @article {150951, title = {The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling.}, journal = {Protein Sci}, volume = {33}, year = {2024}, month = {2024 Dec}, pages = {e5217}, abstract = {

Desmosomes mediate cell-cell adhesion and are prevalent in tissues under mechanical stress. However, their detailed structural characterization is not available. Here, we characterized the molecular architecture of the desmosomal outer dense plaque (ODP) using Bayesian integrative structural modeling via the Integrative Modeling Platform. Starting principally from the structural interpretation of a cryo-electron tomography (cryo-ET) map of the ODP, we integrated information from x-ray crystallography, an immuno-electron microscopy study, biochemical assays, in silico predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The integrative structure was validated by information from imaging, tomography, and biochemical studies that were not used in modeling. The ODP resembles a densely packed cylinder with a plakophilin (PKP) layer and a plakoglobin (PG) layer; the desmosomal cadherins and PKP span these two layers. Our integrative approach allowed us to localize disordered regions, such as the N-terminus of PKP and the C-terminus of PG. We refined previous protein-protein interactions between desmosomal proteins and provided possible structural hypotheses for defective cell-cell adhesion in several diseases by mapping disease-related mutations on the structure. Finally, we point to features of the structure that could confer resilience to mechanical stress. Our model provides a basis for generating experimentally verifiable hypotheses on the structure and function of desmosomal proteins in normal and disease states.

}, keywords = {Bayes Theorem, Cryoelectron Microscopy, Crystallography, X-Ray, Desmosomes, gamma Catenin, Humans, Models, Molecular, Plakophilins}, issn = {1469-896X}, doi = {10.1002/pro.5217}, author = {Pasani, Satwik and Menon, Kavya S and Viswanath, Shruthi} } @article {140662, title = {Optimizing representations for integrative structural modeling using Bayesian model selection.}, journal = {Bioinformatics}, volume = {40}, year = {2024}, month = {2024 Mar 04}, abstract = {

MOTIVATION: Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually.

RESULTS: Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies.

AVAILABILITY AND IMPLEMENTATION: NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor. Data for the benchmark is at https://www.doi.org/10.5281/zenodo.10360718.

}, keywords = {Bayes Theorem, Benchmarking, Macromolecular Substances}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btae106}, author = {Arvindekar, Shreyas and Pathak, Aditi S and Majila, Kartik and Viswanath, Shruthi} } @article {117966, title = {Identification of potential modulators of IFITM3 by in-silico modeling and virtual screening.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {2022 Sep 24}, pages = {15952}, abstract = {

IFITM3 is a transmembrane protein that confers innate immunity. It has been established to restrict entry of multiple viruses. Overexpression of IFITM3 has been shown to be associated with multiple cancers, implying IFITM3 to be good therapeutic target. The regulation of IFITM3 activity is mediated by multiple post-translational modifications (PTM). In this study, we have modelled the structure of IFITM3, consistent with experimental predictions on its membrane topology. MD simulation in membrane-aqueous environment revealed the stability of the model. Ligand binding sites on the IFITM3 surface were predicted and it was observed that the best site includes important residues involved in PTM and has good druggable score. Molecular docking was performed using FDA approved ligands and natural ligands from Super Natural II database. The ligands were re-ranked by calculating binding free energy. Select docking complexes were simulated again to substantiate the binding between ligand and IFITM3. We observed that known drugs like Eluxadoline and natural products like SN00224572 and Parishin A have good binding affinity against IFITM3. These ligands form persistent interactions with key lysine residues (Lys83, Lys104) and hence can potentially alter the activity of IFITM3. The results of this computational study can provide a starting point for experimental investigations on IFITM3 modulators.

}, keywords = {Biological Products, Ligands, Lysine, Molecular Docking Simulation, RNA-Binding Proteins}, issn = {2045-2322}, doi = {10.1038/s41598-022-20259-8}, author = {Tiwari, Vikas and Viswanath, Shruthi} } @article {116182, title = {Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination.}, journal = {Protein Sci}, volume = {31}, year = {2022}, month = {2022 Sep}, pages = {e4387}, abstract = {

The nucleosome remodeling and deacetylase (NuRD) complex is a chromatin-modifying assembly that regulates gene expression and DNA damage repair. Despite its importance, limited structural information describing the complete NuRD complex is available and a detailed understanding of its mechanism is therefore lacking. Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4, MTA1 -HDAC1-MBD3 , and MTA1-HDAC1-RBBP4-MBD3-GATAD2A [nucleosome deacetylase (NuDe)]. The integrative structures were corroborated by examining independent crosslinks, cryo-EM maps, biochemical assays, known cancer-associated mutations, and structure predictions from AlphaFold. The robustness of the models was assessed by jack-knifing. Localization of the full-length MBD3, which connects the deacetylase and chromatin remodeling modules in NuRD, has not previously been possible; our models indicate two different locations for MBD3, suggesting a mechanism by which MBD3 in the presence of GATAD2A asymmetrically bridges the two modules in NuRD. Further, our models uncovered three previously unrecognized subunit interfaces in NuDe: HDAC1 -MTA1 , MTA1 -MBD3 , and HDAC1 -MBD3 . Our approach also allowed us to localize regions of unknown structure, such as HDAC1 and MBD3 , thereby resulting in the most complete and robustly cross-validated structural characterization of these NuRD sub-complexes so far.

}, issn = {1469-896X}, doi = {10.1002/pro.4387}, author = {Arvindekar, Shreyas and Jackman, Matthew J and Low, Jason K K and Landsberg, Michael J and Mackay, Joel P and Viswanath, Shruthi} } @article {115255, title = {PrISM: Precision for Integrative Structural Models}, journal = {Bioinformatics}, year = {2022}, month = {06/2022}, abstract = {

Motivation: A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information.

Results: We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models.

}, doi = {10.1093/bioinformatics/btac400 }, author = {Ullanat, V and Kasukurthi, N and Viswanath, Shruthi} } @article {93056, title = {CM1-driven assembly and activation of yeast γ-tubulin small complex underlies microtubule nucleation}, journal = {Elife}, volume = {10}, year = {2021}, month = {05/2021}, abstract = {

Microtubule (MT) nucleation is regulated by the γ-tubulin ring complex (γTuRC), conserved from yeast to humans. In Saccharomyces cerevisiae, γTuRC is composed of seven identical γ-tubulin small complex (γTuSC) sub-assemblies, which associate helically to template MT growth. γTuRC assembly provides a key point of regulation for the MT cytoskeleton. Here, we combine crosslinking mass spectrometry, X-ray crystallography, and cryo-EM structures of both monomeric and dimeric γTuSCs, and open and closed helical γTuRC assemblies in complex with Spc110p to elucidate the mechanisms of γTuRC assembly. γTuRC assembly is substantially aided by the evolutionarily conserved CM1 motif in Spc110p spanning a pair of adjacent γTuSCs. By providing the highest resolution and most complete views of any γTuSC assembly, our structures allow phosphorylation sites to be mapped, surprisingly suggesting that they are mostly inhibitory. A comparison of our structures with the CM1 binding site in the human γTuRC structure at the interface between GCP2 and GCP6 allows for the interpretation of significant structural changes arising from CM1 helix binding to metazoan γTuRC.

}, author = {Brilot, AF and Lyon, SA and Zelter, Alex and Viswanath, Shruthi and Maxwell, A and MacCoss, MJ and Muller, EJ and Sali, Andrej and Davis, TN and Agard, DA} } @article {102045, title = {A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies}, journal = {Life}, volume = {11}, year = {2021}, month = {11/2021}, pages = {1183}, abstract = {

ntegrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid bodies, for sampling to be efficient and accurate. Currently, these parameters are tuned manually. To automate this process, we developed a general heuristic for derivative-free, global, stochastic, parallel, multiobjective optimization, termed StOP (Stochastic Optimization of Parameters) and applied it to optimize sampling-related parameters for the Integrative Modeling Platform (IMP). Given an integrative modeling setup, list of parameters to optimize, their domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision

}, doi = {10.3390/life11111183}, author = {Pasani, Satwik and Viswanath, Shruthi} } @article {85185, title = {Using Integrative Modeling Platform to Compute, Validate, and Archive a Model of a Protein Complex Structure.}, journal = {Protein Sci}, volume = {30}, year = {2021}, month = {2020 Nov 09}, pages = {250-261}, abstract = {

Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org). This article is protected by copyright. All rights reserved.

}, issn = {1469-896X}, doi = {10.1002/pro.3995}, author = {Saltzberg, Daniel J and Viswanath, Shruthi and Echeverria, Ignacia and Chemmama, Ilan E and Webb, Ben and Sali, Andrej} }