Biology subjects

Viswanath, S.

Publications and source records attributed to Viswanath, S..

17 recordsLinked to original sources

bioRxiv2026

Evaluation of methods for AlphaFold-based integrative modeling

Motivation Recent methods enable incorporation of experimental data into AlphaFold for predicting structures consistent with the data. However, the applicability of these methods for integrative modeling remains to be determined. It is unclear how these methods balance the input experimental data with the learned structural priors. Results We assess the performance of state-of-the-art AlphaFold-based integrative modeling methods, including AlphaLink2, Boltz2, and GRASP, on a dataset of 37 complexes based on crosslinking data. We evaluate these methods based on their ability to predict structures that satisfy the input crosslinks. We further assess the robustness of these methods to noise in the crosslinking data. Finally, we probe their ability to predict distinct states using crosslinks from multiple states. Overall, our study highlights the limitations of the AF-based IM methods and points to directions for future improvements. Availability and implementation All scripts used to obtain the predictions and perform the analysis in this study are available at https://github.com/isblab/af_im.

bioinformatics↗

bioRxiv2026

GATA3-extension mutants rewire lineage identity in luminal breast cancer

GATA3, one of the most frequently mutated transcription factors in breast cancers, is a master regulator of luminal epithelial identity. Unlike many truncating and splice-site GATA3 mutations that disrupt one of the two zinc finger DNA-binding domains, C-terminal extension mutations (eGATA3) retain both of them. This raises the question of whether the eGATA3 mutants can alter transcriptional regulation and thereby exert distinct functional effects. Here, we integrate patient tumor-derived transcription and chromatin accessibility profiling with cell line derived genome-wide mapping of GATA3 occupancy and single-cell multiomics to define the regulatory consequences of eGATA3. We show that eGATA3 is associated with poor clinical outcome and remodels the luminal transcriptional program, leading to attenuation of estrogen-responsive transcription and progressive loss of luminal differentiation. Mechanistically, eGATA3 maintains widespread chromatin occupancy but redistributes GATA3 binding across different classes of regulatory elements, with preferential loss at AP-1 motif-enriched sites and gain at FOX-associated enhancers, along with coordinated remodeling of chromatin accessibility. These changes rewire the luminal regulatory landscape, destabilizing lineage identity without inducing complete lineage conversion. Together, our findings identify eGATA3 as a mechanistically distinct class of GATA3 mutation that promotes lineage plasticity through redistribution of genomic occupancy rather than loss of DNA-binding function alone.

cancer biology↗

bioRxiv2026

Integrative structure determination of a human mitochondrial contact site and cristae organizing system (MICOS) sub-assembly

The Mitochondrial contact site and Cristae Organizing System (MICOS) complex is an inner mitochondrial membrane (IMM) assembly present at the cristae junction. It is responsible for regulating cristae formation and remodeling. However, its structure is not known. We applied Bayesian integrative structure determination to characterize the structure of the Mic60, Mic19, Mic10, and Mic13-containing MICOS complex combining AlphaFold predictions with data from crosslinking mass spectrometry, biochemical assays, electron tomography, homology modeling, and sequence alignments. The integrative structure revealed novel mutual interfaces among Mic10N,C, Mic60LBS1,LBS2,mitofilin, and Mic13central,C, which were experimentally validated. Several likely-pathogenic missense mutations also localize to these novel interfaces, highlighting their importance. Our results indicate that Mic13 likely facilitates MICOS assembly by binding Mic10 in the IMM-proximal region and Mic60 in the intermembrane space. Taken together, our integrative approach sheds light on the structure and assembly of the MICOS complex.

bioinformatics↗

bioRxiv2026

Multicomplex Integrative Structural Modeling of a Human Histone Deacetylase Interactome

Histone Deacetylase (HDAC) 1 and 2 are key enzymatic components in multiple large chromatin remodeling complexes including NuRD, SIN3, and CoREST. In addition, both HDAC 1 and 2 contain a large intrinsically disordered region (IDR) within their C-terminal domain (CTD). How HDAC1/2 assemble into these complexes and the structure of the CTD IDR remains poorly understood. Here, we used HDAC1/2 to isolate their protein interaction networks from cells and used crosslinking mass spectrometry (XL-MS) coupled with the Integrative Modeling Platform to build structural models of the NuRD, SIN3A, and CoREST complexes. Next, we implemented an AlphaFold-enabled XL-MS constrained modeling approach to investigate how HDAC1 could assemble into these complexes. We show that the CTD IDR of HDAC1 folds into alpha helices in these complexes. Finally, we built a complete integrative structural model of a NuRD subcomplex including the abundant HDAC1:MBD3:MTA1:GATAD2B:RBBP4 subunits, which included 6 IDRs. The approaches used herein are broadly applicable for the study of protein complexes and protein interaction networks that can provide important insights into IDRs.

systems biology↗

bioRxiv2026

Cell-autonomous Wnt activity promotes transient re-programming and cell cycle re-entry of coronary artery endothelial cells

Coronary occlusion leads to formation of collateral arteries connecting occluded and perfused arteries. This process, namely artery reassembly, is temporally restricted to the cardiac regenerative window in mice and relies on cell cycle re-entry of post-mitotic artery endothelial cells (ECs). Analyses of single cell RNA sequencing data of ischemic mouse hearts led to identification of Wnt activity in proliferating artery cells. Using mouse genetics and whole organ imaging at cellular resolution, we show that Wntless (Wls)-mediated Wnt secretion and Frizzled (Fzd)4-dependent Wnt reception by artery cells, was essential for development of coronary collaterals. Specifically, Fzd4 promoted proliferation and expression of (progenitor-like) capillary/venous markers in coronary artery ECs. In embryonic hearts, coronary arteries originate from capillaries. Fzd4 in these capillaries was essential for proliferation; depletion of which, accelerated their differentiation into arteries by arterialization. Along these lines, an exogeneous dose of human WNT2-sFRP1 ligand complex reactivated artery cell proliferation and restored artery reassembly in non-regenerative mouse hearts. Thus, ischemia-induced cell-autonomous Wnt signaling promoted developmental reprogramming of coronary artery ECs. Finally, exome sequencing of coronary artery disease patients, revealed unique pathogenic variants of WLS and FZD4. Together, our study provides mechanistic insights into EC plasticity and underscores the significance of WNT pathway in cardiovascular disease.

developmental biology↗

bioRxiv2025

PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms

Cryo-electron tomography (cryo-ET) datasets are rich sources of information capable of describing the localizations, structures, and interactions of macromolecules. However, most current methods for localizing particles in cryo-electron tomograms are limited to macromolecules with known structure, require extensive manual annotations, and/or are computationally expensive. Here, we present PickET, a method for localizing macromolecules in tomograms that does not rely on expert annotations and prior structures. Its performance is demonstrated on a diverse dataset comprising over a hundred tomograms from publicly available datasets, varying in sample types, sample preparation conditions, microscope hardware, and image processing workflows. We demonstrate that PickET can simultaneously localize macromolecules of various shapes, sizes, and abundance. The predicted particle localizations can be used for 3D classification and de novo structural characterization. Our fully unsupervised approach is efficient and scalable, and enables high-throughput analysis of cryo-ET data.

bioinformatics↗

bioRxiv2024

A deep learning method for predicting interactions for intrinsically disordered regions of proteins

Intrinsically disordered proteins or regions (IDPs/IDRs) adopt diverse binding modes with different partners, from coupled-folding-and-binding, to fuzzy binding, to fully-disordered binding. Characterizing IDR interfaces is challenging experimentally and computationally. The state-of-the-art 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 the 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. A record of this papers transparent peer review process is included in the supplemental information.

bioinformatics↗

bioRxiv2024

A new discrete-geometry approach for integrative docking of proteins using chemical crosslinks

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 crosslinks 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 crosslinks. The method is an adaptation of EASAL (Efficient Atlasing and Search of Assembly Landscapes), a state-of-the-art discrete geometry method for efficient and exhaustive sampling of macromolecular configurations under pairwise inter-molecular distance constraints. We provide a mathematical proof that the method finds a structure satisfying the crosslink constraints under a natural condition satisfied by energy landscapes. We compare wall-EASAL with IMP (Integrative Modeling Platform), a commonly used integrative modeling method, on a benchmark, varying the numbers, types, and sources of input crosslinks, and sources of monomer structures. The wall-EASAL method performs better than IMP in terms of the average satisfaction of the configurations to the input crosslinks and the average similarity of the configurations to their corresponding native structures. The ensembles from IMP exhibit greater variability in these two measures. Further, wall-EASAL is more efficient than IMP. Although the current study uses crosslinks, 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 uncertainties in the crosslinking data beyond using distance bounds. 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. The wall-EASAL method is available at https://bitbucket.org/geoplexity/easal-dev/src/Crosslink and the benchmark is available at https://github.com/isblab/Integrative_docking_benchmark.

bioinformatics↗

bioRxiv2024

StrIDR: a database of intrinsically disordered regions of proteins with experimentally resolved structures

MotivationIntrinsically disordered regions (IDRs) of proteins exist as an ensemble of conformations, and not as a single structure. Existing databases contain extensive, experimentally derived annotations of intrinsic disorder for millions of proteins at the sequence level. However, only a tiny fraction of these IDRs are associated with an experimentally determined protein structure. Moreover, even if a structure exists, parts of the disordered regions may still be unresolved. ResultsHere we organize Structures of Intrinsically Disordered Regions (StrIDR), a database of IDRs confirmed via experimental or homology-based evidence, resolved in experimentally determined structures. The database can provide useful insights into the dynamics, folding, and interactions of IDRs. It can also facilitate computational studies on IDRs, such as those using molecular dynamics simulations and/or machine learning. AvailabilityStrIDR is available at https://isblab.ncbs.res.in/stridr. The web UI allows for downloading PDB structures and SIFTS mappings of individual entries. Additionally, the entire database can be downloaded in a JSON format. The source code for creating and updating the database is available at https://github.com/isblab/stridr.

bioinformatics↗

bioRxiv2023

Optimizing representations for integrative structural modeling using Bayesian model selection

MotivationIntegrative 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. ResultsHere, 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. AvailabilityNestOR 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. Supplementary Information is available online.

bioinformatics↗

bioRxiv2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

bioinformatics↗

bioRxiv2023

The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling

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 an electron cryo-tomogram, 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 PKP layer and a PG layer; the desmosomal cadherins and PKP span these two layers. Our integrative approach allowed us to localize disordered regions, such as N-PKP and PG-C. 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. Significance statementDesmosomes are cell-cell junctions that possess a hyper-adhesive property and are prevalent in tissues under mechanical stress. However, their detailed structural characterization has eluded experimental structural biologists so far. Here, we use an integrative approach that allows us to rigorously combine biochemical, biophysical, and cell biological data at multiple scales in order to determine the molecular architecture of the outer dense plaque region of desmosomes. We validate the structural model by several pieces of information not used to compute it. The model allows us to generate hypotheses on the desmosomal proteins in normal and disease states.

biophysics↗

bioRxiv2023

Mesenchymal stem cells ameliorate inflammation in an experimental model of Crohn's disease via the mesentery

ObjectiveMesenchymal stem cells (MSCs) are novel therapeutics for treatment of Crohns disease. However, their mechanism of action is unclear, especially in disease-relevant chronic models of inflammation. Thus, we used SAMP-1/YitFc, a chronic and spontaneous murine model of small intestinal inflammation, to study the therapeutic effect and mechanism of human bone marrow-derived MSCs (hMSC). DesignhMSC immunosuppressive potential was evaluated through in vitro mixed lymphocyte reaction, ELISA, macrophage co-culture, and RT-qPCR. Therapeutic efficacy and mechanism in SAMP were studied by stereomicroscopy, histopathology, MRI radiomics, flow cytometry, RT-qPCR, small animal imaging, and single-cell RNA sequencing (Sc-RNAseq). ResultshMSC dose-dependently inhibited naive T lymphocyte proliferation in MLR via PGE2 secretion and reprogrammed macrophages to an anti-inflammatory phenotype. hMSC promoted mucosal healing and immunologic response early after administration in SAMP model of chronic small intestinal inflammation when live hMSCs are present (until day 9) and resulted in complete response characterized by mucosal, histological, immunologic, and radiological healing by day 28 when no live hMSCs are present. hMSC mediate their effect via modulation of T cells and macrophages in the mesentery and mesenteric lymph nodes (mLN). Sc-RNAseq confirmed the anti-inflammatory phenotype of macrophages and identified macrophage efferocytosis of apoptotic hMSCs as a mechanism of action that explains their long-term efficacy. ConclusionhMSCs result in healing and tissue regeneration in a chronic model of small intestinal inflammation. Despite being short-lived, exert long-term effects via macrophage reprogramming to an anti-inflammatory phenotype. Data Transparency StatementSingle-cell RNA transcriptome datasets are deposited in an online open access repository Figshare (DOI: https://doi.org/10.6084/m9.figshare.21453936.v1)

immunology↗

bioRxiv2021

Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination

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 (MHR), MTA1N-HDAC1-MBD3GATAD2CC (MHM), and MTA1-HDAC1-RBBP4-MBD3-GATAD2A (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: HDAC1C-MTA1BAH, MTA1BAH-MBD3MBD, and HDAC160-100-MBD3MBD. Our approach also allowed us to localize regions of unknown structure, such as HDAC1C and MBD3IDR, thereby resulting in the most complete and robustly cross-validated structural characterization of these NuRD sub-complexes so far.

biophysics↗

bioRxiv2021

Annotating precision for integrative structural models using deep learning

MotivationA 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. ResultsWe develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models. AvailabilityPrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200. Contactshruthiv@ncbs.res.in Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

CM1-driven assembly and activation of Yeast γ-Tubulin Small Complex underlies microtubule nucleation

Microtubule (MT) nucleation is regulated by the {gamma}-tubulin ring complex ({gamma}TuRC), conserved from yeast to humans. In Saccharomyces cerevisiae, {gamma}TuRC is composed of seven identical {gamma}-tubulin small complex ({gamma}TuSC) sub-assemblies which associate helically to template microtubule growth. {gamma}TuRC assembly provides a key point of regulation for the MT cytoskeleton. Here we combine cross-linking mass spectrometry (XL-MS), X-ray crystallography and cryo-EM structures of both monomeric and dimeric {gamma}TuSCs, and open and closed helical {gamma}TuRC assemblies in complex with Spc110p to elucidate the mechanisms of {gamma}TuRC assembly. {gamma}TuRC assembly is substantially aided by the evolutionarily conserved CM1 motif in Spc110p spanning a pair of adjacent {gamma}TuSCs. By providing the highest resolution and most complete views of any {gamma}TuSC 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 {gamma}TuRC structure at the interface between GCP2 and GCP6 allows for the interpretation of significant structural changes arising from CM1 helix binding to metazoan {gamma}TuRC.

biochemistry↗

bioRxiv2018

Spc110 N-Terminal Domains Act Independently to Mediate Stable γ-Tubulin Small Complex Binding and γ-Tubulin Ring Complex Assembly

Microtubule (MT) nucleation in vivo is regulated by the {gamma}-tubulin ring complex ({gamma}TuRC), an approximately 2-megadalton complex conserved from yeast to humans. In Saccharomyces cerevisiae, {gamma}TuRC assembly is a key point of regulation over the MT cytoskeleton. Budding yeast {gamma}TuRC is composed of seven {gamma}-tubulin small complex ({gamma}TuSC) subassemblies which associate helically to form a template from which microtubules grow. This assembly process requires higher-order oligomers of the coiled-coil protein Spc110 to bind multiple {gamma}TuSCs, thereby stabilizing the otherwise low-affinity interface between {gamma}TuSCs. While Spc110 oligomerization is critical, its N-terminal domain (NTD) also plays a role that is poorly understood both functionally and structurally. In this work, we sought a mechanistic understanding of Spc110 NTD using a combination of structural and biochemical analyses. Through crosslinking-mass spectrometry (XL-MS), we determined that a segment of Spc110 coiled-coil is a major point of contact with {gamma}TuSC. We determined the structure of this coiled-coil segment by X-ray crystallography and used it in combination with our XL-MS dataset to generate an integrative structural model of the {gamma}TuSC-Spc110 complex. This structural model, in combination with biochemical analyses of Spc110 heterodimers lacking one NTD, suggests that the two NTDs within an Spc110 dimer act independently, one stabilizing association between Spc110 and {gamma}TuSC and the other stabilizing the interface between adjacent {gamma}TuSCs.

biochemistry↗