
@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 = {<p>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.</p>
},
	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 {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 = {<p>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</p>
},
	doi = {10.3390/life11111183},
	author = {Pasani, Satwik and Viswanath, Shruthi}
}
