Biology subjects

Sasmal, S.

Publications and source records attributed to Sasmal, S..

4 recordsLinked to original sources

bioRxiv2025

Tutorial on quantifying and sampling biomolecular ensembles withShapeGMM

Here we present a detailed workflow for clustering and enhanced sampling of biomolecular conformations using the ShapeGMM methodology. This approach fits a probabilistic model of biomolecular conformations rooted in the idea that the free energy can expressed in terms of local fluctuations in atomic positions around metastable states. We demonstrate using a single model system how to generate and fit equilibrium molecular dynamics simulation data. We then show how to use the resulting model to generate a reaction coordinate between two states, how to sample along that coordinate using Metadynamics using our size-and-shape PLUMED module, and how to cluster those biased conformations to give a refined equilibrium ShapeGMM model.

biophysics↗

bioRxiv2025

Improved data-driven collective variables for biased sampling through iteration on biased data

Our ability to efficiently sample conformational transitions between two known states of a biomolecule using collective variable (CV) based sampling depends strongly on the choice of the CV. We previously reported a data-driven approach to clustering biomolecular configurations with a probabilistic clustering model termed ShapeGMM. ShapeGMM is a Gaussian Mixture Model in cartesian coordinates, with means and covariances in each cluster representing the harmonic approximation to the conformational ensemble around a metastable state. We subsequently showed that Linear Discriminant Analysis on positions (posLDA) is a good reaction coordinate to characterize the transition between two of these states, and moreover can be biased to produce transitions between the states using Metadynamics-like approaches. However, the quality of these LDA coordinates depends on the amount of data used to characterize the states, and here we demonstrate the ability to systematically improve them using enhanced sampling data. Specifically, we demonstrate that improved CVs for sampling can be generated by iteratively performing biased sampling along a posLDA coordinate and then generating a new shapeGMM model from biased data in the previous iteration. The new coordinates derived from our iterative approach show a substantial improvement in being able to induce transitions between metastable states, and to converge a free energy surface.

biophysics↗

bioRxiv2024

Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM

Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as ligand binding and allosteric regulation. Accurate quantification of these ensembles remains a challenge for all but the simplest molecules. One such challenge is insufficient sampling which enhanced sampling approaches, such as metadynamics, were designed to overcome; however, the non-uniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of non-uniform frame weights into a structural clustering method entitled shapeGMM. The shapeGMM method fits a Gaussian mixture model to particle positions, and here we advance that approach by incorporating nonuniform frame weights in the estimates of all parameters of the model. The resulting models are high dimensional probability densities for the unbiased systems from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a high dimensional probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the metadynamics free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, demonstrating the broad applicability of combining our prior and new methods, and illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantify biomolecular ensembles from biased simulations.

biophysics↗

bioRxiv2023

BrainProt(™) 3.0: Understanding Human Brain Diseases using comprehensively curated & Integrated OMICS datasets

BrainProt 3.0 is an integrative and simplified omics-based knowledge base of the human brain and its associated diseases. The current version of BrainProt includes six domains, which provide simplified, robust, and comprehensive data visualization to understand the human brain and its diseases/disorders based on proteomics, transcriptomics, public data curation, and integration strategies. Firstly, the HBDA (Human Brain Disease Atlas), index and navigator of BrainProt provides a resource table for 56 brain diseases. Secondly, Brain Disease Marker Curator (BDMC) and Brain Disease Drug Finder (BDDF) include a total of 20,202 diseases associated genes, more than 1,30,000 Chemical Target interactions, and around 2,145 Clinical Trial Information for more than 50 Brain Diseases. Thirdly, Brain Disease Transcriptome Map (BDTM) and Brain Disease Proteome Map (BDPM) integrate multi-omics data for 11 and 6 alarming brain diseases respectively. Currently, these two domains feature an expressional profile of 52 datasets, information of 1,868 samples, 3,657 DEPs, and 6,256 DEGs. Lastly, BrainProt also modifies and integrates the proteome and phosphoproteome data of the Inter-hemispheric Brain Proteome Map (IBPM). Overall, BrainProt is the first knowledgebase that connects different omics level information of brain diseases and provides a powerful scoring-based ranking platform to select and identify brain disease-associated markers, along with exploration of clinical trials, and drugs/chemical compounds to accelerate the identification of new disease markers and novel therapeutic strategies. The objectives of BrainProt are to support and follow the footsteps of the HBPP (Human Brain Proteome Project) by integrating different datasets to unravel the complexity of Human Brain and its associated diseases.

neuroscience↗