Biology subjects

Vats, S.

Publications and source records attributed to Vats, S..

4 recordsLinked to original sources

bioRxiv2026

A genomic and functional framework for the rapid domestication of the wild plant Chenopodium album

Global reliance on a small number of genetically uniform crops makes our food system increasingly vulnerable to pests, diseases, and climate change, highlighting the need to develop resilient local species as crops. Chenopodium album, a stress-tolerant, protein-rich wild plant whose seeds were part of prehistoric Northern European diets and whose leaves are still foraged worldwide, remains undomesticated despite its agrifood potential. We established a Danish collection of 143 accessions and combined seed metabolomics, ploidy assessment and genomics to uncover the molecular basis of key nutritional and anti-nutritional traits. Seed profiling revealed substantial variation in protein content (14-22%), comparable to or higher than major crops, and 16 distinct triterpenoid saponins, which are widespread bitter and anti-nutritional compounds. Seed production of field-grown lines reached up to 1.5 t/ha in trials conducted in Denmark, demonstrating promising yield potential. A high-quality tetraploid genome of a low-saponin line was assembled and contrasted with resequencing of a diploid high-saponin line in order to uncover the genetic basis of saponin variation in C. album. Comparative genomic, phylogenetic, and transcriptomic analyses identified structural variants and candidate genes associated with saponin biosynthesis, and functional validation confirmed the coordinated activity of a {beta}-amyrin synthase, three CYP716 cytochromes P450, and a glucuronosyltransferase that reconstitute the core C. album saponin pathway. Together, these results define the genomic and biochemical foundation of C. album, establishing a platform for its rapid domestication as a locally adapted, high-protein seed crop and a model for translating wild plant diversity into future food security.

plant biology↗

bioRxiv2025

CryoPhold: CryoEM meets AlphaFold and molecular simulation to reveal protein dynamics

Here we are introducing CryoPhold, a modular workflow that unifies AlphaFold-based ensemble generation, Bayesian reweighting against experimental cryo-EM maps, molecular simulation, and machine learning to quantify conformational populations and identify structural fingerprints that govern protein functions. Proteins are inherently dynamic, interconverting among conformational states that govern their function. Perturbations such as mutations, ligand binding, and pH changes modulate these dynamics and are implicated in many diseases. While cryogenic electron microscopy (cryo-EM) has transformed structure determination, it typically yields an averaged density map representing a static snapshot. A central challenge remains capturing the thermodynamics underlying protein motions and corresponding structural fingerprints that modulate function. CryoPhold enables Bayesian reweighting of AlphaFold-generated structural ensembles against experimental cryo-EM maps, generating posterior structural ensembles that are consistent with experimental data while preserving conformational heterogeneity. Molecular simulations seeded from the posterior ensemble capture time-dependent dynamics, while machine learning models trained on featurized molecular simulation data identify structural fingerprints ("hotspots") that modulate protein dynamics. Finally, Markov state models trained on featurized molecular simulation data quantify metastable state populations and free-energy landscapes. By integrating a generative AI-based protein structure prediction model, experimental cryo-EM density, physics-based sampling, and machine learning, CryoPhold enables dynamics paradigm to capture biomolecular motion. The workflow enables prediction of equilibrium populations and structural fingerprints governing conformational dynamics in human transporter protein, GlyT1. It further captures structural changes and population shifts associated with oncogenic BRAF mutants, key driving factors behind melanoma progression.

biophysics↗

bioRxiv2025

Generalizable Protein Dynamics in Serine-Threonine Kinases: Physics is the key

Kinases play crucial roles in signaling pathways across oncology, inflammation, and neurodegenerative diseases. Historically, their conformational states have been defined by the DFG motif: DFGin and DFGout. However, this binary paradigm overlooks the broader conformational heterogeneity of apo kinases, which encompasses multiple metastable states within an expanded DFG-Phe ensemble. We introduced a novel nomenclature that integrates the dynamics of the DFG-Phe, activation loop, and C-helix, highlighting how these regions respond to various ensemble perturbations (e.g., mutations, ligand binding, and protein-protein interactions). A major bottleneck in studying kinases lies in sampling their wide-ranging conformations because static snapshots often remain trapped in specific free energy minima, limiting traditional molecular dynamics simulations from exploring multiple functionally relevant states starting from a single structure. To accelerate conformational sampling, we introduce a computational framework that integrates AlphaFold, machine learning, physics- based simulations, and Markov state modeling. Rather than focusing on single-structure snapshots or structural hypotheses generated by protein structure prediction models, our framework captures shifts in conformational populations under varying perturbations, shedding light on both the thermodynamics and kinetics of the transitions. We show generalizability of our ensemble definitions and protocol across members of serine-threonine kinases and tyrosine kinases. A key innovation lies in our machine learning algorithms, which capture slowly varying structural features to uncover hidden states and generate latent representations of conformational motions across different kinase domains. The physics-refined structural ensemble sampled from the latent layers is then used to launch new simulations that more comprehensively explore the full conformational landscape than traditional molecular simulation approaches. By capturing how these conformational shifts influence downstream protein-protein interactions, conformational allostery, and cryptic pocket formation, our accelerated simulation framework provides deeper insights into generalized molecular recognition mechanism in kinases and how conformational heterogeneity is influenced by ensemble perturbations. This framework can be extended to investigate broader protein families--such as G protein-coupled receptors (GPCRs) and tumor necrosis factors (TNFs)--where functional outcomes are dictated by conformational heterogeneity.

biophysics↗

bioRxiv2023

AlphaFold-SFA: accelerated sampling of cryptic pocket opening by fusing AlphaFold, slow feature analysis and metadynamics

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by standard molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample deep cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture accelerated ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

biophysics↗