Biology subjects

Mardt, A.

Publications and source records attributed to Mardt, A..

3 recordsLinked to original sources

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↗

bioRxiv2022

Deep learning to decompose macromolecules into independent Markovian domains

The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a data-efficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning "Ising models" of large molecular complexes from simulation data.

biophysics↗