Publications

Viruses exploit host cell reliance on compartmentalization to facilitate their replication. Herpes simplex virus type 1 (HSV-1) modulates the subcellular localization of host proteins to suppress immune activation, license viral gene expression, and achieve translational shutoff. To spatially resolve dynamic protein-protein interaction (PPI) networks during infection with an immunostimulatory HSV-1 strain, we integrated nuclear/cytoplasmic fractionation with thermal proximity coaggregation analysis (N/C-TPCA). The resulting expanded depth and spatial resolution of PPIs charted compartment-specific assemblies of protein complexes throughout infection. We find that a broader suite of host chaperones than previously anticipated exhibits nuclear recruitment to form condensates known as virus-induced chaperone-enriched (VICE) domains. Monitoring protein and RNA constituents and ribosome activity, we establish that VICE domains sequester ribosome biogenesis factors from ribosomal RNA, accompanying a cell-wide defect in ribosome supply. These findings highlight infection-driven VICE domains as nodes of translational remodeling and demonstrate the utility of N/C-TPCA to study dynamic biological contexts.

A lack of tools for detecting receptor activity in vivo has limited our ability to fully explore receptor-level control of developmental patterning. Here, we extend phospho-tyrosine tag (pYtag) biosensors to visualize endogenous receptor tyrosine kinase (RTK) activity in Drosophila. We build biosensors for three RTKs that function across developmental stages and tissues. By characterizing Torso::pYtag during embryonic terminal patterning, we find that Torso activity differs from downstream extracellular signal-regulated kinase (ERK) activity in two surprising ways: Torso activity is narrowly restricted to the poles but produces a broader gradient of ERK and decreases over developmental time, while ERK activity is sustained, an effect mediated by ERK pathway-dependent negative feedback. Our results suggest that a narrow domain of Torso activity, tuned in amplitude by negative feedback, locally activates signaling effectors, which diffuse through the syncytial embryo to form the ERK gradient. Altogether, the results of this work highlight the usefulness of pYtags for investigating receptor-level regulation of developmental patterning.

Microtubules are essential cytoskeletal filaments involved in cell motility, division, and intracellular transport, exhibiting complex structural dynamics governed by diverse biophysical factors. Atomistic simulations of microtubule assemblies remain challenging due to their extensive spatiotemporal scales. To address this, we present a multiscale approach combining the primarily top-down Martini 3 coarse-grained (CG) model with an appropriately parameterized heterogeneous elastic network to capture microtubule mechanics and molecular detail efficiently. By iteratively tuning the elastic network, we matched the structural fluctuations of CG heterodimeric building blocks to atomistic reference data, reproducing experimentally consistent mechanical properties. This framework helped us identify stabilizing long-lived interactions between charged C-terminal tails and the folded domain of neighboring tubulin subunits, offering insight into sequence-specific contributions to lattice stability. Our efforts culminated in the construction of a 200 nm microtubule composed of million interaction centers, enabling exploration of large-scale microtubule-associated processes with amino acid-level resolution. This work bridges the gap between molecular specificity and computational scalability, offering a platform for simulating biophysical processes across cellular length and time scales.

Microtubules are essential cytoskeletal filaments involved in cell motility, division, and intracellular transport, exhibiting complex structural dynamics governed by diverse biophysical factors. Atomistic simulations of microtubule assemblies remain challenging due to their extensive spatiotemporal scales. To address this, we present a multiscale approach combining the primarily top-down Martini 3 coarse-grained (CG) model with an appropriately parameterized heterogeneous elastic network to capture microtubule mechanics and molecular detail efficiently. By iteratively tuning the elastic network, we matched the structural fluctuations of CG heterodimeric building blocks to atomistic reference data, reproducing experimentally consistent mechanical properties. This framework helped us identify stabilizing long-lived interactions between charged C-terminal tails and the folded domain of neighboring tubulin subunits, offering insight into sequence-specific contributions to lattice stability. Our efforts culminated in the construction of a 200 nm microtubule composed of million interaction centers, enabling exploration of large-scale microtubule-associated processes with amino acid-level resolution. This work bridges the gap between molecular specificity and computational scalability, offering a platform for simulating biophysical processes across cellular length and time scales.

Automating embryo viability prediction for in vitro fertilization (IVF) is important but challenging due to the limited availability of labeled pregnancy outcome data, as only a small fraction of embryos are labeled after transfer. Self-supervised learning (SSL) can leverage both labeled and unlabeled data to improve prediction. However, existing SSL methods for videos are not directly applicable to embryo development videos due to two challenges: (1) embryo time-lapse videos contain hundreds of frames, requiring significant GPU memory for conventional SSL; (2) the dataset contains videos with varying lengths and many outlier frames, causing traditional video alignment methods to struggle with semantic misalignment. We propose Spatial-Temporal Pre-Training (STPT) to address these challenges. STPT includes two stages: spatial and temporal. In each stage, only one encoder is trained while the other is frozen, reducing memory demands. To handle temporal misalignment, STPT avoids frame-by-frame alignment across videos. The spatial stage learns from alignments within each video and its temporally consistent augmentations. The temporal stage then models relationships between video embeddings. Our method efficiently handles long videos and temporal variability. On 23,027 time-lapse videos (3,286 labeled), STPT achieves the highest AUC of 0.635 (95% CI: 0.632-0.638) compared to baselines, with limited computational resources.

Proteins are dynamic molecules that depend on conformational flexibility to carry out functions in the cell, yet despite significant advances in the modeling of static protein structure, prediction of these dynamics remains challenging. We introduce RocketSHP, a machine learning model that predicts dynamic protein properties from sequence or static structure with unprecedented speed and accuracy. Trained on thousands of molecular dynamics trajectories spanning diverse protein families, RocketSHP simultaneously models multiple dynamics features: root-mean-square fluctuations (RMSF), generalized correlation coefficients (GCC-LMI), and a novel structural heterogeneity profile (SHP) based on recent structure quantization methods. RocketSHP significantly outperforms existing methods in predicting simulation-derived dynamics. We reduce RMSF prediction error by 57% compared to BioEmu and calibrated Dyna-1 predictions, including an up to 73% error reduction for long proteins. We validate these predictions with experimental hetNOE data, and we demonstrate the ability to adapt predictions to different physical temperatures. We highlight RocketSHP’s utility in constructing allosteric networks in the oncogene KRAS and identify structural sub-modules with correlated motions, and we validate RocketSHP by showing that changes in node centrality within predicted KRAS allosteric networks correlate with changes of folding free energy in experimental DMS data. Our approach makes predictions in seconds rather than hours or days, enabling us to perform the first comprehensive dynamics analysis of the entire human proteome. RocketSHP bridges the gap between static structural biology and dynamic functional understanding, enabling dynamics-aware structural analysis and variant effect prediction at scales previously unavailable. RocketSHP is available as free and open-source software at https://github.com/flatironinstitute/RocketSHP.

Proteins are dynamic molecules that depend on conformational flexibility to carry out functions in the cell, yet despite significant advances in the modeling of static protein structure, prediction of these dynamics remains challenging. We introduce RocketSHP, a machine learning model that predicts dynamic protein properties from sequence or static structure with unprecedented speed and accuracy. Trained on thousands of molecular dynamics trajectories spanning diverse protein families, RocketSHP simultaneously models multiple dynamics features: root-mean-square fluctuations (RMSF), generalized correlation coefficients (GCC-LMI), and a novel structural heterogeneity profile (SHP) based on recent structure quantization methods. RocketSHP significantly outperforms existing methods in predicting simulation-derived dynamics. We reduce RMSF prediction error by 57% compared to BioEmu and calibrated Dyna-1 predictions, including an up to 73% error reduction for long proteins. We validate these predictions with experimental hetNOE data, and we demonstrate the ability to adapt predictions to different physical temperatures. We highlight RocketSHP’s utility in constructing allosteric networks in the oncogene KRAS and identify structural sub-modules with correlated motions, and we validate RocketSHP by showing that changes in node centrality within predicted KRAS allosteric networks correlate with changes of folding free energy in experimental DMS data. Our approach makes predictions in seconds rather than hours or days, enabling us to perform the first comprehensive dynamics analysis of the entire human proteome. RocketSHP bridges the gap between static structural biology and dynamic functional understanding, enabling dynamics-aware structural analysis and variant effect prediction at scales previously unavailable. RocketSHP is available as free and open-source software at https://github.com/flatironinstitute/RocketSHP.

Yukawa systems have drawn widespread interest across various applications, including plasma physics, colloidal science, and astrophysics, due to their critical role in modeling electrostatic interactions. In this paper, we introduce a novel random batch sum-of-Gaussians (RBSOG) algorithm for molecular dynamics simulations of three-dimensional Yukawa systems with periodic boundary conditions. We develop a sum-of-Gaussians (SOG) decomposition of the Yukawa kernel, dividing the interactions into near-field and far-field components. The near-field component, singular but compactly supported in a local domain, is calculated directly. The far-field component, represented as a sum of smooth Gaussians, is treated using the random batch approximation in Fourier space with an adaptive importance sampling strategy to reduce the variance of force calculations. Unlike the traditional Ewald decomposition, which introduces discontinuities and significant truncation error at the cutoff, the SOG decomposition achieves high-order smoothness and accuracy near the cutoff, allowing for efficient and energy-stable simulations. Additionally, by avoiding the use of the fast Fourier transform, our method achieves optimal O(N) complexity while maintaining high parallel scalability. Finally, unlike previous random batch approaches, the proposed adaptive importance sampling strategy achieves nearly optimal variance reduction across the regime of the coupling parameters, which is essential for handling varying coupling strengths across weak and strong regimes of electrostatic interactions. Rigorous theoretical analyses are presented, including SOG decomposition construction, variance estimation, and simulation convergence. We validate the performance of RBSOG method through numerical simulations of one-component plasma under weak and strong coupling conditions, using up to 106 particles and 1024 CPU cores. As a practical application in fusion ignition, we simulate high-temperature, high-density deuterium-α mixtures to study the energy exchange between deuterium and high-energy α particles. Due to the flexibility of the Gaussian approximation, the RBSOG method can be readily extended to other dielectric response functions, offering a promising approach for large-scale simulations.

In this work we analyze the complex scaling method applied to the problem of time-harmonic scalar wave propagation in junctions between `leaky,' or open dielectric waveguides. In [arXiv:2302.04353, arXiv:2310.05816, arXiv:2401.04674, arXiv:2411.11204], it was shown that under suitable assumptions the problem can be reduced to a system of Fredholm second-kind integral equations on an infinite interface, transverse to the waveguides. Here, we show that the kernels appearing in the integral equation admit a rapidly decaying analytic continuation on certain natural totally real submanifolds of $\mathbb{C}^2.$ We then show that for suitable, physically-meaningful, boundary data the resulting solutions to the integral equations themselves admit analytic continuation and satisfy related asymptotic estimates. By deforming the integral equation to a suitable contour, the decay in the kernels, density, and data enable straightforward discretization and truncation, with an error that decays exponentially in the truncation length. We illustrate our results with several representative numerical examples.

Understanding protein conformation is key to understanding their function. Importantly, most proteins adopt multiple conformations with nontrivial ensemble distributions that change depending on their environment to perform functions like catalysis, signaling, and transport. Recently, machine learning techniques, especially deep generative models, have been employed to develop protein conformation generators. These models, known as unified protein ensemble samplers, are trained on the Protein Data Bank (PDB) dataset and can generate diverse protein conformation ensembles given a protein sequence. However, their reliance solely on structural data from the PDB, which primarily captures folded protein states, restricts the diversity of the generated ensembles and can result in physically unrealistic conformations. In this paper, we overcome these challenges by introducing ExEnDiff, an experiment-guided diffusion model for protein conformation generation. ExEnDiff integrates experimental measurements as a physical prior, enabling the generation of protein conformations with desired properties. Our experiments on a variety of fast-folding and intrinsically disordered proteins demonstrate that ExEnDiff significantly advances the capabilities of current unified protein ensemble samplers. With little computational cost, ExEnDiff can capture important proteins' configuration properties and the underlying Boltzmann distribution, paving the way for a next-generation molecular dynamics engine. We further demonstrate the effectiveness of ExEnDiff to capture conformational changes in the presence of mutations and as an efficient tool for determining a reasonable collective variable space for protein ensembles. With these results, ExEnDiff is well poised to push the study of protein ensembles into a data-rich regime currently available to few problems in biology.