A new physics-informed representation using Fourier transforms as an inductive bias for the multiscale temporal nature of protein dynamics, DynaMode is presented, achieving strong performance across a set of ensemble-based metrics.
Abstract
Generative models present a promising alternative to expensive molecular dynamics for computationally querying protein dynamics, yet many existing approaches treat ensembles as unordered snapshots rather than temporally coherent trajectories, or scale poorly with protein size. We present a new physics-informed representation using Fourier transforms as an inductive bias for the multiscale temporal nature of protein dynamics. Diffusion in the spectral domain allows for disentangling of dynamics into slow conformational modes and fast atomic jitter, enabling rapid and improved prediction of dynamics across a range of temperatures. This is facilitated by denoising of structure and temperature conditioned spectral volumes where the low frequencies directly encode per-residue flexibility. Trained on the mdCATH dataset, we evaluate our model, DynaMode, on a held-out test set achieving strong performance across a set of ensemble-based metrics including a Root Mean Squared Fluctuation (RMSF) pearson $r$ of $0.844$. Code is available at https://github.com/HPuntu/DynaMode.
DyneTrion is presented, a generative protein dynamics emulator that jointly enforces geometric symmetry, structural consistency and temporal coherence within a single framework and provides a scalable path from static structure prediction toward time-resolved, ensemble-faithful protein modeling.
Kaihui Cheng, Zhiqiang Cai, Peng Tu et al.· 0 citations
P pHaseMD4AI is presented, a molecular dynamics dataset that combines a globally equilibrated peptide branch with a protein-scale constant-pH molecular dynamics (CpHMD) branch spanning hundreds of soluble proteins and provides a resource for developing and benchmarking molecular machine learning methods.
Tiefeng Song, Yi-Xin Guo, Jiahao He et al.· bioRxiv· 0 citations
UniFlow is introduced, the first scalable generative model that unifies protein ensemble generation and machine-learned coarse-grained force fields for molecular dynamics simulation within a single framework, and paves the way for a unified class of models that bridges generative ensemble modeling with physics-based mo...
Yikai Liu, Ming Chen, Guang Lin· bioRxiv· 0 citations
AquaGen is presented, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD) and demonstrates the utility of high-resolution ensemble generation for free energy e...
Emmanuel Bengio, Sanjeev Raja, Yui-Tik Pang et al.· arXiv.org· 1 citation
SPINDLE, an ensemble of deep neural networks trained on a large synthetic set of NMR relaxation data predicts both fast and slow timescale dynamics parameters from a single set of three relaxation datasets using the ensemble for error estimation, and demonstrates a strong correlation to ground truth dynamics parameters...
Olivia E. Krise, Michael P. Latham· bioRxiv· 0 citations
A quantitative scoring framework for comparing experimental and back-calculated observables is introduced and combined with regularized ensemble selection and Monte Carlo simulated annealing to provide direct inference of protein ensembles within a flexible ensemble-selection architecture incorporating multiple classes...
Murray Coles· bioRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
