EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants

• URL: http://arxiv.org/abs/2410.09667v1
• Date: Sat, 12 Oct 2024 23:22:49 GMT
• Title: EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
• Authors: Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess Smidt, Emine Kucukbenli, Joseph Jacobson,
• Abstract summary: We introduce EquiJump, a transferable SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly. Our approach achieves diverse sampling methods and is benchmarked against existing models on trajectory data of fast folding proteins.
• Score: 13.493198442811865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of protein motion, its computational toll hinders its use in practice. To address this challenge, multiple deep learning models for reproducing and accelerating MD have been proposed drawing on transport-based generative methods. However, existing work focuses on generation through transport of samples from prior distributions, that can often be distant from the data manifold. The recently proposed framework of stochastic interpolants, instead, enables transport between arbitrary distribution endpoints. Building upon this work, we introduce EquiJump, a transferable SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly. Our approach unifies diverse sampling methods and is benchmarked against existing models on trajectory data of fast folding proteins. EquiJump achieves state-of-the-art results on dynamics simulation with a transferable model on all of the fast folding proteins.

Related papers

• Structure Language Models for Protein Conformation Generation [66.42864253026053]
  Traditional physics-based simulation methods often struggle with sampling equilibrium conformations. Deep generative models have shown promise in generating protein conformations as a more efficient alternative. We introduce Structure Language Modeling as a novel framework for efficient protein conformation generation.
  arXiv  Detail & Related papers  (2024-10-24T03:38:51Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
  We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction. EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
  arXiv  Detail & Related papers  (2024-01-19T21:50:32Z)
• Equivariant Flow Matching with Hybrid Probability Transport [69.11915545210393]
  Diffusion Models (DMs) have demonstrated effectiveness in generating feature-rich geometries. DMs typically suffer from unstable probability dynamics with inefficient sampling speed. We introduce geometric flow matching, which enjoys the advantages of both equivariant modeling and stabilized probability dynamics.
  arXiv  Detail & Related papers  (2023-12-12T11:13:13Z)
• Rethinking materials simulations: Blending direct numerical simulations with neural operators [1.6874375111244329]
  We develop a new method that blends numerical solvers with neural operators to accelerate such simulations. We demonstrate the effectiveness of this framework on simulations of microstructure evolution during physical vapor deposition.
  arXiv  Detail & Related papers  (2023-12-08T23:44:54Z)
• Navigating protein landscapes with a machine-learned transferable coarse-grained model [29.252004942896875]
  coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. We develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins.
  arXiv  Detail & Related papers  (2023-10-27T17:10:23Z)
• Top-down machine learning of coarse-grained protein force-fields [2.1485350418225244]
  Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data. By applying Markov State Models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations.
  arXiv  Detail & Related papers  (2023-06-20T08:31:24Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Manifold Interpolating Optimal-Transport Flows for Trajectory Inference [64.94020639760026]
  We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) MIOFlow learns, continuous population dynamics from static snapshot samples taken at sporadic timepoints. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
  arXiv  Detail & Related papers  (2022-06-29T22:19:03Z)
• A Score-based Geometric Model for Molecular Dynamics Simulations [33.158796937777886]
  We propose a novel model called ScoreMD to estimate the gradient of the log density of molecular conformations. With multiple architectural improvements, we outperforms state-of-the-art baselines on MD17 and isomers of C7O2H10. This research provides new insights into the acceleration of new material and drug discovery.
  arXiv  Detail & Related papers  (2022-04-19T05:13:46Z)
• Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields [8.744017403796406]
  We develop a model for learning heterogeneous and dynamic patterns of velocity field data. We show the effectiveness of our techniques to the NGSIM dataset of complex multi-vehicle interactions.
  arXiv  Detail & Related papers  (2021-02-15T17:45:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.