Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations

• URL: http://arxiv.org/abs/2402.10433v2
• Date: Mon, 11 Mar 2024 20:20:16 GMT
• Title: Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
• Authors: Jiarui Lu, Zuobai Zhang, Bozitao Zhong, Chence Shi, Jian Tang
• Abstract summary: generative models have been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner.
• Score: 27.984190594059868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulation data (a "zero-shot" inference). However, being agnostic of the underlying energy landscape, the accuracy of such generative model may still be limited. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner. Specifically, given a target protein of interest, we first acquire some seeding conformations from the pre-trained sampler followed by a number of physical simulations in parallel starting from these seeding samples. Then we fine-tuned the generative model using the simulation trajectories above to become a target-specific sampler. Experimental results demonstrated the superior performance of such few-shot conformation sampler at a tractable computational cost.

Related papers

• Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loading [0.0]
  We present a surrogate-based multiscale approach to model constant strain-rate and creep experiments on unidirectional thermoplastic composites under off-axis loading. Results show better agreement with experiments than the single-scale micromechanical approach over a wide range settings.
  arXiv  Detail & Related papers  (2025-01-17T13:39:10Z)
• GauSim: Registering Elastic Objects into Digital World by Gaussian Simulator [55.02281855589641]
  GauSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics, modeling each kernel as a continuous piece of matter to account for realistic deformations without idealized assumptions. GauSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Parallel simulation for sampling under isoperimetry and score-based diffusion models [56.39904484784127]
  As data size grows, reducing the iteration cost becomes an important goal. Inspired by the success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Our work highlights the potential advantages of simulation methods in scientific computation for dynamics-based sampling and diffusion models.
  arXiv  Detail & Related papers  (2024-12-10T11:50:46Z)
• Generative Modeling of Molecular Dynamics Trajectories [12.255021091552441]
  We introduce generative modeling of molecular trajectories as a paradigm for learning flexible multi-task surrogate models of MD from data. We show such generative models can be adapted to diverse tasks such as forward simulation, transition path sampling, and trajectory upsampling.
  arXiv  Detail & Related papers  (2024-09-26T13:02:28Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators [29.22292758901411]
  We adapt the soft prompt-based learning method to molecular dynamics tasks. Our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples.
  arXiv  Detail & Related papers  (2023-08-29T08:29:08Z)
• Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling [23.74897713386661]
  The dynamic nature of proteins is crucial for determining their biological functions and properties. Existing learning-based approaches perform direct sampling yet heavily rely on target-specific simulation data for training. We propose Str2Str, a novel structure-to-structure translation framework capable of zero-shot conformation sampling.
  arXiv  Detail & Related papers  (2023-06-05T15:19:06Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks [0.0]
  In this work, we demonstrate a novel approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adrial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. To evaluate the quality of the proposed model, we integrate it into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator.
  arXiv  Detail & Related papers  (2020-12-08T17:57:37Z)

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.