Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
<|reference_start|>Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations: 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.<|reference_end|>
@article{lu2024fusing,
    title={Fusing Neural and Physical: Augment Protein Conformation Sampling with
  Tractable Simulations},
    author={Jiarui Lu, Zuobai Zhang, Bozitao Zhong, Chence Shi, Jian Tang},
    journal={arXiv preprint arXiv:2402.10433},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10433},
    primaryClass={q-bio.BM cs.LG q-bio.QM}
}