Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要