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.

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