论文标题
基于能量的原子分辨率蛋白质构象模型
Energy-based models for atomic-resolution protein conformations
论文作者
论文摘要
我们提出了一种基于能量的模型（EBM），以原子量表运行。该模型仅对结晶的蛋白质数据进行训练。相比之下，现有的评分构象方法使用能量功能，这些函数纳入了几十年的研究和调整的复杂产物的物理原理和特征知识。为了评估该模型，我们基于Rotamer恢复任务进行基准测试，这是从蛋白质结构中从其上下文中预测侧链构象的问题，该蛋白质结构已用于评估蛋白质设计的能量功能。该模型可实现接近Rosetta Energy函数的性能，Rosetta Energy函数是一种最新的方法，该方法广泛用于蛋白质结构预测和设计。对模型的输出和隐藏表示形式的研究发现，它捕获了与蛋白质能量相关的物理化学特性。
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex product of several decades of research and tuning. To evaluate the model, we benchmark on the rotamer recovery task, the problem of predicting the conformation of a side chain from its context within a protein structure, which has been used to evaluate energy functions for protein design. The model achieves performance close to that of the Rosetta energy function, a state-of-the-art method widely used in protein structure prediction and design. An investigation of the model's outputs and hidden representations finds that it captures physicochemical properties relevant to protein energy.
