鉴定和表征有助于蛋白质功能的结构位点对于理解生物学机制至关重要。评估疾病风险,并开发有针对性的治疗方法。然而,已知蛋白质结构的数量正在迅速超过我们对它们进行功能性注释的能力。现有的函数预测方法要么不在本地站点上运行,遭受高的假阳性或假阴性率,或需要大型特定站点的培训数据集,需要开发新的计算方法来大规模注释功能位点。我们提出了COLLAPSE(从对齐蛋白质结构环境中学习的压缩潜伏期),用于学习蛋白质位点的深度表示的框架。COLLAPSE直接作用于一个位点周围原子的3D位置,并使用同源蛋白之间的进化关系作为自我监督信号,使学习的嵌入能够隐式捕获每个站点内的结构-功能关系。我们的表示在迁移学习环境中概括了不同的任务,在标准化基准(蛋白质-蛋白质相互作用和突变稳定性)以及从Prosite数据库预测功能位点方面实现最先进的性能。我们使用COLLAPSE搜索大型蛋白质数据集的相似位点,并基于已知功能位点的数据库注释蛋白质。这些方法证明了COLLAPSE在计算上是有效的,可调,并且可以解释,为计算蛋白质分析提供通用平台。本文受版权保护。保留所有权利。
The identification and characterization of the structural sites which contribute to protein function are crucial for understanding biological mechanisms, evaluating disease risk, and developing targeted therapies. However, the quantity of known protein structures is rapidly outpacing our ability to functionally annotate them. Existing methods for function prediction either do not operate on local sites, suffer from high false positive or false negative rates, or require large site-specific training datasets, necessitating the development of new computational methods for annotating functional sites at scale. We present COLLAPSE (Compressed Latents Learned from Aligned Protein Structural Environments), a framework for learning deep representations of protein sites. COLLAPSE operates directly on the 3D positions of atoms surrounding a site and uses evolutionary relationships between homologous proteins as a self-supervision signal, enabling learned embeddings to implicitly capture structure-function relationships within each site. Our representations generalize across disparate tasks in a transfer learning context, achieving state-of-the-art performance on standardized benchmarks (protein-protein interactions and mutation stability) and on the prediction of functional sites from the Prosite database. We use COLLAPSE to search for similar sites across large protein datasets and to annotate proteins based on a database of known functional sites. These methods demonstrate that COLLAPSE is computationally efficient, tunable, and interpretable, providing a general-purpose platform for computational protein analysis.