功能几何引导的蛋白质序列与骨架结构协同设计
Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design
AI总结：
提出NAEPro模型，通过注意力与等变层交错网络，基于功能位点联合设计蛋白质序列与结构，在两个数据集上取得最优恢复率、TM-score和最低RMSD，并能生成结合目标金属辅因子的高效蛋白质。
摘要：
蛋白质是负责几乎所有生物体基本功能的大分子。设计具有所需功能的合理蛋白质至关重要。蛋白质的序列和结构高度相关，它们共同决定了其功能。在本文中，我们提出NAEPro，一个基于自动检测的功能位点来联合设计蛋白质序列和结构的模型。NAEPro由注意力层和等变层的交错网络驱动，能够捕获整个序列中的全局相关性以及三维（3D）空间中最近邻氨基酸的局部影响。这种架构促进了在两个层面上有效且经济的消息传递。我们在两个蛋白质数据集$β$-lactamase和myoglobin上评估了我们的模型及多个强基线。实验结果表明，我们的模型在所有竞争者中始终实现最高的氨基酸恢复率、TM-score和最低的RMSD。这些发现证明了我们的模型设计蛋白质序列和结构的能力，使其与天然对应物高度相似。此外，深入分析进一步证实了我们的模型生成高效蛋白质的能力，这些蛋白质能够结合其目标金属辅因子。我们在Github上提供代码、数据和模型。
英文摘要：
Proteins are macromolecules responsible for essential functions in almost all living organisms. Designing reasonable proteins with desired functions is crucial. A protein's sequence and structure are strongly correlated and they together determine its function. In this paper, we propose NAEPro, a model to jointly design Protein sequence and structure based on automatically detected functional sites. NAEPro is powered by an interleaving network of attention and equivariant layers, which can capture global correlation in a whole sequence and local influence from nearest amino acids in three dimensional (3D) space. Such an architecture facilitates effective yet economic message passing at two levels. We evaluate our model and several strong baselines on two protein datasets, $β$-lactamase and myoglobin. Experimental results show that our model consistently achieves the highest amino acid recovery rate, TM-score, and the lowest RMSD among all competitors. These findings prove the capability of our model to design protein sequences and structures that closely resemble their natural counterparts. Furthermore, in-depth analysis further confirms our model's ability to generate highly effective proteins capable of binding to their target metallocofactors. We provide code, data and models in Github.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
