美国华盛顿大学David Baker和哈佛大学Sergey Ovchinnikov小组合作在研究中取得进展。他们的研究利用深度学习构建蛋白质功能位点。相关论文于2022年7月22日发表在《科学》杂志上。

研究人员研发了用于揭示蛋白质功能位点的深度学习方法，并且无需预先设定蛋白质的折叠或二级结构。第一种方法“constrained hallucination”，优化序列，使其预测结构包含所需的功能位点。 第二种方法，“修复”，从功能位点开始，填充额外的序列和结构，利用经过专门训练的RoseTTAFold网络在单次前向传递中创建可行的蛋白质支架。研究人员使用这两种方法来设计候选免疫原、受体陷阱、金属蛋白、酶和蛋白质结合蛋白，并结合使用计算机和实验测试来验证设计。

据悉，蛋白质的结合和催化功能通常由整个蛋白质序列中少量功能残基决定的。

附：英文原文

Title: Scaffolding protein functional sites using deep learning

Author: Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Joseph L. Watson, Karla M. Castro, Robert Ragotte, Amijai Saragovi, Lukas F. Milles, Minkyung Baek, Ivan Anishchenko, Wei Yang, Derrick R. Hicks, Marc Expòsit, Thomas Schlichthaerle, Jung-Ho Chun, Justas Dauparas, Nathaniel Bennett, Basile I. M. Wicky, Andrew Muenks, Frank DiMaio, Bruno Correia, Sergey Ovchinnikov, David Baker

Issue&Volume: 2022-07-22

Abstract: The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second approach, “inpainting,” starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

DOI: abn2100

Source: https://www.science.org/doi/10.1126/science.abn2100