小柯机器人

德国柏林工业大学Juri Rappsilber和Oliver Brock共同合作，近期取得重要工作进展。他们研究利用细胞内光交联质谱和深度学习预测蛋白质结构。相关研究成果2023年3月20日在线发表于《自然—生物技术》杂志上。

据介绍，虽然AlphaFold2可以从一级序列预测准确的蛋白质结构，但对于经历构象变化或同源序列很少的蛋白质来说，仍然存在挑战。

研究人员开发了AlphaLink工具，这是AlphaFold2算法的一个修改版本，它将实验性的距离约束信息纳入其网络架构。通过使用稀疏的实验接触作为锚点，AlphaLink提高了AlphaFold2在预测挑战性目标方面的性能。研究人员通过使用非经典氨基酸光亮氨酸通过交联质谱法获得细胞内残基-残基接触的信息，通过实验证实了这一点。该程序可以在提供的距离约束的基础上预测蛋白质的不同构象，证明了实验数据在驱动蛋白质结构预测中的价值。

总之，本文提出的用于在蛋白质结构预测中整合数据的耐噪声框架为从细胞内数据中准确表征蛋白质结构提供了新的方案。

附：英文原文

Title: Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning

Author: Stahl, Kolja, Graziadei, Andrea, Dau, Therese, Brock, Oliver, Rappsilber, Juri

Issue&Volume: 2023-03-20

Abstract: While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the AlphaFold2 algorithm that incorporates experimental distance restraint information into its network architecture. By employing sparse experimental contacts as anchor points, AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets. We confirm this experimentally by using the noncanonical amino acid photo-leucine to obtain information on residue–residue contacts inside cells by crosslinking mass spectrometry. The program can predict distinct conformations of proteins on the basis of the distance restraints provided, demonstrating the value of experimental data in driving protein structure prediction. The noise-tolerant framework for integrating data in protein structure prediction presented here opens a path to accurate characterization of protein structures from in-cell data.

DOI: 10.1038/s41587-023-01704-z

Source: https://www.nature.com/articles/s41587-023-01704-z

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex