EFG-CS:利用机器学习和深度学习模型从氨基酸序列预测化学位移与蛋白质结构预测。

IF 4.5 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Protein Science Pub Date : 2024-08-01 DOI:10.1002/pro.5096
Xiaotong Gu, Yoochan Myung, Carlos H M Rodrigues, David B Ascher
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引用次数: 0

摘要

核磁共振(NMR)晶体学是结构生物学中分析蛋白质立体化学和结构的主要方法之一。共振频率的化学位移反映了分子中质子在不同化学环境中产生不同核磁共振信号的效应。从核磁共振信号中理解化学位移具有挑战性,因为核磁共振结构并不一定能提供所有所需的化学位移信息,因此必须建立预测模型,才能从蛋白质结构或更理想的直接从氨基酸序列中准确推导出化学位移。在此,我们介绍专门从事化学位移预测的网络服务器 EFG-CS。EFG-CS 采用基于机器学习的转移预测模型,利用 ESMFold 预测的蛋白质结构进行骨干原子化学位移预测。此外,ESG-CS 还结合了基于图神经网络的模型,提供全面的侧链原子化学位移预测。我们的方法在骨架原子预测方面表现出了可靠的性能,达到了相当高的准确度水平,H 的均方根误差(RMSE)为 0.30 ppm,Hα 为 0.22 ppm,C 为 0.89 ppm,Cα 为 0.89 ppm,Cβ 为 0.84 ppm,N 为 1.69 ppm。此外,我们的方法还显示了侧链原子化学位移预测能力,仅利用氨基酸序列而不进行同源性或特征整理,Hβ的RMSE值为0.71 ppm,Hδ的RMSE值为0.74-1.15 ppm,Hγ的RMSE值为0.58-0.94 ppm。这项工作首次表明,生成式人工智能蛋白质模型可以预测几乎与实验模型相当的核磁共振位移。该网络服务器可在 https://biosig.lab.uq.edu.au/efg_cs 免费获取,化学位移预测结果可以表格格式下载,也可以三维格式可视化。
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EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

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来源期刊
Protein Science
Protein Science 生物-生化与分子生物学
CiteScore
12.40
自引率
1.20%
发文量
246
审稿时长
1 months
期刊介绍: Protein Science, the flagship journal of The Protein Society, is a publication that focuses on advancing fundamental knowledge in the field of protein molecules. The journal welcomes original reports and review articles that contribute to our understanding of protein function, structure, folding, design, and evolution. Additionally, Protein Science encourages papers that explore the applications of protein science in various areas such as therapeutics, protein-based biomaterials, bionanotechnology, synthetic biology, and bioelectronics. The journal accepts manuscript submissions in any suitable format for review, with the requirement of converting the manuscript to journal-style format only upon acceptance for publication. Protein Science is indexed and abstracted in numerous databases, including the Agricultural & Environmental Science Database (ProQuest), Biological Science Database (ProQuest), CAS: Chemical Abstracts Service (ACS), Embase (Elsevier), Health & Medical Collection (ProQuest), Health Research Premium Collection (ProQuest), Materials Science & Engineering Database (ProQuest), MEDLINE/PubMed (NLM), Natural Science Collection (ProQuest), and SciTech Premium Collection (ProQuest).
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