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Phyre2.2:基于模板的蛋白质结构预测的社区资源。

Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.

作者信息

Powell Harold R, Islam Suhail A, David Alessia, Sternberg Michael J E

机构信息

Department of Life Sciences, Imperial College London, London SW7 2AZ UK.

Department of Life Sciences, Imperial College London, London SW7 2AZ UK.

出版信息

J Mol Biol. 2025 Jan 23:168960. doi: 10.1016/j.jmb.2025.168960.

Abstract

Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of a known structure as they will adopt a similar conformation, and the known structure can be used as the template for modelling the query sequence. Recently several approaches, most notably AlphaFold, have employed enhanced machine learning and have yielded accurate models irrespective of whether there is an identifiable template. Here we report Phyre2.2 which incorporates several enhancements to our widely-used template modelling portal Phyre2. The main development is facilitating a user to submit their sequence and then Phyre2.2 identifies the most suitable AlphaFold model to be used as a template. In Phyre2.2 the user searches a template library of known structures. We have now included in our library a representative structure for every protein sequence in the protein databank (PDB). In addition, there are representatives for an apo and a holo structure if they are in the PDB. The ranking of hits has been modified to highlight to the user if there are different domains spanning the sequence. Phyre2.2 continues to support batch processing where a user can submit up to 100 sequences facilitating processing of proteomes. Phyre2.2 is freely available to all users, including commercial users, at https://www.sbg.bio.ic.ac.uk/phyre2/.

摘要

基于模板的建模,也称为同源建模,是一种根据蛋白质氨基酸序列预测其结构的强大方法。该方法要求识别查询序列与已知结构序列之间的序列相似性,因为它们将采用相似的构象,并且已知结构可用于对查询序列进行建模。最近,几种方法,最著名的是AlphaFold,采用了增强的机器学习,并且无论是否存在可识别的模板,都能生成准确的模型。在此,我们报告了Phyre2.2,它对我们广泛使用的基于模板的建模平台Phyre2进行了多项改进。主要进展是方便用户提交他们的序列,然后Phyre2.2会识别出最适合用作模板的AlphaFold模型。在Phyre2.2中,用户搜索已知结构的模板库。我们现在已在库中为蛋白质数据库(PDB)中的每个蛋白质序列包含了一个代表性结构。此外,如果PDB中有apo和全酶结构的代表,也会包含在内。命中结果的排名已被修改,以向用户突出显示序列中是否存在不同的结构域。Phyre2.2继续支持批量处理,用户可以提交多达100个序列,便于对蛋白质组进行处理。所有用户,包括商业用户,均可在https://www.sbg.bio.ic.ac.uk/phyre2/免费使用Phyre2.2。

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