CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models.
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models.
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CATHe：使用蛋白质语言模型的嵌入检测 CATH 超家族的远程同源物。
DOI：
10.1093/bioinformatics/btad029
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发表时间：
2023-01-01
期刊：
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CATH是一个蛋白质结构域分类资源库，它利用结构与序列比较的自动化流程，结合专家人工审编，构建进化与结构关系的层级分类。本研究旨在开发算法，以检测当前基于隐马尔可夫模型（HMM）的先进方法所遗漏的远源同源物。所开发的方法（CATHe）将神经网络与从蛋白质语言模型获得的序列表征相结合。该方法使用一个远源同源物数据集进行评估，这些远源同源物与训练集中任何结构域的序列同一性均低于20%。
在1773个最大和50个最大的CATH超家族上训练的CATHe模型，准确率分别为85.6±0.4%和98.2±0.3%。为进一步测试CATHe检测CATH结构域衍生的HMM所遗漏的更多远源同源物的能力，我们使用了一个蛋白质结构域数据集，这些结构域在Pfam中有注释，但在CATH中没有。通过使用高度可靠的CATHe预测（预期错误率<0.5%），我们能够为462万个Pfam结构域提供CATH注释。对于来自智人的这些结构域的一个子集，我们通过将其相应的AlphaFold2结构与它们所归属的CATH超家族的结构进行比较，对90.86%的预测进行了结构验证。
所开发模型的代码可在https://github.com/vam-sin/CATHe获取，本研究中开发的数据集可在https://zenodo.org/record/6327572访问。
补充数据可在《生物信息学》在线版获取。
CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models. It was assessed using a dataset of remote homologues having less than 20% sequence identity to any domain in the training set. The CATHe models trained on 1773 largest and 50 largest CATH superfamilies had an accuracy of 85.6 ± 0.4% and 98.2 ± 0.3%, respectively. As a further test of the power of CATHe to detect more remote homologues missed by HMMs derived from CATH domains, we used a dataset consisting of protein domains that had annotations in Pfam, but not in CATH. By using highly reliable CATHe predictions (expected error rate <0.5%), we were able to provide CATH annotations for 4.62 million Pfam domains. For a subset of these domains from Homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold2 structures with structures from the CATH superfamilies to which they were assigned. The code for the developed models is available on https://github.com/vam-sin/CATHe, and the datasets developed in this study can be accessed on https://zenodo.org/record/6327572. Supplementary data are available at Bioinformatics online.
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DOI：
10.3389/fbinf.2022.1019597
发表时间：
2022
期刊：
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影响因子：
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