利用深度学习研究蛋白质家族关系。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-09-18 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae132
Irina Ponamareva, Antonina Andreeva, Maxwell L Bileschi, Lucy Colwell, Alex Bateman
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引用次数: 0

摘要

动机在本文中,我们提出了一种基于预训练神经网络 ProtENN2 的 Pfam 族间相似性发现方法。我们使用 ProtENN2 每残基嵌入模型生成新的高维每族嵌入,并开发了一种基于这些嵌入计算族间相似性得分的方法,并使用结构比较对其预测结果进行了评估:我们将我们的方法应用到 Pfam 注释中,通过完善 Pfam 家族的家族成员资格,为现有家族推荐新成员,并为未来发布的 Pfam 推荐潜在的新家族。我们研究了我们方法的一些失败模式,为今后的改进提出了方向。我们的方法相对简单,参数很少,可以应用于其他蛋白质族分类模型。总之,我们的工作表明,利用深度学习提高我们对蛋白质家族关系和以前未表征家族功能的理解具有潜在的益处。可用性和实现:github.com/iponamareva/ProtCNNSim, 10.5281/zenodo.10091909。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of protein family relationships with deep learning.

Motivation: In this article, we propose a method for finding similarities between Pfam families based on the pre-trained neural network ProtENN2. We use the model ProtENN2 per-residue embeddings to produce new high-dimensional per-family embeddings and develop an approach for calculating inter-family similarity scores based on these embeddings, and evaluate its predictions using structure comparison.

Results: We apply our method to Pfam annotation by refining clan membership for Pfam families, suggesting both new members of existing clans and potential new clans for future Pfam releases. We investigate some of the failure modes of our approach, which suggests directions for future improvements. Our method is relatively simple with few parameters and could be applied to other protein family classification models. Overall, our work suggests potential benefits of employing deep learning for improving our understanding of protein family relationships and functions of previously uncharacterized families.

Availability and implementation: github.com/iponamareva/ProtCNNSim, 10.5281/zenodo.10091909.

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