Contrastive learning on protein embeddings enlightens midnight zone.
Contrastive learning on protein embeddings enlightens midnight zone.
复制标题
DOI：
10.1093/nargab/lqac043
复制
发表时间：
2022-06
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
通过多序列比对，或更一般地通过基于同源性的推断（HBI），利用实验结构，促进信息从具有已知注释的蛋白质转移到没有任何注释的查询。最近的替代方案将HBI的概念从序列距离查找扩展到基于嵌入的注释转移（EAT）。这些嵌入来自蛋白质语言模型（pLM）。在这里，我们介绍使用单个蛋白质表示从pLM的对比学习。该学习过程创建了一组新的嵌入，该嵌入优化了由CATH资源定义的蛋白质3D结构的分层分类所捕获的约束。这种被称为ProtTucker的方法比传统的技术（如线程或折叠识别）具有更好的识别远距离同源关系的能力。因此，这些嵌入允许序列比较进入蛋白质相似性的“午夜区”，即其中远距离相关序列具有看似随机的成对序列相似性的区域。这项工作的新奇在于工具和采样技术的特定组合，其确定了与现有的最先进的序列比较方法相当或更好的良好性能。此外，由于该方法不需要生成对齐，因此其速度也快了几个数量级。该代码可在https://github.com/Rostlab/EAT上获得。
Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the ‘midnight zone’ of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.
影响因子：
14.9
作者：
Przybylski D;Rost B
通讯作者：
Rost B