IEEE/ACM Trans Comput Biol Bioinform. 2023 Jan-Feb;20(1):94-105. doi: 10.1109/TCBB.2021.3130923. Epub 2023 Feb 3.
Inferring the protein function(s) via the protein sub-sequence classification is often obstructed due to lack of knowledge about function(s) of sub-sequences in the protein sequence. In this regard, we develop a novel "multi-aspect" paradigm to perform the sub-sequence classification in an efficient way by utilizing the information of the parent sequence. The aspects are: (1) Multi-label: independent labelling of sub-sequences with more than one functions of the parent sequence, and (ii) Label-relevance: scoring the parent functions to highlight the relevance of performing a given function by the sub-sequence. The multi-aspect paradigm is used to propose the "Multi-Attention Based Multi-Aspect Network" for classifying the protein sub-sequences, where multi-attention is a novel approach to process sub-sequences at word-level. Next, the proposed Global-ProtEnc method is a sub-sequence based approach to encoding protein sequences for protein function prediction task, which is finally used to develop as ensemble methods, Global-ProtEnc-Plus. Evaluations of both the Global-ProtEnc and the Global-ProtEnc-Plus methods on the benchmark CAFA3 dataset delivered a outstanding performances. Compared to the state-of-the-art DeepGOPlus, the improvements in F with the Global-ProtEnc-Plus for the biological process is +6.50 percent and cellular component is +1.90 percent.
通过蛋白质子序列分类来推断蛋白质的功能通常会受到蛋白质序列中对子序列功能缺乏了解的阻碍。在这方面，我们开发了一种新的“多方面”范式，通过利用父序列的信息，以有效的方式进行子序列分类。这些方面包括：（1）多标签：对子序列进行独立的多标签标注，标注的标签来自父序列的多个功能；（2）标签相关性：对父序列的功能进行评分，突出子序列执行给定功能的相关性。多方面范式用于提出“基于多注意的多方面网络”，以对蛋白质子序列进行分类，其中多注意是一种处理子序列的新颖方法，可在单词级别进行处理。接下来，所提出的 Global-ProtEnc 方法是一种基于子序列的方法，用于对蛋白质序列进行编码，以进行蛋白质功能预测任务，最终用于开发成集成方法 Global-ProtEnc-Plus。在 CAFA3 基准数据集上对 Global-ProtEnc 和 Global-ProtEnc-Plus 方法的评估都取得了出色的性能。与最先进的 DeepGOPlus 相比，Global-ProtEnc-Plus 在生物学过程方面的 F 值提高了+6.50%，在细胞成分方面提高了+1.90%。
