Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.
Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.
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DOI：
10.1007/978-1-0716-2317-6_15
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发表时间：
2022-01-01
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影响因子：
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通讯作者：
Kc, Dukka B
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作者：
Pakhrin, Subash C;Pokharel, Suresh;Kc, Dukka B
翻译后修饰(PTM)是真核生物和原核生物中普遍存在的一种现象，它产生了巨大的蛋白质组多样性。PTM主要有两种形式：多肽链的共价修饰和蛋白水解性切割。对PTM的理解和表征是理解生物学基础的基本步骤。实验方法的最新进展，主要是基于质谱学的方法，极大地帮助了获得和表征PTMS。然而，实验方法不足以理解和表征450多种不同类型的PTM，互补的计算方法正变得流行起来。近年来，随着深度学习在各个领域的应用日益广泛，基于深度学习的计算预测领域也出现了大量基于深度学习的方法。在本书的这一章中，我们首先回顾了最近在PTM站点预测领域中的一些基于DL的方法。此外，我们还综述了尚未被研究的PTM，即蛋白水解性切割预测的最新进展。我们通过强调深度学习体系结构、特征编码、方法的新颖性和工具/方法的可用性来描述PTM预测的进展。最后，对基于动态链接库的PTM预测方法进行了展望和未来可能的研究方向。
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.