Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.
Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches.
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单链DNA结合蛋白及其基于机器学习的鉴定方法。
DOI：
10.3390/biom12091187
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发表时间：
2022-08-26
期刊：
影响因子：
5.5
通讯作者：
中科院分区：
文献类型：
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作者：
单链DNA结合蛋白(SSB)在维持基因组稳定性方面起着至关重要的作用，它在DNA复制和基因转录等基本生物学过程中保护单链DNA的瞬时存在不受损害。端粒的单链区域还需要ssDNA结合蛋白的保护，以防它被错误地识别为异常。除了它们在基因组稳定性和完整性中的关键作用外，已经证明单链DNA和SSB-单链DNA相互作用在生命和病毒的所有三个领域的转录调控中都发挥着关键作用。在这篇综述中，我们介绍了我们对SSB的结构和功能的了解，以及SSB结合特异性的结构特征。然后，我们讨论了已经开发的基于机器学习的方法，用于从双链DNA(DsDNA)结合蛋白(DSB)中预测SSB。
Single-stranded DNA (ssDNA) binding proteins (SSBs) are critical in maintaining genome stability by protecting the transient existence of ssDNA from damage during essential biological processes, such as DNA replication and gene transcription. The single-stranded region of telomeres also requires protection by ssDNA binding proteins from being attacked in case it is wrongly recognized as an anomaly. In addition to their critical roles in genome stability and integrity, it has been demonstrated that ssDNA and SSB–ssDNA interactions play critical roles in transcriptional regulation in all three domains of life and viruses. In this review, we present our current knowledge of the structure and function of SSBs and the structural features for SSB binding specificity. We then discuss the machine learning-based approaches that have been developed for the prediction of SSBs from double-stranded DNA (dsDNA) binding proteins (DSBs).
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影响因子：
11.4
作者：
Crichlow, Gregg V.;Zhou, Hongwen;Hsiao, Hsin-hao;Frederick, Kendra B.;Debrosse, Maxime;Yang, Yuande;Folta-Stogniew, Ewa J.;Chung, Hye-Jung;Fan, Chengpeng;De La Cruz, Enrique M.;Levens, David;Lolis, Elias;Braddock, Demetrios
通讯作者：
Braddock, Demetrios
影响因子：
30.8
作者：
Burns, Michael B.;Temiz, Nuri A.;Harris, Reuben S.
通讯作者：
Harris, Reuben S.
影响因子：
3.5
作者：
Ali, Farman;Ahmed, Saeed;Akbar, Shahid
通讯作者：
Akbar, Shahid
DOI：
10.1038/nsb814
发表时间：
2002-07-01
期刊：
NATURE STRUCTURAL BIOLOGY
影响因子：
--
作者：
Desveaux, D;Allard, J;Sygusch, J
通讯作者：
Sygusch, J
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V