A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities.
A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities.
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DOI：
10.1021/acschembio.1c00689
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发表时间：
2022-11-18
影响因子：
4
通讯作者：
Mahal, Lara K.
中科院分区：
文献类型：
--
作者：
Bojar, Daniel;Meche, Lawrence;Meng, Guanmin;Eng, William;Smith, David F.;Cummings, Richard D.;Mahal, Lara K.
聚糖对生物学和医学的各个方面都至关重要，从病毒感染到胚胎发育。研究聚糖的工具正在迅速发展;然而，我们的大部分知识都深深依赖于聚糖结合蛋白的结合（例如，凝集素）。凝集素通常是天然分离的蛋白质，其特异性尚未得到很好的定义，使得难以充分利用其用于聚糖分析的潜力。在这里，我们使用机器学习算法和专家注释的组合来定义这个重要探针集的凝集素特异性。我们的分析使用了我们使用功能性糖组学协会聚糖微阵列（CFGv 5）5.0版获得的市售凝集素的全面聚糖微阵列分析。该数据集于2011年公布。我们报告了这个数据集的创建及其在凝集素-聚糖结合行为的大规模评估中的应用。我们的基序分析是通过整合68个手动定义的聚糖功能与系统探测的计算规则的重要结合基序使用单糖和二糖和链接。结合机器学习和手动注释，我们创建了57种独特凝集素的聚糖结合特异性的详细解释，按其主要结合基序分类：甘露糖、复合型N-聚糖、O-聚糖、岩藻糖、唾液酸和硫酸盐、GlcNAc和几丁质、Gal和LacNAc以及GalNAc。我们的工作提供了新的见解复杂的结合功能，目前使用的市售凝集素，这些重要的试剂提供了一个重要的指导。
Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin–glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.
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影响因子：
2.9
作者：
ETZLER, ME;KABAT, EA
通讯作者：
KABAT, EA
影响因子：
30.3
作者：
Bojar, Daniel;Powers, Rani K.;Collins, James J.
通讯作者：
Collins, James J.
影响因子：
2.9
作者：
BAUSCH, JN;PORETZ, RD
通讯作者：
PORETZ, RD
DOI：
10.1016/0006-291x(79)91597-3
发表时间：
1979-01-01
影响因子：
3.1
作者：
BREWER, CF
通讯作者：
BREWER, CF
DOI：
10.1016/0304-4165(94)00210-o
发表时间：
1995-05-11
影响因子：
3
作者：
ALLEN, AK
通讯作者：
ALLEN, AK