Ho Alex D, Wu Shang-Chuen, Kamili Nourine A, Blenda Anna V, Cummings Richard D, Stowell Sean R, Arthur Connie M
Joint Program in Transfusion Medicine, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC, United States.
Front Mol Biosci. 2022 Aug 11;9:893185. doi: 10.3389/fmolb.2022.893185. eCollection 2022.
Numerous studies have highlighted the utility of glycan microarray analysis for the elucidation of protein-glycan interactions. However, most current glycan microarray studies analyze glycan binding protein (GBP)-glycan interactions at a single protein concentration. While this approach provides useful information related to a GBP's overall binding capabilities, extrapolation of true glycan binding preferences using this method fails to account for printing variations or other factors that may confound relative binding. To overcome this limitation, we examined glycan array binding of three galectins over a range of concentrations to allow for a more complete assessment of binding preferences. This approach produced a richer data set than single concentration analysis and provided more accurate identification of true glycan binding preferences. However, while this approach can be highly informative, currently available data analysis approaches make it impractical to perform binding isotherms for each glycan present on currently available platforms following GBP evaluation. To overcome this limitation, we developed a method to directly optimize the efficiency of assessing association constants following multi-GBP concentration glycan array analysis. To this end, we developed programs that automatically analyze raw array data (kdMining) to generate output graphics (kaPlotting) following array analysis at multiple doses. These automatic programing methods reduced processing time from 32.8 h to 1.67 min. Taken together, these results demonstrate an effective approach to glycan array analysis that provides improved detail and efficiency when compared to previous methods.
众多研究突出了聚糖微阵列分析在阐明蛋白质 - 聚糖相互作用方面的效用。然而，目前大多数聚糖微阵列研究在单一蛋白质浓度下分析聚糖结合蛋白（GBP）与聚糖的相互作用。虽然这种方法提供了与GBP整体结合能力相关的有用信息，但使用此方法推断真正的聚糖结合偏好时，未能考虑到打印变化或其他可能混淆相对结合的因素。为克服这一限制，我们在一系列浓度范围内检测了三种半乳糖凝集素与聚糖阵列的结合情况，以便更全面地评估结合偏好。与单一浓度分析相比，这种方法产生了更丰富的数据集，并能更准确地识别真正的聚糖结合偏好。然而，尽管这种方法可能极具信息价值，但按照目前可用的数据分析方法，在评估GBP后，要对当前可用平台上存在的每种聚糖进行结合等温线分析并不实际。为克服这一限制，我们开发了一种方法，可在多GBP浓度聚糖阵列分析后直接优化评估缔合常数的效率。为此，我们开发了程序，在多剂量阵列分析后自动分析原始阵列数据（kdMining）以生成输出图形（kaPlotting）。这些自动编程方法将处理时间从32.8小时缩短至1.67分钟。综上所述，这些结果证明了一种有效的聚糖阵列分析方法，与以前的方法相比，它能提供更详细的信息和更高的效率。
