CarboGrove: a resource of glycan-binding specificities through analyzed glycan-array datasets from all platforms

Abstract

Glycan arrays continue to be the primary resource for determining the glycan-binding specificity of proteins. The volume and diversity of glycan-array data are increasing, but no common method and resource exists to analyze, integrate, and use the available data. To meet this need, we developed a resource of analyzed glycan-array data called CarboGrove. Using the ability to process and interpret data from any type of glycan array, we populated the database with the results from 35 types of glycan arrays, 13 glycan families, 5 experimental methods, and 19 laboratories or companies. In meta-analyses of glycan-binding proteins, we observed glycan-binding specificities that were not uncovered from single sources. In addition, we confirmed the ability to efficiently optimize selections of glycan-binding proteins to be used in experiments for discriminating between closely related motifs. Through descriptive reports and a programmatically accessible API, CarboGrove yields unprecedented access to the wealth of glycan-array data being produced and powerful capabilities for both experimentalists and bioinformaticians.

Boltzmann Model Predicts Glycan Structures from Lectin Binding

“…In general, terminal motifs were the most easily identified, presumably because they are the least susceptible to steric interference. This is in broad agreement with most known lectin binding rules. ,,, …”

“…This is in broad agreement with most known lectin binding rules. 24,26,42,43 We observed, however, that there are also terminal motifs that were less well-identified, and finding GBPs that recognize these features could improve the performance of our model in future studies. The following is a list of motifs that we believe may benefit from the identification of new partner GBPs:…”

“…Robust Motif Binding is Rare Among Lectins. The binding motifs of many lectins have been studied and cataloged, [24][25][26][27]43 and experiments have been performed to evaluate the relative affinities of lectins for some glycoproteins of interest. 53,54 However, little has been published to quantify the specificity of lectin binding, and such studies have only been done on a predetermined set of known glycan motifs.…”

“…Recent studies have utilized machine learning (ML) techniques to extract the binding specificities of a plethora of commonly used lectins, − demonstrating the utility of ML for lectin analytics. ML has also been used for other tasks in glycobiology, such as glycan classification and glycosylation site prediction. − We refer the reader to refs – for more comprehensive reviews of the present and future of ML and big data in glycobiology.…”

Mass Spectrometry-Based Glycoproteomic Workflows for Cancer Biomarker Discovery

“…84 The range of specificity/selectivity of these proteins can vary significantly and can be used to cross-correlate glycan changes. 27,42,48,[85][86][87] Lectins can be used individually or in combination to obtain more confidence about glycan structures and have been historically utilized in combination with tools such as HPLC which provide some information about glycan structure but not the precision of precise mass or hydrogen/carbon interactions which can be obtained from techniques like mass spectrometry or NMR respectively. A disadvantage of lectin-based technologies is that often the full specificity of a lectin (or lack thereof) is not perfectly understood and multiple glycoforms will be bound and purified/imaged.…”