Advances in Tools to Determine the Glycan-Binding Specificities of Lectins and Antibodies

Abstract

Proteins that bind carbohydrate structures can serve as tools to quantify or localize specific glycans in biological specimens. Such proteins, including lectins and glycan-binding antibodies, are particularly valuable if accurate information is available about the glycans that a protein binds. Glycan arrays have been transformational for uncovering rich information about the nuances and complexities of glycan-binding specificity. A challenge, however, has been the analysis of the data. Because protein-glycan interactions are so complex, simplistic modes of analyzing the data and describing glycan-binding specificities have proven inadequate in many cases. This review surveys the methods for handling high-content data on protein-glycan interactions. We contrast the approaches that have been demonstrated and provide an overview of the resources that are available. We also give an outlook on the promising experimental technologies for generating new insights into protein-glycan interactions, as well as a perspective on the limitations that currently face the field.

Computational prediction method to decipher receptor–glycoligand interactions in plant immunity

“…However, structural conformations of glycans are not easy to determine due to their intrinsic mobility and the scarce capability of some structural techniques, like X‐ray diffraction, to solve their conformations (Fadda and Woods, 2010; Gimeno et al ., 2020). Moreover, protein–glycan interactions are weak, with affinities ranging from the µ m to the m m range, due to the formation of transient structural states that result in more dynamic interactions than protein–peptide ones (Otto et al ., 2011; Sapay et al ., 2013; Isaacson and Díaz‐Moreno, 2019; Mende et al ., 2019; Gimeno et al ., 2020; Haab and Klamer, 2020). In silico approaches, particularly molecular dynamics, can aid to solve complex protein–glycan interactions, but simulation of carbohydrate‐based structures can also be challenging since the initial protein–glycan conformation and the force field employed during the simulation might be critical steps to obtain reliable results.…”

Exploring the carbohydrate‐binding ability of Canavalia bonariensis lectin in inflammation models

“…The tool uses atomic coordinates of a 3D structure of a protein complexed to a carbohydrate assuming that this model is minimal determinant binding (MDB) of the protein and then the glycan array analyses de presence of the same glycan motif in the carbohydrate library to indicate predicted binders from this list. If there is any steric impediment between the branches of the glycan and the side chains of the amino acid residues, it is assumed that the binding is not favorable 16 …”

Glycowork: A Python package for glycan data science and machine learning

“…Moderately large datasets from sources such as glycomics (Cummings and Pierce 2014), glycan arrays (Oyelaran and Gildersleeve 2009), or lectin arrays (Ribeiro and Mahal 2013) can by now be gathered on a more or less routine basis, depending on the application. Many algorithms for the analysis of these glycan-related data, such as subtree mining for the analysis of glycan array data (Coff et al 2020;Haab and Klamer 2020) or glycan-focused machine learning (Bojar et al 2021;Burkholz, Quackenbush and Bojar 2021), have been recently developed. Yet while both factors that are necessary for effective analysis -data and algorithms -are present in glycobiology, most efforts of algorithm development in the field are inaccessible to the typical end user, who may not be well-versed in computational workflows.…”