Docking of Glycosaminoglycans to Proteins: Methods and Challenges

ABSTRACT

Glycoscience continues to emerge as a high-value information-rich field providing medical insight in the post-genomic era. Among the glycans, glycosaminoglycans (GAGs) represent a large family of highly sulfated, complex, linear, periodic polysaccharides that display a variety of important biological roles via interaction with protein targets. One of the recent examples is that heparan sulfate, itself a GAG, facilitates SARS-CoV-2 spike protein binding to the ACE2 receptor which triggers coronavirus infection. Not only this, but certain other kinds of GAGs have also been found to inhibit SARS-CoV-2 activity considerably and have been proposed as potential therapeutics. Computational modeling is an effective tool in studying biological systems but the nature of these long periodic linear and negatively charged polysaccharides makes it challenging to model GAG systems alone or their complexes with proteins. Docking is an essential tool for understanding protein-GAG interactions, but there has been a lack of validation studies to show the reliability of docking programs in predicting protein-GAG complexes. In this work, we will show some of the challenges and limitations of current software in modeling protein-GAG interactions by docking.