Structural analysis of large polysaccharides has been a continuing challenge in glycobiology. The problem is especially acute when polysaccharides in question are glycosaminoglycans (GAGs). GAGs are large, linear, sulfated polysaccharides ubiquitous to all mammals. Interests in GAG structures stem from GAGs diverse biological activities that govern phenomena such as tissue development/regeneration, inflammation, blood coagulation and amyloid plaque formation. Abnormal GAG structures have also been associated with the development of a number of diseases, notably cancer and inflammation. As a result, there has been a desire to understand how GAG structures correlate with their biological activities, especially how the distribution of sulfate groups along the chain influence their interactions with GAG-binding proteins. However, GAGs large size and complex sulfation patterns make analysis of intact GAG chains by conventional bulk sample techniques difficult, if not impossible. Here we propose to carry out pre-requisite work needed to achieve single molecule sequencing of intact GAG chains by recognition tunneling (RT) nanopores. RT devices recognize unique chemical motifs in polymers by the distinctive tunneling signals these molecules produce as they translocate the nanopore and interact with nearby recognition molecules. Single molecule analysis of GAG chains circumvents the need to obtain homogeneous samples of GAG chains, greatly reducing complexity of sample preparation. GAG analysis by RT devices also does not have the size limitations of most of the existing analytical techniques, and the solid state device planned here are economical to manufacturer and operate. In this application, we aim to carry out two preliminary investigations needed to make GAG analysis by RT nanopores possible: (1) we will investigate the translocation of size defined sulfated GAG fragments through nanopores to optimize the translocation efficiency of GAG ligands as well as to understand the influence of GAG sulfation density and GAG size on their translocation efficiency and speed; (2) we will carry out recognition tunneling experiments on sulfated GAG disaccharides as well as trisaccharides so these signals of GAGs can be analyzed using machine learning algorithms to identify unique signatures needed to detect the presence of these sulfation motifs in longer GAG chains. Completion of these aims will provide all the knowledge required for correct interpretations of RT signals produced by GAG translocation and sets the stage for analysis of intact GAG chains by RT devices.
|Effective start/end date||7/15/15 → 6/30/18|
- HHS-NIH: National Institute of General Medical Sciences (NIGMS): $545,559.00
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