The goal of this project is to characterize sialic acid (SIA) binding in proteins by looking at the electrochemical environment as well as the stereochemical conformation bias inherent in the SIA carbohydrate. Chemical space probing will be done through PDB sorting based on alpha or beta face SIA orientation followed by subsequent structural alignments based on configuration (h donor, acceptor, ring, etc.) and then electrochemical environment. Additionally, by computationally modeling the position of SIA for each structure and then merging their electrostatic maps, an average electrochemical ligand pocket can be produced which will subsequently be the main tool for high throughput screening of potential broadly neutralizing small molecules and antibody CDRs. This mesh will also be used to constrain the possible amino acid sequence of the CDR as well as provide a canvas for de novo modeling of a CDR. We hypothesize that we will find a clear bias in amino acid sequence in the CDR structure corresponding to the acetamido group of SIA as it is the only planar region of the molecule, as well as significant bias towards aromatic amino acids at the hydroxyl substituted positions of SIA. Computational screening and model building will be done using EON and ROCS followed by experimental testing of the predicted trends by ELISA and biolayer interferometry. This is a novel hybrid computational and structural approach that will be used to focus on the electrostatic map to provide a rationale for cross-reactivity and amino acid sequence bias in the CDR.
EON and ROCS results show high levels of homology between hemaggluttinin and neuraminidase SIA binding pockets
Primary: James E. Crowe, Jr.
Secondary: Jens Meiler
Type of Trainee