Glycan struture determination using CheSweet and Bayesian inferece

ARROYUELO, AGUSTINA.; VILA, JORGE A.; GARAY, PABLO G.; MARTÍN OSVALDO A.

San Luis

Conferencia; PyData San Luis 2017; 2017

Glycans are the most abundant and structurally diverse biomolecules in nature. The knowledge of their tridimensional structure is necessary to understand in detail, at atomic level, the molecular processes in which they are involved. Chemical Shifts (CS) are observables obtained from Nuclear Magnetic Resonance experiments that are highly sensitive probes to sense conformational changes. CS can be calculated accurately using quantum chemical tools, although these computations are very demanding for routine computations of more than a few conformations. For that reason we have developed CheSweet [ref], a Python module for accurate and fast computation of CS. Here we discuss some details of its implementation, and a result of using CheSweet as part of a Bayesian model implemented in PyMC3 [(Salvatier et al. 2016)]) to predict glycan structures.