Performance of a neural-network-based determination of amino acid class and secondary structure from 1H-15N NMR data

A neural network which can determine both amino acid class andsecondary structure using NMR data from 15N-labeled proteinsis described. We have included nitrogen chemical shifts,3JHNHα coupling constants, α-protonchemical shifts, and side-chain proton chemical shifts as input to athree-layer feed-forward network. The network was trained with 456 spinsystems from several proteins containing various types of secondarystructure, and tested on human ubiquitin, which has no sequence homologywith any of the proteins in the training set. A very limited set of data,representative of those from a TOCSY-HSQC and HNHA experiment, was used.Nevertheless, in 60% of the spin systems the correct amino acid classwas among the top two choices given by the network, while in 96% ofthe spin systems the secondary structure was correctly identified. Theperformance of this network clearly shows the potential of the neuralnetwork algorithm in the automation of NMR spectral analysis.

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