An Accurate Prediction Method for Protein Structural Class from Signal Patterns of NMR Spectra in the Absence of Chemical Shift Assignments

Abstract

The structural class information about a protein is important to understand its biological properties. NMR is one of the most powerful tools to obtain structural information of proteins in atomic resolution. However, an analysis of protein three-dimensional structure from NMR spectra usually requires laborious chemical shift assignment. We developed a new method for predicting the protein structural class directly from the NMR spectra without any chemical shift assignment. The results show that our method outperforms the methods using current secondary structure prediction.