Protein structural segments and their interconnections derived from optical spectra. Thermal unfolding of ribonuclease T-1 as an example

A novel descriptor for protein structure is examined here that goes beyond predictions of the average fractional components (FC) of a few conformational types and represents the number and interconnection of segments of continuous, well-defined secondary structural elements such as alpha-helices and beta-sheets. This matrix descriptor can be predicted from optical spectra using neural network methods. The new matrix plus traditional FC descriptors can be quickly and generally obtained to provide a level of detail not previously derived from optical spectra and a discrimination between proteins that might otherwise be viewed as being very similar using just the FC descriptor. As an example of its potential utilization, this matrix descriptor approach was applied to an analysis of both the native state and the reversible thermal denaturation of ribonuclease T-1 in H2O. Analyses of the FTIR spectral data indicate initial loss of the major helical segment at 50-55 degrees C but with little accompanying change in the number of sheet segments or the sheet FC values, Circular dichroism (CD) and vibrational CD data are also used to support this interpretation based on FC changes with temperature. Parallel analysis of the corresponding data for this protein in D2O demonstrates that the method is sensitive to the match between the degree of H-D exchange used to prepare samples for the unknown and the reference data set.