Probability‐based protein secondary structure identification using combined NMR chemical‐shift data

Publisher Website
Google Scholar
Abstract
For a long time, NMR chemical shifts have been used to identify protein secondary structures. Currently, this is accomplished through comparing the observed 1Hα, 13Cα, 13Cβ, or 13C′ chemical shifts with the random coil values. Here, we present a new protocol, which is based on the joint probability of each of the three secondary structural types (β‐strand, α‐helix, and random coil) derived from chemical‐shift data, to identify the secondary structure. In combination with empirical smooth filters/functions, this protocol shows significant improvements in the accuracy and the confidence of identification. Updated chemical‐shift statistics are reported, on the basis of which the reliability of using chemical shift to identify protein secondary structure is evaluated for each nucleus. The reliability varies greatly among the 20 amino acids, but, on average, is in the order of: 13Cα>13C′>1Hα>13Cβ>15N>1HN to distinguish an α‐helix from a random coil; and 1Hα>13Cβ >1HN ∼13Cα∼13C′∼15N for a β‐strand from a random coil. Amide 15N and 1HN chemical shifts, which are generally excluded from the application, in fact, were found to be helpful in distinguishing a β‐strand from a random coil. In addition, the chemical‐shift statistical data are compared with those reported previously, and the results are discussed. A JAVA User Interface program has been developed to make the entire procedure fully automated and is available via http://ccsr3150‐p3.stanford.edu.