Predicting the redox state and secondary structure of cysteine residues using multi-dimensional classification analysis of NMR chemical shifts.

A tool for predicting the redox state and secondary structure of cysteine residues using multi-dimensional analyses of different combinations of nuclear magnetic resonance (NMR) chemical shifts has been developed. A data set of cysteine [Formula: see text], (13)C(α), (13)C(ß), (1)H(α), (1)H(N), and (15)N(H) chemical shifts was created, classified according to redox state and secondary structure, using a library of 540 re-referenced BioMagResBank (BMRB) entries. Multi-dimensional analyses of three, four, five, and six chemical shifts were used to derive rules for predicting the structural states of cysteine residues. The results from 60 BMRB entries containing 122 cysteines showed that four-dimensional analysis of the C(α), C(ß), H(α), and N(H) chemical shifts had the highest prediction accuracy of 100 and 95.9 % for the redox state and secondary structure, respectively. The prediction of secondary structure using 3D, 5D, and 6D analyses had the accuracy of ~90 %, suggesting that H(N) and [Formula: see text] chemical shifts may be noisy and made the discrimination worse. A web server (6DCSi) was established to enable users to submit NMR chemical shifts, either in BMRB or key-in formats, for prediction. 6DCSi displays predictions using sets of 3, 4, 5, and 6 chemical shifts, which shows their consistency and allows users to draw their own conclusions. This web-based tool can be used to rapidly obtain structural information regarding cysteine residues directly from experimental NMR data.

Type I and II ß-turns prediction using NMR chemical shifts.

A method for predicting type I and II ß-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated ß-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes ß-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I ß-turns, the mean values of Cο, Cα, H(N), and N(H) chemical shifts were generally (i + 1) > (i + 2). The mean values of Cß and Hα chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII ß-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II ß-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2% with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II ß-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the ß-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.

2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts.

Chemical shifts of amino acids in proteins are the most sensitive and easily obtainable NMR parameters that reflect the primary, secondary, and tertiary structures of the protein. In recent years, chemical shifts have been used to identify secondary structure in peptides and proteins, and it has been confirmed that (1)H(alpha), (13)C(alpha), (13)C(beta), and (13)C' NMR chemical shifts for all 20 amino acids are sensitive to their secondary structure. Currently, most of the methods are purely based on one-dimensional statistical analyses of various chemical shifts for each residue to identify protein secondary structure. However, it is possible to achieve an increased accuracy from the two-dimensional analyses of these chemical shifts. The 2DCSi approach performs two-dimension cluster analyses of (1)H(alpha), (1)H(N), (13)C(alpha), (13)C(beta), (13)C', and (15)N(H) chemical shifts to identify protein secondary structure and the redox state of cysteine residue. For the analysis of paired chemical shifts of 6 data sets, each of the 20 amino acids has its own 15 two-dimension cluster scattering diagrams. Accordingly, the probabilities for identifying helix and extended structure were calculated by using our scoring matrix. Compared with existing the chemical shift-based methods, it appears to improve the prediction accuracy of secondary structure identification, particularly in the extended structure. In addition, the probability of the given residue to be helix or extended structure is displayed, allows the users to make decisions by themselves.