Beginnings and early history of the International Conferences on Magnetic Resonance in Biological Systems: development of the basic ideas in the field.

The early history of the principal meeting in the field of biological NMR spectroscopy, the International Conference on Magnetic Resonance in Biological Systems (ICMRBS), is presented from the perspective of one of the founders.

Predicting 15N chemical shifts in proteins using the preceding residue-specific individual shielding surfaces from phi, psi i-1, and chi 1 torsion angles.

Empirical shielding surfaces are most commonly used to predict chemical shifts in proteins from known backbone torsion angles, phi and psi. However, the prediction of (15)N chemical shifts using this technique is significantly poorer, compared to that for the other nuclei such as (1)H(alpha), (13)C(alpha), and (13)C(beta). In this study, we investigated the effects from the preceding residue and the side-chain geometry, chi(1), on (15)N chemical shifts by statistical methods. For an amino acid sequence XY, the (15)N chemical shift of Y is expressed as a function of the amino acid types of X and Y, as well as the backbone torsion angles, phi and psi(i-1). Accordingly, 380 empirical 'Preceding Residue Specific Individual (PRSI)' (15)N chemical shift shielding surfaces, representing all the combinations of X and Y (except for Y=Pro), were built and used to predict (15)N chemical shift from phi and psi(i-1). We further investigated the chi(1) effects, which were found to account for differences in (15)N chemical shifts by approximately 5 ppm for amino acids Val, Ile, Thr, Phe, His, Tyr, and Trp. Taking the chi(1) effects into account, the chi(1)-calibrated PRSI shielding surfaces (XPRSI) were built and used to predict (15)N chemical shifts for these amino acids. We demonstrated that (15)N chemical shift predictions are significantly improved by incorporating the preceding residue and chi(1) effects. The present PRSI and XPRSI shielding surfaces were extensively compared with three recently published programs, SHIFTX (Neal et al., 2003), SHIFTS (Xu and Case, 2001 and 2002), and PROSHIFT (Meiler, 2003) on a set of ten randomly selected proteins. A set of Java programs using XPRSI shielding surfaces to predict (15)N chemical shifts in proteins were developed and are freely available for academic users at http://www.pronmr.com or by sending email to one of the authors Yunjun Wang (yunjunwang@yahoo.com).

Surface plasmon resonance studies of wild-type and AV77 tryptophan repressor resolve ambiguities in super-repressor activity.

The interactions of wild-type (WT) and AV77 tryptophan repressor (TR) with several operators have been studied using surface plasmon resonance. The use of this real-time method has been able to settle several outstanding issues in the field, in a way that has heretofore not been possible. We resolve the issue of the super-repressor status of the AV77 aporepressor and find that in contrast to early studies, which found no significant difference in the binding constants in vitro to those of the WT, that there is indeed a clear difference in the binding constant that can simply account for the phenotype. Accordingly, there is no need for alternative proposals invoking complex equilibria with in vivo components not found in the in vitro experiments. In addition, we find that the AV77 holorepressor-DNA complex is much more stable than the equivalent WT complex, which has not been apparent from either in vitro or equilibrium binding experiments.

Investigation of the neighboring residue effects on protein chemical shifts.

In this study, we report nearest neighbor residue effects statistically determined from a chemical shift database. For an amino acid sequence XYZ, we define two correction factors, Delta((X)Y)n,s and Delta(Y(Z))n,s, representing the effects on Y's chemical shifts from the preceding residue (X) and the following residue (Z), respectively, where X, Y, and Z are any of the 20 naturally occurring amino acids, n stands for (1)H(N), (15)N, (1)H(alpha), (13)C(alpha), (13)C(beta), and (13)C' nuclei, and s represents the three secondary structural types beta-strand, random coil, and alpha-helix. A total of approximately 14400 Delta((X)Y)n,s and Delta(Y(Z))n,s, representing nearly all combinations of X, Y, Z, n, and s, have been quantitatively determined. Our approach overcomes the limits of earlier experimental methods using short model peptides, and the resulting correction factors have important applications such as chemical shift prediction for the folded proteins. More importantly, we have found, for the first time, a linear correlation between the Delta((X)Y)n,s (n = (15)N) and the (13)C(alpha) chemical shifts of the preceding residue X. Since (13)C(alpha) chemical shifts of the 20 amino acids, which span a wide range of 40-70 ppm, are largely dominated by one property, the electron density of the side chain, the correlation indicates that the same property is responsible for the effect on the following residue. The influence of the secondary structure on both the chemical shifts and the nearest neighbor residue effect are also investigated.

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

For a long time, NMR chemical shifts have been used to identify protein secondary structures. Currently, this is accomplished through comparing the observed (1)H(alpha), (13)C(alpha), (13)C(beta), or (13)C' 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 (beta-strand, alpha-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: (13)C(alpha)>(13)C'>(1)H(alpha)>(13)C(beta)>(15)N>(1)H(N) to distinguish an alpha-helix from a random coil; and (1)H(alpha)>(13)C(beta) >(1)H(N) approximately (13)C(alpha) approximately (13)C' approximately (15)N for a beta-strand from a random coil. Amide (15)N and (1)H(N) chemical shifts, which are generally excluded from the application, in fact, were found to be helpful in distinguishing a beta-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.