Donna A. Bassolino

Solution structures of the rabbit neutrophil defensin NP-5.

Solution structures of the rabbit neutrophil defensin NP-5 have been determined by 1H nuclear magnetic resonance (n.m.r.) spectroscopy and distance geometry techniques. This 33 amino acid peptide is part of the oxygen-independent mammalian defense system against microbial infection. The structures were generated from 107 n.m.r. derived inter-residue proton-proton distance constraints. A distance geometry algorithm was then used to determine the range of structures consistent with these distance constraints. These distance geometry calculations employed an improved algorithm that allowed the chirality constraints to be relaxed on prochiral centers when it was not possible to make stereo-specific assignments of protons on these centers. This procedure gave superior results compared with standard distance geometry methods and also produced structures that were more consistent with the original n.m.r. data. Analysis of the NP-5 structures shows that the overall folding of the peptide backbone is well defined by the n.m.r. distance information but that the side-chain group conformations are generally less well defined.

Determination of Protein Structures in Solution Using Nmr Data and Impact

A new procedure for generating and refining protein structures that satisfy constraints derived from two-di mensional data includes an internal coordinate Monte Carlo search algorithm for conformational sampling and a simple target function with terms for describing the structural information contained in 2-D NMR spectra. Solution structures of the peptide neutrophil defensin NP-5, generated with a metric matrix distance geometry algorithm, are refined with the Monte Carlo procedure, leading to structures with interproton distances that are closer to the bounds observed in the NMR data and have improved local geometry. In model studies, an α- helical peptide is rapidly folded from an extended chain when NMR distance constraints corresponding to an α- helix are used as input parameters. The ab initio folding of NP-5 from an extended chain and possible application to studying protein folding pathways are discussed. Cal culations were carried out with the program package IMPACT (Integrated Modeling Program Using Applied Chemical Theory), under development. An overview of IMPACT is presented.

Simulating the effect of the two-spin approximation on the generation of protein structures from NOE data

The use of structural information derived from NMR data, when combined with computer modeling techniques, has enabled the structures of many proteins to be determined ( 1-6). Among the most useful structural information is interproton distances which can be derived from nuclear Overhauser enhancement experiments. For small rigid molecules, the intensity of the NOE between protons i andj is proportional to the inverse sixth power of the distance between the two protons. In macromolecules, indirect magnetization transfer (spin diffusion) from nearby protons and intramolecular dynamics affect the magnitude of the NOE between protons i andj and contribute to errors in estimating interproton distances from NOE cross peaks ( 7-9). Several methods have been proposed for dealing with the spin-diffusion problem. These methods make use of the relaxation matrix to incorporate multispin effects (10-14). The simplest but least systematic approach is to calculate a theoretical spectrum from the relaxation matrix using a trial structure, and to adjust distances by trial and error between pairs of protons whose calculated cross peaks have the largest deviations from the experimental values ( 15-18). In another approach theoretical cross peaks from a trial structure are merged with experimental NOES ( I2,19,20), and after the solution of the Bloch equations is inverted to obtain the relaxation matrix, the resulting distances can be added as constraints in a molecular-dynamics or distance geometry refinement. The most rigorous and computationally intensive procedures involve the direct refinement of calculated against experimental NOE spectra, either by analytically evaluating the gradients of the calculated NOES with respect to coordinates (21-23) or by using a Monte-Carlo procedure (24). Despite the recent focus on incorporating spin-diffusion effects in the refinement of macromolecular structures, it is unclear how much error is introduced by spin diffusion. This is of interest because, although the direct refinement of calculated against experimental NOE intensities is in principle the more “correct” procedure, it is much more computationally intensive than re-

Fold Recognition using the OPLS All-Atom Potential and the Surface Generalized Born Solvent Model

Protein decoy data sets provide a benchmark for testing scoring functions designed for fold recognition and protein homology modeling problems. It is commonly believed that statistical potentials based on reduced atomic models are better able to discriminate native-like from misfolded decoys than scoring functions based on more detailed molecular mechanics models. Recent benchmark tests, however, suggest otherwise. Further analysis of the effectiveness of all atom molecular mechanics scoring functions for detecting misfolded decoys and direct comparison with results obtained using a statistical potential derived for a reduced atomic model are presented in this report. The OPLS all-atom force field is used as a scoring function to detect native protein folds among the Park & Levitt large decoy sets. Solvent electrostatic effects are included through the Surface Generalized Born (SGB) model. The OPLS potential with SGB solvation (OPLS-AA/SGB) provides good discrimination between native-like structures and non-native decoys. From an analysis of the individual energy components of the OPLS-AA/SGB potential for the native and the best-ranked decoy, it is determined that a roughly even balance of the terms of the potential is responsible for distinguishing the native from the misfolded conformations. Different combinations of individual energy terms provide less discrimination than the total energy. The effects of scoring decoys using several dielectric models are compared also. With the SGB solvation model, close to 100% of the structures with energies within 100 kcal/mol of the native state minimum are native-like. In contrast, only 20% of the low energy structures are found to be native-like when a distance dependent dielectric is used instead of SGB to model solvent electrostatic effects. The results are consistent with observations that all-atom molecular potentials coupled with intermediate level solvent dielectric models are competitive with knowledge-based potentials for decoy detection and protein modeling problems such as fold recognition.