Veni,vidi, vici - atomic resolution unravelling the mysteries of protein function

Atomic resolution macromolecular crystallography has become a powerful and versatile tool in structural biology; the number of atomic resolution structures is steadily increasing. Novel techniques are being developed and the use of complementary methods that span the field from sample preparation to validation and analysis of the resulting models has emerged. These allow the fuller exploitation of the information stored in crystal structures and reveal a depth of structural detail that was unattainable in the recent past.     

The program ASNO for computer-supported collection of NOE upper distance constraints as input for protein structure determination

Summary A new program, ASNO (ASsign NOes), for computer-supported NOE cross-peak assignments is described. ASNO is used for structure refinement in several rounds of NOESY cross-peak assignments and 3D structure calculations, where the preliminary structures are used as a reference to resolve ambiguities in NOE assignments which are otherwise based on the chemical shifts available from the sequence-specific resonance assignments. The practical use of ASNO for proteins is illustrated with the structure determination of Dendrotoxin K from Dendroaspis polylepis polylepis.Abbreviations Toxin K dendrotoxin K (or trypsin inhibitor homologue K) from the venom of the black mamba Dendroaspis polylepis polylepis - NOE nuclear Overhauser effect - NOESY NOE spectroscopy - REDAC use of redundant dihedral angle constraints - RMSD root-mean-square deviation To whom correspondence should be addressed.     

Automatically Fixing Errors in Glycoprotein Structures with Rosetta

1. Download high-res image (143KB)
2. Download full-size image

     

goCTF: Geometrically optimized CTF determination for single-particle cryo-EM

Preferred particle orientation represents a recurring problem in single-particle cryogenic electron microcopy (cryo-EM). A specimen-independent approach through tilting has been attempted to increase particle orientation coverage, thus minimizing anisotropic three-dimensional (3D) reconstruction. However, focus gradient is a critical issue hindering tilt applications from being a general practice in single-particle cryo-EM. The present study describes a newly developed geometrically optimized approach, goCTF, to reliably determine the global focus gradient. A novel strategy of determining contrast transfer function (CTF) parameters from a sector of the signal preserved power spectrum is applied to increase reliability. Subsequently, per-particle based local focus refinement is conducted in an iterative manner to further improve the defocus accuracy. Novel diagnosis methods using a standard deviation defocus plot and goodness of fit heatmap have also been proposed to evaluate CTF fitting quality prior to 3D refinement. In a benchmark study, goCTF processed a published single-particle cryo-EM dataset for influenza hemagglutinin trimer collected at a 40-degree specimen tilt. The resulting 3D reconstruction map was improved from 4.1?Å to 3.7?Å resolution. The goCTF program is built on the open-source code of CTFFIND4, which adopts a consistent user interface for ease of use.     

Homology models of dipeptidyl peptidases 8 and 9 with a focus on loop predictions near the active site

Dipeptidyl peptidase 4 (DP4) inhibitors are currently under intensive investigation in late-stage clinical trials as a treatment for type II diabetes. Lack of selectivity toward the related enzymes DP8 and DP9 has recently emerged as a possible source of drug-induced toxicity. Unlike DP4, X-ray structures of DP8 and DP9 are not yet available. As an aid to understanding the structural basis for selectivity, the authors have constructed homology models of DP8 and DP9 based on the X-ray coordinates of DP4. Accurate sequence alignment reveals common structural features indicative for a well-preserved overall fold comprising two domains, namely, a hydrolase domain and a so-called beta-propeller, which together form the active site deeply buried within the protein. The conformation of two loops inside this deep cavity is particularly relevant for the active sites. The authors used a published protocol for loop prediction based on conformational sampling and energy analysis to generate plausible solutions for these two loops. The predictive power of the approach was successfully evaluated for the template protein DP4 and two additional known structures from the same protein family, namely, FAP and DPX. The authors also show that inclusion of the covalent ligand NVP-728 greatly enhances the refinement. Based on the established evaluation protocol, the corresponding loops of DP8 and DP9 were predicted and the resulting active sites were compared with DP4. In particular, the authors conclude that differences in the P2-pocket are relevant for the design of selective DP4 inhibitors. The loss of key interactions in DP8 and DP9 as predicted from their models is consistent with the selectivity profile of the DP4 clinical candidate MK-431.     

Refinement of protein structure against non-redundant carbonyl <Superscript>13</Superscript>C NMR relaxation

Carbonyl ¹³C′ relaxation is dominated by the contribution from the ¹³C′ chemical shift anisotropy (CSA). The relaxation rates provide useful and non-redundant structural information in addition to dynamic parameters. It is straightforward to acquire, and offers complimentary structural information to the ¹⁵N relaxation data. Furthermore, the non-axial nature of the ¹³C′ CSA tensor results in a T₁/T₂ value that depends on an additional angular variable even when the diffusion tensor of the protein molecule is axially symmetric. This dependence on an extra degree of freedom provides new geometrical information that is not available from the NH dipolar relaxation. A protocol that incorporates such structural restraints into NMR structure calculation was developed within the program Xplor-NIH. Its application was illustrated with the yeast Fis1 NMR structure. Refinement against the ¹³C′ T₁/T₂ improved the overall quality of the structure, as evaluated by cross-validation against the residual dipolar coupling as well as the ¹⁵N relaxation data. In addition, possible variations of the CSA tensor were addressed. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Protein structure refinement by optimization

Knowledge‐based protein potentials are simplified potentials designed to improve the quality of protein models, which is important as more accurate models are more useful for biological and pharmaceutical studies. Consequently, knowledge‐based potentials often are designed to be efficient in ordering a given set of deformed structures denoted decoys according to how close they are to the relevant native protein structure. This, however, does not necessarily imply that energy minimization of this potential will bring the decoys closer to the native structure. In this study, we introduce an iterative strategy to improve the convergence of decoy structures. It works by adding energy optimized decoys to the pool of decoys used to construct the next and improved knowledge‐based potential. We demonstrate that this strategy results in significantly improved decoy convergence on Titan high resolution decoys and refinement targets from Critical Assessment of protein Structure Prediction competitions. Our potential is formulated in Cartesian coordinates and has a fixed backbone potential to restricts motions to be close to those of a dihedral model, a fixed hydrogen‐bonding potential and a variable coarse grained carbon alpha potential consisting of a pair potential and a novel solvent potential that are b‐spline based as we use explicit gradient and Hessian for efficient energy optimization. Proteins 2015; 83:1616–1624. © 2015 Wiley Periodicals, Inc.     

Progress and challenges in high-resolution refinement of protein structure models

Achieving atomic level accuracy in de novo structure prediction presents a formidable challenge even in the context of protein models with correct topologies. High-resolution refinement is a fundamental test of force field accuracy and sampling methodology, and its limited success in both comparative modeling and de novo prediction contexts highlights the limitations of current approaches. We constructed four tests to identify bottlenecks in our current approach and to guide progress in this challenging area. The first three tests showed that idealized native structures are stable under our refinement simulation conditions and that the refinement protocol can significantly decrease the root mean square deviation (RMSD) of perturbed native structures. In the fourth test we applied the refinement protocol to de novo models and showed that accurate models could be identified based on their energies, and in several cases many of the buried side chains adopted native-like conformations. We also showed that the differences in backbone and side-chain conformations between the refined de novo models and the native structures are largely localized to loop regions and regions where the native structure has unusual features such as rare rotamers or atypical hydrogen bonding between beta-strands. The refined de novo models typically have higher energies than refined idealized native structures, indicating that sampling of local backbone conformations and side-chain packing arrangements in a condensed state is a primary obstacle.