Target highlights in CASP9: Experimental target structures for the critical assessment of techniques for protein structure prediction

15 pags, 9 figsOne goal of the CASP community wide experiment on the critical assessment of techniques for protein structure prediction is to identify the current state of the art in protein structure prediction and modeling. A fundamental principle of CASP is blind prediction on a set of relevant protein targets, that is, the participating computational methods are tested on a common set of experimental target proteins, for which the experimental structures are not known at the time of modeling. Therefore, the CASP experiment would not have been possible without broad support of the experimental protein structural biology community. In this article, several experimental groups discuss the structures of the proteins which they provided as prediction targets for CASP9, highlighting structural and functional peculiarities of these structures: the long tail fiber protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase Iβ dimerization/docking domain, the ectodomain of the JTB (jumping translocation breakpoint) transmembrane receptor, Autotaxin in complex with an inhibitor, the DNA-binding J-binding protein 1 domain essential for biosynthesis and maintenance of DNA base-J (β-D-glucosyl-hydroxymethyluracil) in Trypanosoma and Leishmania, an so far uncharacterized 73 residue domain from Ruminococcus gnavus with a fold typical for PDZ-like domains, a domain from the phycobilisome core-membrane linker phycobiliprotein ApcE from Synechocystis, the heat shock protein 90 activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. © 2011 Wiley-Liss, Inc.Grant sponsor: Spanish Ministry of Education and Science; Grant number: BFU2008-01588; Grant sponsor: European Commission; Grant number: NMP4-CT-2006-033256; Grant sponsor: Spanish Ministry of Education and Science (José Castillejo fellowship); Grant sponsor: Xunta de Galicia (Angeles Alvariño fellowship); Grant sponsor: National Institutes of Health; Grant numbers: K22-CA124517 (D.E.C.); R01-GM090161 (C.K.) GM074942; GM094585; Grant sponsor: U. S. Department of Energy, Office of Biological and Environmental Research; Grant number: DE-AC02-06CH11357 (to A.J.); Grant sponsor: Foundation for Polish Science (to K.M.); Grant sponsor: NSF; Grant number: DBI 0829586

Facilitating Unambiguous NMR Assignment and Enabling Higher Probe Density by Selective Labeling of all Methyl Containing Amino Acids

The deuteration of proteins and selective labeling of side chain methyl groups has greatly enhanced the molecular weight range of protein and protein complexes which can be studied using solution NMR spectroscopy. Protocols for the selective labeling of all six methyl group containing amino acids individually are available, however to date, only a maximum combination of five amino acids have been labeled simultaneously. Here, we describe a new methodology for the simultaneous, selective labeling of all six methyl containing amino acids using the 115 kDa homohexameric enzyme CoaD from E. coli as a model system. The utility of the labeling protocol is demonstrated by efficiently and unambiguously assigning all methyl groups in the enzymatic active site using a single 4D 13C-resolved HMQC-NOESY-HMQC experiment, in conjunction with a crystal structure. Furthermore, the six fold labeled protein was employed to characterize the interaction between the substrate analogue (R)-pantetheine and CoaD by chemical shift perturbations, demonstrating the benefit of the increased probe density

The structure of the flock house virus B2 protein, a viral suppressor of RNA interference, shows a novel mode of double-stranded RNA recognition

We report the structure of the flock house virus B2 protein, a potent suppressor of RNA interference (RNAi) in animals and plants. The B2 protein is a homodimer in solution and contains three α-helices per monomer. Chemical shift perturbation shows that an antiparallel arrangement of helices (α2/α2′) forms an elongated binding interface with double-stranded RNA (dsRNA). This implies a novel mode of dsRNA recognition and provides insights into the mechanism of RNAi suppression by B2

High-Confidence Protein–Ligand Complex Modeling by NMR-Guided Docking Enables Early Hit Optimization

Structure-based drug design is an integral part of modern day drug discovery and requires detailed structural characterization of protein–ligand interactions, which is most commonly performed by X-ray crystallography. However, the success rate of generating these costructures is often variable, in particular when working with dynamic proteins or weakly binding ligands. As a result, structural information is not routinely obtained in these scenarios, and ligand optimization is challenging or not pursued at all, representing a substantial limitation in chemical scaffolds and diversity. To overcome this impediment, we have developed a robust NMR restraint guided docking protocol to generate high-quality models of protein–ligand complexes. By combining the use of highly methyl-labeled protein with experimentally determined intermolecular distances, a comprehensive set of protein–ligand distances is generated which then drives the docking process and enables the determination of the correct ligand conformation in the bound state. For the first time, the utility and performance of such a method is fully demonstrated by employing the generated models for the successful, prospective optimization of crystallographically intractable fragment hits into more potent binders

High-Confidence Protein–Ligand Complex Modeling by NMR-Guided Docking Enables Early Hit Optimization

Structure-based drug design is an integral part of modern day drug discovery and requires detailed structural characterization of protein–ligand interactions, which is most commonly performed by X-ray crystallography. However, the success rate of generating these costructures is often variable, in particular when working with dynamic proteins or weakly binding ligands. As a result, structural information is not routinely obtained in these scenarios, and ligand optimization is challenging or not pursued at all, representing a substantial limitation in chemical scaffolds and diversity. To overcome this impediment, we have developed a robust NMR restraint guided docking protocol to generate high-quality models of protein–ligand complexes. By combining the use of highly methyl-labeled protein with experimentally determined intermolecular distances, a comprehensive set of protein–ligand distances is generated which then drives the docking process and enables the determination of the correct ligand conformation in the bound state. For the first time, the utility and performance of such a method is fully demonstrated by employing the generated models for the successful, prospective optimization of crystallographically intractable fragment hits into more potent binders

High-Confidence Protein–Ligand Complex Modeling by NMR-Guided Docking Enables Early Hit Optimization

Structure-based drug design is an integral part of modern day drug discovery and requires detailed structural characterization of protein–ligand interactions, which is most commonly performed by X-ray crystallography. However, the success rate of generating these costructures is often variable, in particular when working with dynamic proteins or weakly binding ligands. As a result, structural information is not routinely obtained in these scenarios, and ligand optimization is challenging or not pursued at all, representing a substantial limitation in chemical scaffolds and diversity. To overcome this impediment, we have developed a robust NMR restraint guided docking protocol to generate high-quality models of protein–ligand complexes. By combining the use of highly methyl-labeled protein with experimentally determined intermolecular distances, a comprehensive set of protein–ligand distances is generated which then drives the docking process and enables the determination of the correct ligand conformation in the bound state. For the first time, the utility and performance of such a method is fully demonstrated by employing the generated models for the successful, prospective optimization of crystallographically intractable fragment hits into more potent binders