Contribution of the a-baumannii A1S_0114 gene to the interaction with eukaryotic cells and virulence

Genetic and functional studies showed that some components of the Acinetobacter baumannii ATCC 17978 A1S_0112-A1S_0119 gene cluster are critical for biofilm biogenesis and surface motility. Recently, our group has shown that the A1S_0114 gene was involved in biofilm formation, a process related with pathogenesis. Confirming our previous results, microscopy images revealed that the ATCC 17978 10114 derivative lacking this gene was unable to form a mature biofilm structure. Therefore, other bacterial phenotypes were analyzed to determine the role of this gene in the pathogenicity of A. baumannii ATCC 17978. The interaction of the ATCC 17978 parental strain and the 10114 mutant with A549 human alveolar epithelial cells was quantified revealing that the A1S_0114 gene was necessary for proper attachment to A549 cells. This dependency correlates with the negative effect of the A1S_0114 deletion on the expression of genes coding for surface proteins and pili-assembly systems, which are known to play a role in adhesion. Three different experimental animal models, including vertebrate and invertebrate hosts, confirmed the role of the A1S_0114 gene in virulence. All of the experimental infection assays indicated that the virulence of the ATCC 17978 was significantly reduced when this gene was inactivated. Finally, we discovered that the A1S_0114 gene was involved in the production of a small lipopeptide-like compound herein referred to as acinetin 505 (Ac-505). Ac-505 was isolated from ATCC 17978 spent media and its chemical structure was interpreted by mass spectrometry. Overall, our observations provide novel information on the role of the A1S_0114 gene in A. baumannii’s pathobiology and lay the foundation for future work to determine the mechanisms by which Ac-505, or possibly an Ac-505 precursor, could execute critical functions as a secondary metaboliteS

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

Structural Asymmetry in the Closed State of Mitochondrial Hsp90 (TRAP1) Supports a Two-Step ATP Hydrolysis Mechanism

While structural symmetry is a prevailing feature of homo-oligomeric proteins, asymmetry provides unique mechanistic opportunities. We present the crystal structure of full-length TRAP1, the mitochondrial Hsp90 molecular chaperone, in a catalytically active closed state. The TRAP1 homodimer adopts a distinct, asymmetric conformation, where one protomer is reconfigured via a helix swap at the middle:C-terminal domain (MD:CTD) interface. This interface plays a critical role in client binding. Solution methods validate the asymmetry and show extension to Hsp90 homologs. Point mutations that disrupt unique contacts at each MD:CTD interface reduce catalytic activity and substrate binding and demonstrate that each protomer needs access to both conformations. Crystallographic data on a dimeric NTD:MD fragment suggests that asymmetry arises from strain induced by simultaneous NTD and CTD dimerization. The observed asymmetry provides the potential for an additional step in the ATPase cycle, allowing sequential ATP hydrolysis steps to drive both client remodeling and client release

NMR structure and MD simulations of the AAA protease intermembrane space domain indicates peripheral membrane localization within the hexaoligomer

AbstractWe have determined the solution NMR structure of the intermembrane space domain (IMSD) of the human mitochondrial ATPase associated with various activities (AAA) protease known as AFG3-like protein 2 (AFG3L2). Our structural analysis and molecular dynamics results indicate that the IMSD is peripherally bound to the membrane surface. This is a modification to the location of the six IMSDs in a model of the full length yeast hexaoligomeric homolog of AFG3L2 determined at low resolution by electron cryomicroscopy [1]. The predicted protein–protein interaction surface, located on the side furthest from the membrane, may mediate binding to substrates as well as prohibitins

SpecDB: A relational database for archiving biomolecular NMR spectral data

NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB

The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis

Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2-4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.ISSN:2052-446

NMR characterization of HtpG, the E. coli Hsp90, using sparse labeling with 13C-methyl alanine

A strategy for acquiring structural information from sparsely isotopically labeled large proteins is illustrated with an application to the E. coli heat-shock protein, HtpG (high temperature protein G), a 145 kDa dimer. It uses 13C-alanine methyl labeling in a perdeuterated background to take advantage of the sensitivity and resolution of Methyl-TROSY spectra, as well as the backbone-centered structural information from 1H-13C residual dipolar couplings (RDCs) of alanine methyl groups. In all, 40 of the 47 expected crosspeaks were resolved and 36 gave RDC data. Assignments of crosspeaks were partially achieved by transferring assignments from those made on individual domains using triple resonance methods. However, these were incomplete and in many cases the transfer was ambiguous. A genetic algorithm search for consistency between predictions based on domain structures and measurements for chemical shifts and RDCs allowed 60% of the 40 resolved crosspeaks to be assigned with confidence. Chemical shift changes of these crosspeaks on adding an ATP analog to the apo-protein are shown to be consistent with structural changes expected on comparing previous crystal structures for apo- and complex- structures. RDCs collected on the assigned alanine methyl peaks are used to generate a new solution model for the apo-protein structure