Flexibility of KorA, a plasmid-encoded, global transcription regulator, in the presence and the absence of its operator.

The IncP (Incompatibility group P) plasmids are important carriers in the spread of antibiotic resistance across Gram-negative bacteria. Gene expression in the IncP-1 plasmids is stringently controlled by a network of four global repressors, KorA, KorB, TrbA and KorC interacting cooperatively. Intriguingly, KorA and KorB can act as co-repressors at varying distances between their operators, even when they are moved to be on opposite sides of the DNA. KorA is a homodimer with the 101-amino acid subunits, folding into an N-terminal DNA-binding domain and a C-terminal dimerization domain. In this study, we have determined the structures of the free KorA repressor and two complexes each bound to a 20-bp palindromic DNA duplex containing its consensus operator sequence. Using a combination of X-ray crystallography, nuclear magnetic resonance spectroscopy, SAXS and molecular dynamics calculations, we show that the linker between the two domains is very flexible and the protein remains highly mobile in the presence of DNA. This flexibility allows the DNA-binding domains of the dimer to straddle the operator DNA on binding and is likely to be important in cooperative binding to KorB. Unexpectedly, the C-terminal domain of KorA is structurally similar to the dimerization domain of the tumour suppressor p53.

Discovery of novel membrane binding structures and functions.

The function of a protein is determined by its intrinsic activity in the context of its subcellular distribution. Membranes localize proteins within cellular compartments and govern their specific activities. Discovering such membrane-protein interactions is important for understanding biological mechanisms and could uncover novel sites for therapeutic intervention. We present a method for detecting membrane interactive proteins and their exposed residues that insert into lipid bilayers. Although the development process involved analysis of how C1b, C2, ENTH, FYVE, Gla, pleckstrin homology (PH), and PX domains bind membranes, the resulting membrane optimal docking area (MODA) method yields predictions for a given protein of known three-dimensional structures without referring to canonical membrane-targeting modules. This approach was tested on the Arf1 GTPase, ATF2 acetyltransferase, von Willebrand factor A3 domain, and Neisseria gonorrhoeae MsrB protein and further refined with membrane interactive and non-interactive FAPP1 and PKD1 pleckstrin homology domains, respectively. Furthermore we demonstrate how this tool can be used to discover unprecedented membrane binding functions as illustrated by the Bro1 domain of Alix, which was revealed to recognize lysobisphosphatidic acid (LBPA). Validation of novel membrane-protein interactions relies on other techniques such as nuclear magnetic resonance spectroscopy (NMR), which was used here to map the sites of micelle interaction. Together this indicates that genome-wide identification of known and novel membrane interactive proteins and sites is now feasible and provides a new tool for functional annotation of the proteome.

Fold and function of polypeptide transport-associated domains responsible for delivering unfolded proteins to membranes.

Membranes of Gram-negative bacteria, mitochondria and chloroplasts receive and fold beta-barrel transmembrane proteins through the action of polypeptide transport-associated (POTRA) domains. In Escherichia coli, folding substrates are inserted into the outer membrane by the essential protein YaeT, a prototypic Omp85 protein. Here, the articulation between tandem POTRA domains in solution is defined by nuclear magnetic resonance (NMR) spectroscopy, indicating an unprecedented juxtaposition. The novel solution orientations of all five POTRA domains are revealed by small-angle X-ray scattering of the entire 46 kDa periplasmic region. NMR titration studies show that strands from YaeT's canonical folding substrate, PhoE, bind non-specifically along alternating sides of its mixed beta sheets, thus providing an ideal platform for helping to fold nascent outer-membrane proteins. Together, this provides the first structural model of how multiple POTRA domains recruit substrates from the periplasmic solution into the outer membrane.

Lipid interaction networks of peripheral membrane proteins revealed by data-driven micelle docking.

Many signaling and trafficking proteins contain modular domains that bind reversibly to cellular membranes. The structural basis of the intermolecular interactions which mediate these membrane-targeting events remains elusive since protein-membrane complexes are not directly accessible to standard structural biology techniques. Here we report a fast protein-micelle docking methodology that yields three-dimensional model structures of proteins inserted into micelles, revealing energetically favorable orientations, convergent insertion angles, and an array of protein-lipid interactions at atomic resolution. The method is applied to two peripheral membrane proteins, the early endosome antigen 1 (EEA1) FYVE (a zinc finger domain found in the proteins Fab1, YOTB/ZK632.12, Vac1, and EEA1) and Vam7p phagocyte oxidase homology domains, which are revealed to form extensive networks of interactions with multiple phospholipid headgroups and acyl chains. The resulting structural models explain extensive published mutagenesis data and reveal novel binding determinants. The docking restraints used here were based on NMR data, but can be derived from any technique that detects insertion of protein residues into a membrane, and can be applied to virtually any peripheral membrane protein or membrane-like structure.

Using wavelet de-noised spectra in NMR screening.

Principal component analysis (PCA) is a commonly used algorithm in multivariate analysis of NMR screening data. PCA substantially reduces the complexity of data in which a large number of variables are interrelated. For series of NMR spectra obtained for ligand binding, it is commonly used to visually group spectra with a similar response to ligand binding. A series of filters are applied to the experimental data to obtain suitable descriptors for PCA which optimize computational efficiency and minimize the weight of small chemical shift variations. The most common filter is bucketing where adjacent points are summed to a bucket. To overcome some inherent disadvantages of the bucketing procedure we have explored the effect of wavelet de-noising on multivariate analysis, using a series of HSQC spectra of proteins with different ligands present. The combination of wavelet de-noising and PCA is most efficient when PCA is applied to wavelet coefficients. This new algorithm yields good clustering and can be applied to series of one- or two-dimensional spectra.

Solution structure of the twelfth cysteine-rich ligand-binding repeat in rat megalin.

Megalin, an approx. 600 kDa transmembrane glycoprotein that acts as multi-ligand transporter, is a member of the low density lipoprotein receptor gene family. Several cysteine-rich repeats, each consisting of about 40 residues, are responsible for the multispecific binding of ligands. The solution structure of the twelfth cysteine-rich ligand-binding repeat with class A motif found in megalin features two short beta-strands and two helical turns, yielding the typical fold with a I-III, II-V and IV-VI disulfide bridge connectivity pattern and a calcium coordination site at the C-terminal end. The resulting differences in electrostatic surface potential compared to other ligand-binding modules of this gene family, however, may be responsible for the functional divergence.

Automated protein NMR structure determination using wavelet de-noised NOESY spectra.

A major time-consuming step of protein NMR structure determination is the generation of reliable NOESY cross peak lists which usually requires a significant amount of manual interaction. Here we present a new algorithm for automated peak picking involving wavelet de-noised NOESY spectra in a process where the identification of peaks is coupled to automated structure determination. The core of this method is the generation of incremental peak lists by applying different wavelet de-noising procedures which yield peak lists of a different noise content. In combination with additional filters which probe the consistency of the peak lists, good convergence of the NOESY-based automated structure determination could be achieved. These algorithms were implemented in the context of the ARIA software for automated NOE assignment and structure determination and were validated for a polysulfide-sulfur transferase protein of known structure. The procedures presented here should be commonly applicable for efficient protein NMR structure determination and automated NMR peak picking.

Solution structure of the 30 kDa polysulfide-sulfur transferase homodimer from Wolinella succinogenes.

The periplasmic polysulfide-sulfur transferase (Sud) protein encoded by Wolinella succinogenes is involved in oxidative phosphorylation with polysulfide-sulfur as a terminal electron acceptor. The polysulfide-sulfur is covalently bound to the catalytic Cys residue of the Sud protein and transferred to the active site of the membranous polysulfide reductase. The solution structure of the homodimeric Sud protein has been determined using heteronuclear multidimensional NMR techniques. The structure is based on NOE-derived distance restraints, backbone hydrogen bonds, and torsion angle restraints as well as residual dipolar coupling restraints for a refinement of the relative orientation of the monomer units. The monomer structure consists of a five-stranded parallel beta-sheet enclosing a hydrophobic core, a two-stranded antiparallel beta-sheet, and six alpha-helices. The dimer fold is stabilized by hydrophobic residues and ion pairs found in the contact area between the two monomers. Similar to rhodanese enzymes, Sud catalyzes the transfer of the polysulfide-sulfur to the artificial acceptor cyanide. Despite their similar functions and active sites, the amino acid sequences and structures of these proteins are quite different.