The Phyre2 web portal for protein modeling, prediction and analysis

Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission

The Shigella flexneri OmpA amino acid residues 188EVQ190 are essential for the interaction with the virulence factor PhoN2

Shigella flexneri is an intracellular pathogen that deploys an arsenal of virulence factors promoting host cell invasion, intracellular multiplication and intra- and inter-cellular dissemination. We have previously reported that the interaction between apyrase (PhoN2), a periplasmic ATP-diphosphohydrolase, and the C-terminal domain of the outer membrane (OM) protein OmpA is likely required for proper IcsA exposition at the old bacterial pole and thus for full virulence expression of Shigella flexneri (Scribano et al., 2014). OmpA, that is the major OM protein of Gram-negative bacteria, is a multifaceted protein that plays many different roles both in the OM structural integrity and in the virulence of several pathogens. Here, by using yeast two-hybrid technology and by constructing an in silico 3D model of OmpA from S. flexneri 5a strain M90T, we observed that the OmpA residues 188EVQ190 are likely essential for PhoN2-OmpA interaction. The 188EVQ190 amino acids are located within a flexible region of the OmpA protein that could represent a scaffold for protein-protein interaction

A novel bacterial l-arginine sensor controlling c-di-GMP levels in Pseudomonas aeruginosa

Nutrients such as amino acids play key roles in shaping the metabolism of microorganisms in natural environments and in host–pathogen interactions. Beyond taking part to cellular metabolism and to protein synthesis, amino acids are also signaling molecules able to influence group behavior in microorganisms, such as biofilm formation. This lifestyle switch involves complex metabolic reprogramming controlled by local variation of the second messenger 3′, 5′-cyclic diguanylic acid (c-di-GMP). The intracellular levels of this dinucleotide are finely tuned by the opposite activity of dedicated diguanylate cyclases (GGDEF signature) and phosphodiesterases (EAL and HD-GYP signatures), which are usually allosterically controlled by a plethora of environmental and metabolic clues. Among the genes putatively involved in controlling c-di-GMP levels in P. aeruginosa, we found that the multidomain transmembrane protein PA0575, bearing the tandem signature GGDEF-EAL, is an l-arginine sensor able to hydrolyse c-di-GMP. Here, we investigate the basis of arginine recognition by integrating bioinformatics, molecular biophysics and microbiology. Although the role of nutrients such as l-arginine in controlling the cellular fate in P. aeruginosa (including biofilm, pathogenicity and virulence) is already well established, we identified the first l-arginine sensor able to link environment sensing, c-di-GMP signaling and biofilm formation in this bacterium

Defining the complementarities between antibodies and haptens to refine our understanding and aid the prediction of a successful binding interaction

Acknowledgments The authors would like to thank the Scottish Universities Life Sciences Alliance (SULSA) for their support.Peer reviewedPublisher PD

Homology Modeling of Toll-Like Receptor Ligand-Binding Domains

Toll-like receptors (TLRs) are in the front-line during the initiation of an innate immune response against invading pathogens. TLRs are type I transmembrane proteins that are expressed on the surface of immune system cells. They are evolutionarily conserved between insects and vertebrates. To date, 13 groups of mammalian TLRs have been identified, ten in humans and 13 in mice. They share a modular structure that consists of a leucine-rich repeat (LRR) ectodomain, a single transmembrane helix and a cytoplasmic Toll/interleukin-1 receptor (TIR) domain. Most TLRs have been shown to recognize pathogen-associated molecular patterns (PAMPs) from a wide range of invading agents and initiate intracellular signal transduction pathways to trigger expression of genes, the products of which can control innate immune responses. The TLR signaling pathways, however, must be under tight negative regulation to maintain immune balance because over-activation of immune responses in the body can cause autoimmune diseases. The TLR ectodomains are highly variable and are directly involved in ligand recognition. So far, crystal structures are missing for most TLR ectodomains because structure determination by X-ray diffraction or nuclear magnetic resonance (NMR) spectroscopy experiments remains time-consuming, and sometimes the crystallization of a protein can be very difficult. Computational modeling enables initial predictions of three-dimensional structures for the investigation of receptor-ligand interaction mechanisms. Computational methods are also helpful to develop new TLR agonists and antagonists that have therapeutic significance for diseases. In this dissertation, an LRR template assembly approach for homology modeling of TLR ligand-binding domains is discussed. To facilitate the modeling work, two databases, TollML and LRRML, have been established. With this LRR template assembly approach, the ligand-binding domains of human TLR5-10 and mouse TLR11-13 were modeled. Based on the models of human TLR7, 8 and 9, we predicted potential ligand-binding residues and possible configurations of the receptor-ligand complex using a combined procedure. In addition, we modeled the cytoplasmic TIR domains of TLR4 and 7, the TLR adaptor protein MyD88 (myeloid differentiation primary response protein 88) and the TLR inhibitor SIGIRR (Single immunoglobulin interleukin-1 receptor-related molecule) to investigate the structural mechanism of TLR negative regulation

Refinement of the gonadotropin releasing hormone receptor I homology model by applying molecular dynamics

Sexual maturation of human cells in ovaries and prostate is linked to the biochemical cascade initiated by the activation of cell receptors through the binding of Gonadotropin Releasing Hormone (GnRH). The GnRH receptors (GnRHR) are part of the rhodopsin G-protein coupled receptor (GPCR) family and consist of seven trans–membrane helical domains connected via extra– and intra–cellular segments. The GnRH–GnRHR complex has been implicated in various forms of prostate and ovarian cancer. The lack of any structural data about the GnRH receptor impedes the design of antagonists for use in cancer treatment. The aim of the study is to devise a model of GnRHR to be used further for the design of improved peptide/non-peptide GnRH analogues and, to our knowledge provide new structural information regarding the extracellular loop 2 (ECL2) that acts a regulator of ligand entry to GnRHR. The common structural characteristics, of the members of the rhodopsin family of GPCRs, have been employed for the construction of a homology model for GnRHR. Structural information from the human β2–adrenergic receptor, as well as rhodopsins have been used in order to create a theoretical model for GnRHR. Furthermore, molecular dynamics (MD) simulations have been employed for the refinement of the model and to explore the impact of the bilayer membrane in GnRHR conformation

Structural and functional characterization of Pseudomonas aeruginosa CupB chaperones

Pseudomonas aeruginosa, an important human pathogen, is estimated to be responsible for,10% of nosocomial infections worldwide. The pathogenesis of P. aeruginosa starts from its colonization in the damaged tissue or medical devices (e. g. catheters, prothesis and implanted heart valve etc.) facilitated by several extracellular adhesive factors including fimbrial pili. Several clusters containing fimbrial genes have been previously identified on the P. aeruginosa chromosome and named cup [1]. The assembly of the CupB pili is thought to be coordinated by two chaperones, CupB2 and CupB4. However, due to the lack of structural and biochemical data, their chaperone activities remain speculative. In this study, we report the 2.5 A crystal structure of P. aeruginosa CupB2. Based on the structure, we further tested the binding specificity of CupB2 and CupB4 towards CupB1 (the presumed major pilus subunit) and CupB6 (the putative adhesin) using limited trypsin digestion and strep-tactin pull-down assay. The structural and biochemical data suggest that CupB2 and CupB4 might play different, but not redundant, roles in CupB secretion. CupB2 is likely to be the chaperone of CupB1, and CupB4 could be the chaperone of CupB4:CupB5:CupB6, in which the interaction of CupB4 and CupB6 might be mediated via CupB5

Functional domains of P450 1A1 and 1A2: Molecular modeling-guided structure-function study.

Cytochromes P450 are a large family of heme-thiolate enzymes which metabolize a wide variety of both endogenous and xenobiotic compounds. Human P450 1A1 and 1A2 are two major members of the P450 1A subfamily. They share 72% sequence identity but display different specificities towards various substrates. In order to further understand the structure-function relationships of these enzymes, we have performed several molecular modeling-based studies that examined various functional domains of P450 1As. In the first part of this project, we have investigated the ligand binding domain by developing a specificity prediction model based on the correlations between MD (Molecular Dynamics)-based descriptors and the specificity towards resorufin substrates of P450 1A2 WT and its five single mutants. The application of this method to multiple mutants of P450 1A2 containing mutations at two to five of these five key positions have led to the prediction that seven out of twenty six mutants would shift specificity from P450 1A2 to 1A1. The results have been verified by site directed mutagenesis and functional analysis of the mutant P450 enzymes. Subsequently, molecular modeling methods were applied for P450-CPR (Cytochrome P450 Reductase) docking, which helped to identify the key residues at the reductase binding sites of P450 1A1 and 1A2. The effect of the substitution of Asn-145 of P450 1A2 to Lys on substrate specificity and reductase binding have been evaluated using site-directed mutagenesis and kinetics assays. Finally, we conducted a preliminary study into the membrane anchoring domain by submerging P450 1A2 into the explicit membrane in two different orientations. Energy minimization and short molecular dynamics simulations have been performed to make it ready for the subsequent rigorous molecular dynamics simulations, which are expected to provide the plausible membrane association model for P450 1A2. For the majority of the modeling studies, we applied the first generation homology models of P450 1As as the starting line. We also developed a second generation homology models of P450 1A1 and 1A2 using a new strategy to optimize the active site using ligand-enzyme interaction information. These models were based on the structures of multiple eukaryotic P450s and NMR-derived T1 relaxation data using phenacetin and acetaminophen as substrates. Taken together, various molecular modeling techniques have been successfully employed to study several functional domains of P450 1As. They helped to investigate the structural basis of the enzyme function and gave insight into their mechanism of action

Impact of an electric field on P-type ATPases

P-type ATPases are membrane proteins acting as ion pumps that drive an active transport of cations across the membrane against a concentration gradient. The required energy for the ion transport is provided by binding and hydrolysis of ATP. A reaction mechanism of ion transport and energy transduction is assumed to be common for all P-type ATPases and generally described by the Post-Albers cycle. Transient currents and charge translocation of P-type ATPases were extensively investigated by electrical measurements that apply voltage jumps to initiate the reaction cycle. In this study, we simulate an applied voltage across the membrane by an electric field and perform electrostatic calculations in order to verify the experimentally-driven hypothesis that the energy transduction mechanism is regulated by specific structural elements. Side chain conformational and ionization changes induced by the electric field are evaluated for each transmembrane helix and the selectivity in response is qualitatively analyzed for the Ca2+-ATPase as well as for structural models of the Na+/K+-ATPase. Helix M5 responds with more conformer changes as compared to the other transmembrane helices what is even more emphasized when the stalk region is included. Thus our simulations support experimental results and indicate a crucial role for the highly conserved transmembrane helix M5 in the energy transduction mechanism of P-type ATPases

Comparative genomics allowed the identification of drug targets against human fungal pathogens

<p>Abstract</p> <p>Background</p> <p>The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases.</p> <p>Results</p> <p><it>In silico </it>analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for <it>Candida albicans </it>or <it>Aspergillus fumigatus </it>and other 2 genes (<it>kre2 </it>and <it>erg6</it>) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (<it>C. albicans</it>, <it>A. fumigatus</it>, <it>Blastomyces dermatitidis</it>, <it>Paracoccidioides brasiliensis</it>, <it>Paracoccidioides lutzii, Coccidioides immitis</it>, <it>Cryptococcus neoformans </it>and <it>Histoplasma capsulatum</it>). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: <it>trr1 </it>that encodes for thioredoxin reductase, <it>rim8 </it>that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, <it>kre2 </it>that encodes for α-1,2-mannosyltransferase and <it>erg6 </it>that encodes for Δ(24)-sterol C-methyltransferase.</p> <p>Conclusions</p> <p>Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug targets. The preferred profile for fungal targets includes proteins conserved among fungi, but absent in the human genome. These characteristics potentially minimize toxic side effects exerted by pharmacological inhibition of the cellular targets. From this first step of post-genomic analysis, we obtained information relevant to future new drug development.</p