On the interaction between human IQGAP1 and actin

DM thanks the School of Biological Sciences, Queen’s University, Belfast for a summer studentship and EH thanks the Department of Employment and Learning, Northern Ireland for a postgraduate studentship. The work was funded in part by grants from the BBSRC (BB/D000394/1 To DJT) and by the Wellcome Trust [grant number GR06281AIA] which funded the purchase of the QStar XL mass spectrometer at the BBSRC Mass Spectrometry and Proteomics Facility, University of St Andrews and funded SLS.IQGAPs are eukaryotic proteins which integrate signals from various sources and pass these on the cytoskeleton. Understanding how they do this requires information on the interfaces between the proteins. Here, it is shown that the calponin homology domain of human IQGAP1 (CHD1) can be crosslinked with α-actin. The stoichiometry of the interaction was 1:1. A molecular model was built of the complex and associated bioinformatics analyses predicted that the interaction is likely to involve an electrostatic interaction between Lys-240 of α-actin and Glu-30 of CHD1. These residues are predicted to be accessible and are not involved in many intra-protein interactions; they are thus available for interaction with binding partners. They are both located in regions of the proteins which are predicted to be flexible and disordered; interactions between signalling molecules often involve flexible, disordered regions. The predicted binding region in CHD1 is well conserved in many eukaryotic IQGAP-like proteins. In some cases (e.g Dictyostelium discoideum and Saccharomyces cerevisiae) protein sequence conservation is weak, but molecular modelling reveals that a region of charged, polar residues in a flexible N-terminus is structurally well conserved. Therefore we conclude that the calponin homology domains of IQGAP1-like proteins interact initially through the electrostatic interaction identified here and that there may be subsequent conformational changes to form the final complex.PostprintPeer reviewe

Modular approach to select bacteriophages targeting Pseudomonas aeruginosa for their application to children suffering with cystic fibrosis

This review discusses the potential application of bacterial viruses (phage therapy) towards the eradication of antibiotic resistant Pseudomonas aeruginosa in children with cystic fibrosis (CF). In this regard, several potential relationships between bacteria and their bacteriophages are considered. The most important aspect that must be addressed with respect to phage therapy of bacterial infections in the lungs of CF patients is in ensuring the continuity of treatment in light of the continual occurrence of resistant bacteria. This depends on the ability to rapidly select phages exhibiting an enhanced spectrum of lytic activity among several well-studied phage groups of proven safety. We propose a modular based approach, utilizing both mono-species and hetero-species phage mixtures. With an approach involving the visual recognition of characteristics exhibited by phages of well-studied phage groups on lawns of the standard P. aeruginosa PAO1 strain, the simple and rapid enhancement of the lytic spectrum of cocktails is permitted, allowing the development of tailored preparations for patients capable of circumventing problems associated with phage resistant bacterial mutants

Patient and Strain Characteristics Associated With Clostridium difficile Transmission and Adverse Outcomes

Background: No study has used whole-genome sequencing (WGS) to investigate risk factors for Clostridium difficile (CD) transmission between cases, or assessed the impact of recent acquisition on patient outcome. Methods: This 20 month retrospective cohort study included consecutive cytotoxin-positive diarrheal samples, which underwent culture, ribotyping, and WGS (Illumina). Sequenced isolates were compared using single nucleotide variants (SNVs). Independent predictors of acquisition from another case, onward transmission, 120-day recurrence, and 30-day mortality were identified using logistic regression with backwards elimination. Results: Of 660 CD cases, 640 (97%) were sequenced, of which 567 (89%) shared a ribotype with a prior case, but only 227 (35%) were ≤2 SNVs from a prior case, supporting recent acquisition. Plausible ( .1). Conclusions: Greater transmission of certain lineages suggests CD may have different reservoirs and modes of transmission. Acquiring CD from a recent case is associated with poorer clinical outcomes. Clinical characteristics associated with increased healthcare-associated CD transmission could be used to target preventative interventions

Insights into the structural dynamics of the bacteriophage T7 DNA polymerase and its complexes

The T7 DNA polymerase is dependent on the host protein thioredoxin (trx) for its processivity and fidelity. Using all-atom molecular dynamics, we demonstrate the specific interactions between trx and the T7 polymerase, and show that trx docking to its binding domain on the polymerase results in a significant level of stability and exposes a series of basic residues within the domain that interact with the phosphodiester backbone of the DNA template. We also characterize the nature of interactions between the T7 DNA polymerase and its DNA template. We show that the trx-binding domain acts as an intrinsic clamp, constraining the DNA via a two-step hinge motion, and characterize the interactions necessary for this to occur. Together, these insights provide a significantly improved understanding of the interactions responsible for highly processive DNA replication by T7 polymerase

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases

Abstract Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents