Search

Botulinum neurotoxins (BoNTs) produced by the anaerobic bacterium Clostridium botulinum are the most potent biological substances known to mankind. BoNTs are the agents responsible for botulism, a rare condition affecting the neuromuscular junction and causing a spectrum of diseases ranging from mild cranial nerve palsies to acute respiratory failure and death. BoNTs are a potential biowarfare threat and a public health hazard, since outbreaks of foodborne botulism are caused by the ingestion of preformed BoNTs in food. Currently, mathematical models relating to the hazards associated with C. botulinum , which are largely empirical, make major contributions to botulinum risk assessment. Evaluated using statistical techniques, these models simulate the response of the bacterium to environmental conditions. Though empirical models have been successfully incorporated into risk assessments to support food safety decision making, this process includes significant uncertainties so that relevant decision making is frequently conservative and inflexible. Progression involves encoding into the models cellular processes at a molecular level, especially the details of the genetic and molecular machinery. This addition drives the connection between biological mechanisms and botulism risk assessment and hazard management strategies. This review brings together elements currently described in the literature that will be useful in building quantitative models of C. botulinum neurotoxin production. Subsequently, it outlines how the established form of modeling could be extended to include these new elements. Ultimately, this can offer further contributions to risk assessments to support food safety decision making.

The mucus layer covering the gastrointestinal tract is the first point of contact of the intestinal microbiota with the host. Cell surface macromolecules are critical for adherence of commensal bacteria to mucus but structural information is scarce. Here we report the first molecular and structural characterization of a novel cell-surface protein, Lar_0958 from Lactobacillus reuteri JCM 1112(T) , mediating adhesion of L. reuteri human strains to mucus. Lar_0958 is a modular protein of 133 kDa containing six repeat domains, an N-terminal signal sequence and a C-terminal anchoring motif (LPXTG). Lar_0958 homologues are expressed on the cell-surface of L. reuteri human strains, as shown by flow-cytometry and immunogold microscopy. Adhesion of human L. reuteri strains to mucus in vitro was significantly reduced in the presence of an anti-Lar_0958 antibody and Lar_0958 contribution to adhesion was further confirmed using a L. reuteri ATCC PTA 6475 lar_0958 KO mutant (6475-KO). The X-ray crystal structure of a single Lar_0958 repeat, determined at 1.5 A resolution, revealed a divergent immunoglobulin (Ig)-like β-sandwich fold, sharing structural homology with the Ig-like inter-repeat domain of internalins of the food borne pathogen Listeria monocytogenes. These findings provide unique structural insights into cell-surface protein repeats involved in adhesion of Gram-positive bacteria to the intestine.

Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information beyond group level but integrate many component processes, such as signalling, membrane permeability and metabolic activity. In this paper we present a scheme for modelling neurotoxin production in C. botulinum Group I type A1, based on the integration of diverse information coming from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Toxins are released as large protein complexes and are associated with non-toxic components. Here, we systematically review and integrate those regulatory elements previously described in the literature for C. botulinum Group I type A1 into a population dynamics model, to build the very first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities in developing new strategies that could be used to prevent botulism; and potentially contribute to improved methods for the production of toxin that is used for therapeutics., Author Summary Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information. In this paper we present modelling based on the integration of diverse information from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Here, we integrate these regulatory elements into a combined model of population dynamics and gene regulation to build the first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities that could be used to prevent botulism, and potentially contribute to improved methods for the production of toxin used for therapeutics.

With the completion of the sequencing of the Arabidopsis genome and the recent advances in proteomic technology, the identification of proteins from highly complex mixtures is now possible. Rather than using gel electrophoresis and peptide mass fingerprinting, we have used multidimensional protein identification technology (MudPIT) to analyse the "tightly-bound" proteome for purified cell walls from Arabidopsis cell suspension cultures. Using bioinformatics for the prediction of signal peptides for targeting to the secretory pathway and for the absence of ER retention signal, 89 proteins were selected as potential extracellular proteins. Only 33% of these were identified in previous proteomic analyses of Arabidopsis cell walls. A functional classification revealed that a large proportion of the proteins were enzymes, notably carbohydrate active enzymes, peroxidases and proteases. Comparison of all the published proteomic analyses for the Arabidopsis cell wall identified 268 non-redundant genes encoding wall proteins. Sixty of these (22%) were derived from our analysis of tightly-bound wall proteins.

The coiled coil is arguably the simplest protein-structure motif and probably the most ubiquitous facilitator of protein-protein interactions. Coiled coils comprise two or more alpha-helices that wind around each other to form "supercoils". The hallmark of most coiled coils is a regular sequence pattern known as the heptad repeat. Despite this apparent simplicity and relatedness at the sequence level, coiled coils display a considerable degree of structural diversity: the helices may be arranged parallel or anti-parallel and may form a variety of oligomer states. To aid studies of coiled coils, we developed SOCKET, a computer program to identify these motifs automatically in protein structures. We used SOCKET to gather a set of unambiguous coiled-coil structures from the RCSB Protein Data Bank. Rather than searching for sequence features, the algorithm recognises the characteristic knobs-into-holes side-chain packing of coiled coils; this proved to be straightforward to implement and was able to distinguish coiled coils from the great majority of helix-helix packing arrangements observed in globular domains. SOCKET unambiguously defines coiled-coil helix boundaries, oligomerisation states and helix orientations, and also assigns heptad registers. Structures retrieved from the Protein Data Bank included parallel and anti-parallel variants of two, three and four-stranded coiled coils, one example of a parallel pentamer and a small number of structures that extend the classical description of a coiled coil. We anticipate that our structural database and the associated sequence data that we have gathered will be of use in identifying principles for coiled-coil assembly, prediction and design. To illustrate this we give examples of sequence and structural analyses of the structures that are possible using the new data bases, and we present amino acid profiles for the heptad repeats of different motifs.

The coiled coil is a ubiquitous protein-folding motif. It generally is accepted that coiled coils are characterized by sequence patterns known as heptad repeats. Such patterns direct the formation and assembly of amphipathic alpha-helices, the hydrophobic faces of which interface in a specific manner first proposed by Crick and termed "knobs-into-holes packing". We developed software, SOCKET, to recognize this packing in protein structures. As expected, in a trawl of the protein data bank, we found examples of canonical coiled coils with a single contiguous heptad repeat. In addition, we identified structures with multiple, overlapping heptad repeats. This observation extends Crick's original postulate: Multiple, offset heptad repeats help explain assemblies with more than two helices. Indeed, we have found that the sequence offset of the multiple heptad repeats is related to the coiled-coil oligomer state. Here we focus on one particular sequence motif in which two heptad repeats are offset by two residues. This offset sets up two hydrophobic faces separated by approximately 150 degrees -160 degrees around the alpha-helix. In turn, two different combinations of these faces are possible. Either similar or opposite faces can interface, which leads to open or closed multihelix assemblies. Accordingly, we refer to these two forms as alpha-sheets and alpha-cylinders. We illustrate these structures with our own predictions and by reference to natural variants on these designs that have recently come to light.

The interaction of water molecules with apolar amino acids is an important aspect of the hydrophobic effect and hence of protein folding. Our distributed multiple electrostatic model for water interacting with phenylalanine dipeptides shows that minimum energy sites exist above the aromatic ring such that a solvent molecule can interact with the pi electrons, but only when this site is not blocked by main-chain atoms or disturbed by main-chain polar atoms. This is consistent with the experimental evidence of others that water can hydrogen bond to aromatic pi electrons. In contrast, our analysis of solvent interactions with phenylalanine residues based on 48 high-resolution, well-refined protein structures shows that the dominant interaction of solvent molecules is with the edge of the ring and not with the pi elections. As the faces of phenylalanine rings tend to be buried, and solvent interactions with neighbouring polar atoms are more favourable, the interaction of water molecules with the faces of aromatic pi rings appears not to occur frequently in proteins.

We have analysed the distribution of solvent sites within 5.0 A of the apolar side-chains alanine, valine, leucine, isoleucine and phenylalanine based on experimental data from 24 high-resolution protein structures. Clustering of solvent molecules into specific regions can be seen superimposed on a broad background of sites. The non-random nature of these distributions is confirmed by quantitative analysis of the solvent sites according to a spherical polar (r, theta and phi) co-ordinate system with the apolar atom of interest at the centre. One of the general features of these solvent sites is that they peak at around 4.0 A from an apolar protein carbon atom. Preferences in orientation (theta and phi) are also seen in the solvent distributions especially around the alanine CB atom and the phenylalanine ring. Most (around 75%) of the solvent sites around apolar groups are also within hydrogen bonding distance of protein (main chain) polar groups which leads to a distribution dependent on the local secondary structure. The remaining 25% of solvent sites are referred to as "non-polar" water molecules and their crystallographic temperature factors are higher than average by between 15% to 28%. For alanine and phenylalanine there are enough data to show that water molecules not within hydrogen bonding distance of protein polar atoms also cluster into specific regions. However, the main conclusion appears to be that the hydrophobic hydration in protein crystals is correlated with hydration of polar groups and thus depends on the local environment as well as on the stereochemistry of the apolar atoms.