Production and characterization of a low‐molecular‐weight bacteriocin from Bacillus licheniformis MKU3

Aims: Enhancing production and characterization of a low‐molecular‐weight bacteriocin from Bacillus licheniformis MKU3. Methods and Results: The culture supernatant of B. licheniformis MKU3 exhibited bacteriocin‐like activity against Gram‐positive and ‐negative bacteria and different fungi and yeast. SDS–PAGE analysis of the extracellular proteins of B. licheniformis MKU3 revealed a bacteriocin‐like protein with a molecular mass of 1·5 kDa. This bacteriocin activity was found to be stable under a pH range of 3·0–10·0 and at temperatures up to 100°C for 60 min, but inactivated by proteinase K, trypsin or pronase E. An experimental fractional factorial design for optimization of production medium resulted in a maximum activity of bacteriocin (11 000 AU ml^?1) by B. licheniformis MKU3. Conclusions: A low‐molecular‐weight bacteriocin‐like protein from B. licheniformis MKU3 exhibited a wide spectrum of antimicrobial activity against several Gram‐positive bacteria, several fungi and yeast. A 3·6‐fold increase in the production of bacteriocin was achieved using the culture medium optimized through a fractional factorial design. Significance and Impact of the Study: A bacteriocin with wide spectrum of activity against Gram‐positive bacterial pathogens, filamentous fungi and yeast suggested its potential clinical use. Statistical method facilitated optimization of cultural medium for the improved production of bacteriocin.     

Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: implications for the protein docking problem

The protein-protein interaction energy of 12 nonhomologous serine protease-inhibitor and 15 antibody-antigen complexes is calculated using a molecular mechanics formalism and dissected in terms of the main-chain vs. side-chain contribution, nonrotameric side-chain contributions, and amino acid residue type involvement in the interface interaction. There are major differences in the interactions of the two types of protein-protein complex. Protease-inhibitor complexes interact predominantly through a main-chain-main-chain mechanism while antibody-antigen complexes interact predominantly through a side-chain-side-chain or a side-chain-main-chain mechanism. However, there is no simple correlation between the main-chain-main-chain interaction energy and the percentage of main-chain surface area buried on binding. The interaction energy is equally effected by the presence of nonrotameric side-chain conformations, which constitute approximately 20% of the interaction energy. The ability to reproduce the interface interaction energy of the crystal structure if original side-chain conformations are removed from the calculation is much greater in the protease-inhibitor complexes than the antibody-antigen complexes. The success of a rotameric model for protein-protein docking appears dependent on the extent of the main-chain-main-chain contribution to binding. Analysis of (1) residue type and (2) residue pair interactions at the interface show that antibody-antigen interactions are very restricted with over 70% of the antibody energy attributable to just six residue types (Tyr > Asp > Asn > Ser > Glu > Trp) in agreement with previous studies on residue propensity. However, it is found here that 50% of the antigen energy is attributable to just four residue types (Arg = Lys > Asn > Asp). On average just 12 residue pair interactions (6%) contribute over 40% of the favorable interaction energy in the antibody-antigen complexes, with charge-charge and charge/polar-tyrosine interactions being prominent. In contrast protease inhibitors use a diverse set of residue types and residue pair interactions.     

Status and habitat preferences of Uganda cheetahs: an attempt to predict carnivore occurrence based on vegetation structure

In this paper we examine whether the occurrence of cheetahs (Acinonyx jubatus) in Uganda can be predicted from habitat characteristics extracted from a vegetation map. We first established the status of the cheetah in Uganda through field-interviews that Gros conducted in 1990. Cheetahs occurred almost exclusively in the Karamoja region where we estimated 53–310 individuals. Based on 216 sightings, the average number of adults in all-adults sightings was 1.65 + SD 0.95 and the average number of cubs in family groups 2.5 + SD 1.65. Compared to Graham and Parker's 1965 East African survey, average adult group size was slightly smaller in 1990 and large family groups were rarer. Comparison with Gros 1990 survey showed considerably lower cub-to-adult ratio and percent of observations with cubs in Uganda than in Kenya. A Geographic Information System (GIS) analysis of vegetation structure in areas where cheetahs were observed and in those where none were reported suggested that cheetahs might favor habitats with 25–50% woody cover and grasses of medium height (50–100 cm). A discriminant analysis correctly classified 72.1% of used habitats and 70.4% of no-report habitats. A logistic regression analysis improved the correct allocation of used habitats by 2.2%. Either the discriminant function or the logistic regression, which require only four easily obtainable vegetation characteristics, may help to pinpoint suitable cheetah habitats for conservation purposes. Our approach could be adapted for analyzing habitat suitability for other species of carnivores.     

Particle swarm optimization

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems.     

Multiple site-directed mutagenesis of more than 10 sites simultaneously and in a single round

Site-directed mutagenesis is a powerful tool to explore the structure-function relationship of proteins, but most traditional methods rely on the mutation of only one site at a time and efficiencies drop drastically when more than three sites are targeted simultaneously. Many applications in functional proteomics and genetic engineering, including codon optimization for heterologous expression, generation of cysteine-less proteins, and alanine-scanning mutagenesis, would greatly benefit from a multiple-site mutagenesis method with high efficiency. Here we describe the development of a simple and rapid method for site-directed mutagenesis of more than 10 sites simultaneously with up to 100% efficiency. The method uses two terminal tailed primers with a unique 25-nucleotide tail each that are simultaneously annealed to template DNA together with the set of mutagenic primers in between. Following synthesis of the mutant strand by primer extension and ligation with T4 DNA polymerase and ligase, the unique mutant strand-specific tails of the terminal primers are used as anchors to specifically amplify the mutant strand by high-fidelity polymerase chain reaction. We have employed this novel method to mutate simultaneously all 9 and 11 CUG leucine codons of the Hyg and Neo resistance genes, respectively, to the Candida albicans-friendly UUG leucine codon at 100% efficiency.     

Bayesian point estimation of quantitative trait loci

In this article, we consider the problem of the estimation of quantitative trait loci (QTL), those chromosomal regions at which genetic information affecting some quantitative trait is encoded. Generally the number of such encoding sites is unknown, and associations between neutral molecular marker genotypes and observed trait phenotypes are sought to locate them. We consider a Bayesian model for simple experimental designs, and discuss the existing approaches to inference for this problem. In particular, we focus on locating positions of the best candidate markers segregating for the trait, a situation which is of primary interest in comparative mapping. We introduce a loss function for estimating both the number of QTL and their location, and we illustrate its application via simulated and real data.     

Protein disulfide isomerase, but not binding protein, overexpression enhances secretion of a non-disulfide-bonded protein in yeast

In eukaryotes, secretory proteins are folded and assembled in the endoplasmic reticulum (ER). Many heterologous proteins are retained in the ER due to suboptimal folding conditions. We previously reported that heterologous secretion of Pyrococcus furiosus beta-glucosidase in Saccharomyces cerevisiae resulted in the accumulation of a large fraction of inactive beta-glucosidase in the ER. In this work, we determine the effect of introducing additional genes of ER-resident yeast proteins, Kar2p (binding protein [BiP]) and protein disulfide isomerase (PDI), on relieving this bottleneck. Single-copy expression of BiP and PDI worked synergistically to improve secretion by reverse similar 60%. In an effort to optimize BiP and PDI interactions, we created a library of beta-glucosidase expression strains that incorporated four combinations of constitutively or inducibly-expressed BiP and PDI genes integrated to random gene copynumbers in the yeast chromosome. Approximately 15% of the transformants screened had secretion level improvements higher than that seen with single BiP/PDI gene overexpression, and the highest secreting strain had threefold higher beta-glucosidase levels than the control. Nineteen of the improved strains were re-examined for beta-glucosidase secretion as well as BiP and PDI levels. Within the improved transformants BiP and PDI levels ranged sevenfold and tenfold over the control, respectively. Interestingly, increasing BiP levels decreased beta-glucosidase secretion, whereas increasing PDI levels increased beta-glucosidase secretion. The action of PDI was unexpected because beta-glucosidase is not a disulfide-bonded protein. We suggest that PDI may be acting in a chaperone-like capacity or possibly creating mixed disulfides with the beta-glucosidase's lone cysteine residue during the folding and assembly process.     

Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization

The advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. In this work, the computational OptKnock framework is introduced for suggesting gene deletion strategies leading to the overproduction of chemicals or biochemicals in E. coli. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) must be accompanied, due to stoichiometry, by the production of a desired product. Computational results for gene deletions for succinate, lactate, and 1,3-propanediol (PDO) production are in good agreement with mutant strains published in the literature. While some of the suggested deletion strategies are straightforward and involve eliminating competing reaction pathways, many others suggest complex and nonintuitive mechanisms of compensating for the removed functionalities. Finally, the OptKnock procedure, by coupling biomass formation with chemical production, hints at a growth selection/adaptation system for indirectly evolving overproducing mutants.     

Multicriteria optimization of biochemical systems by linear programming: application to production of ethanol by Saccharomyces cerevisiae

In this study we present a method for simultaneous optimization of several metabolic responses of biochemical pathways. The method, based on the use of the power law formalism to obtain a linear system in logarithmic coordinates, is applied to ethanol production by Saccharomyces cerevisiae. Starting from an experimentally based kinetic model, we translated it to its power law equivalent. With this new model representation, we then applied the multiobjective optimization method. Our intent was to maximize ethanol production and minimize each of the internal metabolite concentrations. To ensure cell viability, all optimizations were carried out under imposed constraints. The different solutions obtained, which correspond to alternative patterns of enzyme overexpression, were implemented in the original model. We discovered few discrepancies between the S-system-optimized steady state and the corresponding optimized state in the original kinetic model, thus demonstrating the suitability of the S-system representation as the basis for the optimization procedure. In all optimized solutions, the ATP level reached its maximum and any increase in its activity positively affected the optimization process. This work illustrates that in any optimization study no single criteria is of general application being the multiobjective and constrained task the proper way to address it. It is concluded that the proposed multiobjective method can serve to carry out, in a single study, the general pattern of behavior of a given metabolic system with regard to its control and optimization.