Application of Nucleotide-Based Kinetic Modeling Approaches to Predict Antibiotic Resistance Gene Degradation during UV- and Chlorine-Based Wastewater Disinfection Processes: From Bench- to Full-Scale.

This study investigated antibiotic resistance gene (ARG) degradation kinetics in wastewaters during bench- and full-scale treatment with UV light and chlorine─with the latter maintained as free available chlorine (FAC) in low-ammonia wastewater and converted into monochloramine (NH2Cl) in high-ammonia wastewater. Twenty-three 142-1509 bp segments (i.e., amplicons) of seven ARGs (blt, mecA, vanA, tet(A), ampC, blaNDM, blaKPC) and the 16S rRNA gene from antibiotic resistant bacteria (ARB) strains Bacillus subtilis, Staphylococcus aureus, Enterococcus faecium, Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae were monitored as disinfection targets by qPCR. Rate constants for ARG and 16S rRNA gene amplicon degradation by UV, FAC, and NH2Cl were measured in phosphate buffer and used to expand and validate several recently developed approaches to predict DNA segment degradation rate constants based solely on their nucleotide contents, which were then applied to model ARG degradation during bench-scale treatment in buffer and wastewater matrixes. Kinetics of extracellular and intracellular ARG degradation by UV and FAC were well predicted up to ∼1-2-log10 elimination, although with decreasing accuracy at higher levels for intracellular genes, while NH2Cl yielded minimal degradation under all conditions (agreeing with predictions). ARB inactivation kinetics varied substantially across strains, with intracellular gene degradation lagging cell inactivation in each case. ARG degradation levels observed during full-scale disinfection at two wastewater treatment facilities were consistent with bench-scale measurements and predictions, where UV provided ∼1-log10 ARG degradation, and chlorination of high-ammonia wastewater (dominated by NH2Cl) yielded minimal ARG degradation.

Improvement of a potential anthrax therapeutic by computational protein design.

Past anthrax attacks in the United States have highlighted the need for improved measures against bioweapons. The virulence of anthrax stems from the shielding properties of the Bacillus anthracis poly-γ-d-glutamic acid capsule. In the presence of excess CapD, a B. anthracis γ-glutamyl transpeptidase, the protective capsule is degraded, and the immune system can successfully combat infection. Although CapD shows promise as a next generation protein therapeutic against anthrax, improvements in production, stability, and therapeutic formulation are needed. In this study, we addressed several of these problems through computational protein engineering techniques. We show that circular permutation of CapD improved production properties and dramatically increased kinetic thermostability. At 45 °C, CapD was completely inactive after 5 min, but circularly permuted CapD remained almost entirely active after 30 min. In addition, we identify an amino acid substitution that dramatically decreased transpeptidation activity but not hydrolysis. Subsequently, we show that this mutant had a diminished capsule degradation activity, suggesting that CapD catalyzes capsule degradation through a transpeptidation reaction with endogenous amino acids and peptides in serum rather than hydrolysis.

Computational design of an α-gliadin peptidase.

The ability to rationally modify enzymes to perform novel chemical transformations is essential for the rapid production of next-generation protein therapeutics. Here we describe the use of chemical principles to identify a naturally occurring acid-active peptidase, and the subsequent use of computational protein design tools to reengineer its specificity toward immunogenic elements found in gluten that are the proposed cause of celiac disease. The engineered enzyme exhibits a k(cat)/K(M) of 568 M(-1) s(-1), representing a 116-fold greater proteolytic activity for a model gluten tetrapeptide than the native template enzyme, as well as an over 800-fold switch in substrate specificity toward immunogenic portions of gluten peptides. The computationally engineered enzyme is resistant to proteolysis by digestive proteases and degrades over 95% of an immunogenic peptide implicated in celiac disease in under an hour. Thus, through identification of a natural enzyme with the pre-existing qualities relevant to an ultimate goal and redefinition of its substrate specificity using computational modeling, we were able to generate an enzyme with potential as a therapeutic for celiac disease.