Structure-Guided Recombination Creates an Artificial Family of Cytochromes P450

Creating artificial protein families affords new opportunities to explore the determinants of structure and biological function free from many of the constraints of natural selection. We have created an artificial family comprising ~3,000 P450 heme proteins that correctly fold and incorporate a heme cofactor by recombining three cytochromes P450 at seven crossover locations chosen to minimize structural disruption. Members of this protein family differ from any known sequence at an average of 72 and by as many as 109 amino acids. Most (>73%) of the properly folded chimeric P450 heme proteins are catalytically active peroxygenases; some are more thermostable than the parent proteins. A multiple sequence alignment of 955 chimeras, including both folded and not, is a valuable resource for sequence-structure-function studies. Logistic regression analysis of the multiple sequence alignment identifies key structural contributions to cytochrome P450 heme incorporation and peroxygenase activity and suggests possible structural differences between parents CYP102A1 and CYP102A2

Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes

The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2,3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM {\beta}-lactamase (blaTEM) varied greatly among 15 different {\beta}-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles blaTEM-50 and blaTEM-85, each of which differs from its ancestor blaTEM-1 by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV

Extensive Genomic Diversity among Bovine-Adapted Staphylococcus aureus: Evidence for a Genomic Rearrangement within CC97

peer-reviewedStaphylococcus aureus is an important pathogen associated with both human and veterinary disease and is a common cause of bovine mastitis. Genomic heterogeneity exists between S. aureus strains and has been implicated in the adaptation of specific strains to colonise particular mammalian hosts. Knowledge of the factors required for host specificity and virulence is important for understanding the pathogenesis and management of S. aureus mastitis. In this study, a panel of mastitis-associated S. aureus isolates (n = 126) was tested for resistance to antibiotics commonly used to treat mastitis. Over half of the isolates (52%) demonstrated resistance to penicillin and ampicillin but all were susceptible to the other antibiotics tested. S. aureus isolates were further examined for their clonal diversity by Multi-Locus Sequence Typing (MLST). In total, 18 different sequence types (STs) were identified and eBURST analysis demonstrated that the majority of isolates grouped into clonal complexes CC97, CC151 or sequence type (ST) 136. Analysis of the role of recombination events in determining S. aureus population structure determined that ST diversification through nucleotide substitutions were more likely to be due to recombination compared to point mutation, with regions of the genome possibly acting as recombination hotspots. DNA microarray analysis revealed a large number of differences amongst S. aureus STs in their variable genome content, including genes associated with capsule and biofilm formation and adhesion factors. Finally, evidence for a genomic arrangement was observed within isolates from CC97 with the ST71-like subgroup showing evidence of an IS431 insertion element having replaced approximately 30 kb of DNA including the ica operon and histidine biosynthesis genes, resulting in histidine auxotrophy. This genomic rearrangement may be responsible for the diversification of ST71 into an emerging bovine adapted subgroup

Developing Efficient Metaheuristics for Communication Network Problems by using Problem-specific Knowledge

Metaheuristics, such as evolutionary algorithms or simulated annealing, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult optimization problems. To show high performance, metaheuristics need to be adapted to the properties of the problem at hand. This paper illustrates how efficient metaheuristics can be developed for communication network problems by utilizing problem-specific knowledge for the design of a high-quality problem representation. The minimum communication spanning tree (MCST) problem finds a communication spanning tree that connects all nodes and satisfies their communication requirements for a minimum total cost. An investigation into the properties of the problem reveals that optimum solutions are similar to the minimum spanning tree (MST). Consequently, a problem-specific representation, the link biased (LB) encoding, is developed, which represents trees as a list of floats. The LB encoding makes use of the knowledge that optimum solutions are similar to the MST, and encodes trees that are similar to the MST with a higher probability. Experimental results for different types of metaheuristics show that metaheuristics using the LB-encoding efficiently solve existing MCST problem instances from the literature, as well as randomly generated MCST problems of different sizes and types

Calibration of neural networks using genetic algorithms, with application to optimal path planning

Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface

Multi-Objective Evolutionary Neural Network to Predict Graduation Success at the United States Military Academy

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify students as: graduates, late graduates, or non-graduates. Results of the hybrid method show higher mean classification rates (88%) than the current methodology (80%) with a potential savings of $130M. Additionally, the proposed method is more efficient in that a less complex neural network topology is identified by the algorithm