Function2Form Bridge-Toward synthetic protein holistic performance prediction.

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Microbiome analysis as a platform R&D tool for parasitic nematode disease management.

The relationship between bacterial communities and their host is being extensively investigated for the potential to improve the host's health. Little is known about the interplay between the microbiota of parasites and the health of the infected host. Using nematode co-infection of lambs as a proof-of-concept model, the aim of this study was to characterise the microbiomes of nematodes and that of their host, enabling identification of candidate nematode-specific microbiota member(s) that could be exploited as drug development tools or for targeted therapy. Deep sequencing techniques were used to elucidate the microbiomes of different life stages of two parasitic nematodes of ruminants, Haemonchus contortus and Teladorsagia circumcincta, as well as that of the co-infected ovine hosts, pre- and post infection. Bioinformatic analyses demonstrated significant differences between the composition of the nematode and ovine microbiomes. The two nematode species also differed significantly. The data indicated a shift in the constitution of the larval nematode microbiome after exposure to the ovine microbiome, and in the ovine intestinal microbial community over time as a result of helminth co-infection. Several bacterial species were identified in nematodes that were absent from their surrounding abomasal environment, the most significant of which included Escherichia coli/Shigella. The ability to purposefully infect nematode species with engineered E. coli was demonstrated in vitro, validating the concept of using this bacterium as a nematode-specific drug development tool and/or drug delivery vehicle. To our knowledge, this is the first description of the concept of exploiting a parasite's microbiome for drug development and treatment purposes.

Assessment of HPV infection among female university students in Honduras via Roche linear array.

OBJECTIVE: To determine the prevalence of specific types of human papillomavirus (HPV), and the association with possible risk factors, among female university students at university in Honduras. METHODS: In a cross-sectional study, cervical samples from 400 women aged 18-35years were tested using a Roche HPV linear array to differentiate 37 genotypes of HPV. Associations with risk factors were assessed. RESULTS: Of the 400 participants, 393 completed the study. HPV DNA was detected in 45% of these women, of whom 73% were infected with high-risk types of HPV and 46% had multiple infections. Overall, 36 HPV genotypes were identified, of which HPV types 16, 51, 84, 66, and 39 were the most common. There was a marked decrease in the prevalence of multiple and high-risk infections with age. The factors that were independently associated with risk of being infected were related to sexual behavior and smoking habits. CONCLUSION: The study showed that genital HPV infection is common among sexually active women at university in Honduras. In addition, the Roche linear array was shown to be a valuable tool for HPV genotyping, which will be useful for monitoring the future effectiveness of an HPV vaccine in the population.