Pseudomonas fluorescens biofilms subjected to phage phiIBB-PF7A

Background: Pseudomonas fluorescens is an important food spoilage organism, usually found in the form of biofilms. Bacterial biofilms are inherently resistant to a variety of antimicrobial agents, therefore alternative methods to biofilm control, such as bacteriophages (phages) have been suggested. Phage behavior on biofilms is still poorly investigated and needs further understanding. Here we describe the application of phage ϕIBB-PF7, a newly isolated phage, to control P. fluorescens biofilms. The biofilms were formed under static or dynamic conditions and with or without renewal of medium. Results: Conditions for biofilm formation influenced the feature of the biofilm and the morphology of P. fluorescens. Biomass removal due to phage activity varied between 63 and 91% depending on the biofilm age and the conditions under which the biofilm had been formed and phages applied. Removal of the biofilm by phage treatment was faster in younger biofilms, but the same number of surviving cells was detected in all tested biofilms, after only 4 h of treatment, even in older biofilms. Under static conditions, a 3 log higher number of phage progeny remained either inside the biofilm matrix or attached to the substratum surface than under dynamic conditions, pointing to the importance of experimental conditions for the efficacy of phage entrapment into the biofilm. Conclusion: Phage ϕIBB-PF7A is highly efficient in removing P. fluorescens biofilms within a short time interval. The conditions of biofilm formation and applied during phage infection are critical for the efficacy of the sanitation process. The integration of phages into the biofilm matrix and their entrapment to the surface may be further beneficial factors when phage treatment is considered alone or in addition to chemical biocides in industrial environments where P. fluorescens causes serious spoilage.Fundação para a Ciência e a Tecnologia (FCT

Pseudomonas fluorescens biofilms subjected to phage phiIBB-PF7A

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Engineering proteinase K using machine learning and synthetic genes

BACKGROUND: Altering a protein's function by changing its sequence allows natural proteins to be converted into useful molecular tools. Current protein engineering methods are limited by a lack of high throughput physical or computational tests that can accurately predict protein activity under conditions relevant to its final application. Here we describe a new synthetic biology approach to protein engineering that avoids these limitations by combining high throughput gene synthesis with machine learning-based design algorithms. RESULTS: We selected 24 amino acid substitutions to make in proteinase K from alignments of homologous sequences. We then designed and synthesized 59 specific proteinase K variants containing different combinations of the selected substitutions. The 59 variants were tested for their ability to hydrolyze a tetrapeptide substrate after the enzyme was first heated to 68°C for 5 minutes. Sequence and activity data was analyzed using machine learning algorithms. This analysis was used to design a new set of variants predicted to have increased activity over the training set, that were then synthesized and tested. By performing two cycles of machine learning analysis and variant design we obtained 20-fold improved proteinase K variants while only testing a total of 95 variant enzymes. CONCLUSION: The number of protein variants that must be tested to obtain significant functional improvements determines the type of tests that can be performed. Protein engineers wishing to modify the property of a protein to shrink tumours or catalyze chemical reactions under industrial conditions have until now been forced to accept high throughput surrogate screens to measure protein properties that they hope will correlate with the functionalities that they intend to modify. By reducing the number of variants that must be tested to fewer than 100, machine learning algorithms make it possible to use more complex and expensive tests so that only protein properties that are directly relevant to the desired application need to be measured. Protein design algorithms that only require the testing of a small number of variants represent a significant step towards a generic, resource-optimized protein engineering process