No abstract

Resistive pulse sensors, RPS, are allowing the transport mechanism of molecules, proteins and even nanoparticles to be characterised as they traverse pores. Previous work using RPS has shown that the size, concentration and zeta potential of the analyte can be measured. Here we use tunable resistive pulse sensing (TRPS) which utilises a tunable pore to monitor the translocation times of nanoparticles with DNA modified surfaces. We start by demonstrating that the translocation times of particles can be used to infer the zeta potential of known standards and then apply the method to measure the change in zeta potential of DNA modified particles. By measuring the translocation times of DNA modified nanoparticles as a function of packing density, length, structure, and hybridisation time, we observe a clear difference in zeta potential using both mean values, and population distributions as a function of the DNA structure. We demonstrate the ability to resolve the signals for ssDNA, dsDNA, small changes in base length for nucleotides between 15-40 bases long and even the discrimination between partial and fully complementary target sequences. Such a method has potential and applications in sensors for the monitoring of nanoparticles in both medical and environmental samples.

High-pressure liquid chromatography-tandem mass spectrometry was used to obtain a protein profile of Escherichia coli strain MG1655 grown in minimal medium with glycerol as the carbon source. By using cell lysate from only 3 ؋ 10 8 cells, at least four different tryptic peptides were detected for each of 404 proteins in a short 4-h experiment. At least one peptide with a high reliability score was detected for 986 proteins. Because membrane proteins were underrepresented, a second experiment was performed with a preparation enriched in membranes. An additional 161 proteins were detected, of which from half to two-thirds were membrane proteins. Overall, 1,147 different E. coli proteins were identified, almost 4 times as many as had been identified previously by using other tools. The protein list was compared with the transcription profile obtained on Affymetrix GeneChips. Expression of 1,113 (97%) of the genes whose protein products were found was detected at the mRNA level. The arithmetic mean mRNA signal intensity for these genes was 3-fold higher than that for all 4,300 protein-coding genes of E. coli. Thus, GeneChip data confirmed the high reliability of the protein list, which contains about one-fourth of the proteins of E. coli. Detection of even those membrane proteins and proteins of undefined function that are encoded by the same operons (transcriptional units) encoding proteins on the list remained low.

Mapping the landscape of possible macromolecular polymer sequences to their fitness in performing biological functions is a challenge across the biosciences. A paradigm is the case of aptamers, nucleic acids that can be selected to bind particular target molecules. We have characterized the sequence-fitness landscape for aptamers binding allophycocyanin (APC) protein via a novel Closed Loop Aptameric Directed Evolution (CLADE) approach. In contrast to the conventional SELEX methodology, selection and mutation of aptamer sequences was carried out in silico, with explicit fitness assays for 44 131 aptamers of known sequence using DNA microarrays in vitro. We capture the landscape using a predictive machine learning model linking sequence features and function and validate this model using 5500 entirely separate test sequences, which give a very high observed versus predicted correlation of 0.87. This approach reveals a complex sequence-fitness mapping, and hypotheses for the physical basis of aptameric binding; it also enables rapid design of novel aptamers with desired binding properties. We demonstrate an extension to the approach by incorporating prior knowledge into CLADE, resulting in some of the tightest binding sequences.