Complex Thermodynamic Behavior of Single-Stranded Nucleic Acid Adsorption to Graphene Surfaces.

In just over a decade since its discovery, research on graphene has exploded due to a number of potential applications in electronics, materials, and medicine. In its water-soluble form of graphene oxide, the material has shown promise as a biosensor due to its preferential absorption of single-stranded polynucleotides and fluorescence quenching properties. The rational design of these biosensors, however, requires an improved understanding of the binding thermodynamics and ultimately a predictive model of sequence-specific binding. Toward these goals, here we directly measured the binding of nucleosides and oligonucleotides to graphene oxide nanoparticles using isothermal titration calorimetry and used the results to develop molecular models of graphene-nucleic acid interactions. We found individual nucleosides binding KD values lie in the submillimolar range with binding order of rG < rA < rC < dT < rU, while 5mer and 15mer oligonucleotides had markedly higher binding affinities in the range of micromolar and submicromolar KD values, respectively. The molecular models developed here are calibrated to quantitatively reproduce the above-mentioned experimental results. For oligonucleotides, our model predicts complex binding features such as double-stacked bases and a decrease in the fraction of graphene stacked bases with increasing oligonucleotide length until plateauing beyond ∼10-15 nucleotides. These experimental and computational results set the platform for informed design of graphene-based biosensors, further increasing their potential and application.

Performance of the Quidel Sofia rapid influenza diagnostic test during the 2012-2013 and 2013-2014 influenza seasons.

The Quidel Sofia Influenza A+B Fluorescent Immunoassay was used to test nasal swab specimens from patients with influenza-like illness at US-Mexico border-area clinics in the 2012-2013 and 2013-2014 influenza seasons. Compared with real-time reverse transcription polymerase chain reaction, the overall sensitivities and specificities were 83% and 81%, and 62% and 93%, respectively.

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing.

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.