Antimicrobial peptides (AmPs) are small proteins that are used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and could form the basis for a new class of therapeutic agents. Here we report the rational design of new AmPs that show limited homology to naturally occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs: we treated the amino-acid sequences of natural AmPs as a formal language and built a set of regular grammars to describe this language. We used this set of grammars to create new, unnatural AmP sequences. Our peptides conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space.
Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.
The impact of gene patents on downstream research and innovation are unknown, in part because of a lack of empirical data on the extent and nature of gene patenting. In this Policy Forum, the authors show that 20% of human gene DNA sequences are patented and that some genes are patented as many as 20 times. Unsurprisingly, genes associated with health and disease are more patented than the genome at large. The intellectual property rights for some genes can become highly fragmented between many owners, which suggests that downstream innovators may face considerable costs to gain access to gene-oriented technologies.
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