Kyle Jensen

A linguistic model for the rational design of antimicrobial peptides

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.

A generic motif discovery algorithm for sequential data

MOTIVATION Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems. RESULTS Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemodas output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures. AVAILABILITY Gemoda is freely available at http://web.mit.edu/bamel/gemoda

Governing Knowledge in the Scientific Community: Exploring the Role of Retractions in Biomedicine

Although the validity of knowledge is critical to scientific progress, substantial concerns exist regarding the governance of knowledge production. While as or more important to the knowledge economy as defects are in the manufacturing economy, mechanisms to identify and signal “defective” or false knowledge are poorly understood. In this paper, we investigate one such institution – the system of scientific retractions. By analyzing the universe of peer-reviewed scientific articles retracted from the biomedical literature between 1972-2006 and comparing with a matched control sample, we identify the correlates, timing, and causal impact of scientific retractions, thus providing insight into the workings of a distributed, peer-based system for the governance of validity in scientific knowledge. Our findings suggest that attention is a key predictor of retraction – retracted articles arise most frequently among highly-cited articles. The retraction system is expeditious in uncovering knowledge that is ever determined to be false (the mean time to retraction is less than two years) and democratic (retraction is not systematically affected by author prominence). Lastly, retraction causes an immediate, severe, and long-lived decline in future citations. Conditional on the obvious limitation that we cannot measure the absolute amount of false science in circulation, these results support the view that distributed governance systems can be designed to relatively swiftly to uncover false knowledge and to mitigate the costs that false knowledge for future generations of producers.

Identifying Functionally Important Mutations from Phenotypically Diverse Sequence Data

ABSTRACT Here we present a simple statistical method to determine the phenotypic contribution of a single mutation from libraries of mutants with diverse phenotypes in which each mutant contains a multitude of mutations. The central premise of this method is that, given M phenotypic classes, mutations that do not affect the phenotype should partition among the M classes according to a multinomial distribution. Deviations from this distribution are indicative of a link between specific mutations and phenotypes. We suggest that this method will aid the engineering of functional nucleic acids, proteins, and other biomolecules by uncovering target sites for rational mutagenesis. As a proof of the principle, we show how the method can be used to deduce the individual effects of mutations in a set of 69 PL-λ promoter variants. Each of these promoters was generated by error-prone PCR and incorporated numerous mutations. The activity of the promoters was assayed using flow cytometry to measure the fluorescence of a green fluorescent protein reporter gene. Our analysis of the sequences of these mutants revealed seven positions having a statistically significant correlation with promoter activity. Using site-directed mutagenesis, we constructed point mutations for several sites, both statistically significant and insignificant, and combinations of these sites. Our results show that the statistical method correctly elucidated the phenotypic manifestations of these mutations. We suggest that this method may be useful for expediting directed evolution experiments by allowing both desired and undesired mutations to be identified and incorporated between rounds of mutagenesis.

Gender differences in obtaining and maintaining patent rights

An examination of the prosecution and maintenance histories of approximately 2.7 million US patent applications indicates that women have less favorable outcomes than men.