Codominance and toxins: A path to drugs of nearly unlimited selectivity

The effectiveness of drugs is often limited by their insufficient selectivity. I propose designs of therapeutic agents that address this problem. The key feature of these reagents, termed comtoxins (codominance-mediated toxins), is their ability to utilize codominance, a property characteristic of many signals in proteins, including degradation signals (degrons) and nuclear localization signals. A comtoxin designed to kill cells that express intracellular proteins P1 and P2 but to spare cells that lack P1 and/or P2 is a multidomain fusion containing a cytotoxic domain and two degrons placed within or near two domains P1* and P2* that bind, respectively, to pi and P2. In a cell containing both P1 and P2, these proteins would bind to the P1* and P2* domains of the comtoxin and sterically mask the nearby (appropriately positioned) degrons, resulting in a long-lived and therefore toxic drug. By contrast, in a cell lacking P1 and/or P2, at least one of the comtoxin's degrons would be active (unobstructed), yielding a short-lived and therefore nontoxic drug. A comtoxin containing both a degron and a nuclear localization signal can be designed to kill exclusively cells that contain P1 but lack P2. Analogous strategies yield comtoxins sensitive to the presence (or absence) of more than two proteins in a cell. Also considered is a class of comtoxins in which a toxic domain is split by a flexible insert containing binding sites for the target proteins. The potentially unlimited, combinatorial selectivity of comtoxins may help solve the problem of side effects that bedevils present-day therapies, for even nonselective delivery of a comtoxin would not affect cells whose protein "signatures" differ from the targeted one

Augmented generation of protein fragments during wakefulness as the molecular cause of sleep: A hypothesis

Despite extensive understanding of sleep regulation, the molecular-level cause and function of sleep are unknown. I suggest that they originate in individual neurons and stem from increased production of protein fragments during wakefulness. These fragments are transient parts of protein complexes in which the fragments were generated. Neuronal Ca^(2+) fluxes are higher during wakefulness than during sleep. Subunits of transmembrane channels and other proteins are cleaved by Ca^(2+)-activated calpains and by other nonprocessive proteases, including caspases and secretases. In the proposed concept, termed the fragment generation (FG) hypothesis, sleep is a state during which the production of fragments is decreased (owing to lower Ca^(2+) transients) while fragment-destroying pathways are upregulated. These changes facilitate the elimination of fragments and the remodeling of protein complexes in which the fragments resided. The FG hypothesis posits that a proteolytic cleavage, which produces two fragments, can have both deleterious effects and fitness-increasing functions. This (previously not considered) dichotomy can explain both the conservation of cleavage sites in proteins and the evolutionary persistence of sleep, because sleep would counteract deleterious aspects of protein fragments. The FG hypothesis leads to new explanations of sleep phenomena, including a longer sleep after sleep deprivation. Studies in the 1970s showed that ethanol-induced sleep in mice can be strikingly prolonged by intracerebroventricular injections of either Ca^(2+) alone or Ca^(2+) and its ionophore. These results, which were never interpreted in connection to protein fragments or the function of sleep, may be accounted for by the FG hypothesis about molecular causation of sleep

Strange Bedfellows: Quantum Mechanics and Data Mining

Last year, in 2008, I gave a talk titled {\it Quantum Calisthenics}. This year I am going to tell you about how the work I described then has spun off into a most unlikely direction. What I am going to talk about is how one maps the problem of finding clusters in a given data set into a problem in quantum mechanics. I will then use the tricks I described to let quantum evolution lets the clusters come together on their own.Comment: 11 pages, 7 figures, Invited Talk at Light Cone 200