Modeling of arylamide helix mimetics in the p53 peptide binding site of hDM2 suggests parallel and anti-parallel conformations are both stable.

The design of novel α-helix mimetic inhibitors of protein-protein interactions is of interest to pharmaceuticals and chemical genetics researchers as these inhibitors provide a chemical scaffold presenting side chains in the same geometry as an α-helix. This conformational arrangement allows the design of high affinity inhibitors mimicking known peptide sequences binding specific protein substrates. We show that GAFF and AutoDock potentials do not properly capture the conformational preferences of α-helix mimetics based on arylamide oligomers and identify alternate parameters matching solution NMR data and suitable for molecular dynamics simulation of arylamide compounds. Results from both docking and molecular dynamics simulations are consistent with the arylamides binding in the p53 peptide binding pocket. Simulations of arylamides in the p53 binding pocket of hDM2 are consistent with binding, exhibiting similar structural dynamics in the pocket as simulations of known hDM2 binders Nutlin-2 and a benzodiazepinedione compound. Arylamide conformations converge towards the same region of the binding pocket on the 20 ns time scale, and most, though not all dihedrals in the binding pocket are well sampled on this timescale. We show that there are two putative classes of binding modes for arylamide compounds supported equally by the modeling evidence. In the first, the arylamide compound lies parallel to the observed p53 helix. In the second class, not previously identified or proposed, the arylamide compound lies anti-parallel to the p53 helix

Novel hypophysiotropic AgRP2 neurons and pineal cells revealed by BAC transgenesis in zebrafish

The neuropeptide agouti-related protein (AgRP) is expressed in the arcuate nucleus of the mammalian hypothalamus and plays a key role in regulating food consumption and energy homeostasis. Fish express two agrp genes in the brain: agrp1, considered functionally homologous with the mammalian AgRP, and agrp2. The role of agrp2 and its relationship to agrp1 are not fully understood. Utilizing BAC transgenesis, we generated transgenic zebrafish in which agrp1- and agrp2-expressing cells can be visualized and manipulated. By characterizing these transgenic lines, we showed that agrp1-expressing neurons are located in the ventral periventricular hypothalamus (the equivalent of the mammalian arcuate nucleus), projecting throughout the hypothalamus and towards the preoptic area. The agrp2 gene was expressed in the pineal gland in a previously uncharacterized subgroup of cells. Additionally, agrp2 was expressed in a small group of neurons in the preoptic area that project directly towards the pituitary and form an interface with the pituitary vasculature, suggesting that preoptic AgRP2 neurons are hypophysiotropic. We showed that direct synaptic connection can exist between AgRP1 and AgRP2 neurons in the hypothalamus, suggesting communication and coordination between AgRP1 and AgRP2 neurons and, therefore, probably also between the processes they regulate

Opposite environmental and genetic influences on body size in North American Drosophila pseudoobscura.

Journal ArticleResearch Support, Non-U.S. Gov'tBACKGROUND: Populations of a species often differ in key traits. However, it is rarely known whether these differences are associated with genetic variation and evolved differences between populations, or are instead simply a plastic response to environmental differences experienced by the populations. Here we examine the interplay of plasticity and direct genetic control by investigating temperature-size relationships in populations of Drosophila pseudoobscura from North America. We used 27 isolines from three populations and exposed them to four temperature regimes (16°C, 20°C, 23°C, 26°C) to examine environmental, genetic and genotype-by-environment sources of variance in wing size. RESULTS: By far the largest contribution to variation in wing size came from rearing temperature, with the largest flies emerging from the coolest temperatures. However, we also found a genetic signature that was counter to this pattern as flies originating from the northern, cooler population were consistently smaller than conspecifics from more southern, warmer populations when reared under the same laboratory conditions. CONCLUSIONS: We conclude that local selection on body size appears to be acting counter to the environmental effect of temperature. We find no evidence that local adaptation in phenotypic plasticity can explain this result, and suggest indirect selection on traits closely linked with body size, or patterns of chromosome inversion may instead be driving this relationship.NERCBBSR

How large should whales be?

The evolution and distribution of species body sizes for terrestrial mammals is well-explained by a macroevolutionary tradeoff between short-term selective advantages and long-term extinction risks from increased species body size, unfolding above the 2g minimum size induced by thermoregulation in air. Here, we consider whether this same tradeoff, formalized as a constrained convection-reaction-diffusion system, can also explain the sizes of fully aquatic mammals, which have not previously been considered. By replacing the terrestrial minimum with a pelagic one, at roughly 7000g, the terrestrial mammal tradeoff model accurately predicts, with no tunable parameters, the observed body masses of all extant cetacean species, including the 175,000,000g Blue Whale. This strong agreement between theory and data suggests that a universal macroevolutionary tradeoff governs body size evolution for all mammals, regardless of their habitat. The dramatic sizes of cetaceans can thus be attributed mainly to the increased convective heat loss is water, which shifts the species size distribution upward and pushes its right tail into ranges inaccessible to terrestrial mammals. Under this macroevolutionary tradeoff, the largest expected species occurs where the rate at which smaller-bodied species move up into large-bodied niches approximately equals the rate at which extinction removes them.Comment: 7 pages, 3 figures, 2 data table

Stapled Peptides as HIF‐1α/p300 Inhibitors: Helicity Enhancement in the Bound State Increases Inhibitory Potency

Protein–protein interactions (PPIs) control virtually all cellular processes and have thus emerged as potential targets for development of molecular therapeutics. Peptide‐based inhibitors of PPIs are attractive given that they offer recognition potency and selectivity features that are ideal for function, yet, they do not predominantly populate the bioactive conformation, frequently suffer from poor cellular uptake and are easily degraded, for example, by proteases. The constraint of peptides in a bioactive conformation has emerged as a promising strategy to mitigate against these liabilities. In this work, using peptides derived from hypoxia‐inducible factor 1 (HIF‐1α) together with dibromomaleimide stapling, we identify constrained peptide inhibitors of the HIF‐1α/p300 interaction that are more potent than their unconstrained sequences. Contrary to expectation, the increased potency does not correlate with an increased population of an α‐helical conformation in the unbound state as demonstrated by experimental circular dichroism analysis. Rather, the ability of the peptide to adopt a bioactive α‐helical conformation in the p300 bound state is better supported in the constrained variant as demonstrated by molecular dynamics simulations and circular dichroism difference spectra

Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists\u27 estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard

Topoisomer Differentiation of Molecular Knots by FTICR MS: Lessons from Class II Lasso Peptides

Lasso peptides constitute a class of bioactive peptides sharing a knotted structure where the C-terminal tail of the peptide is threaded through and trapped within an N-terminalmacrolactamring. The structural characterization of lasso structures and differentiation from their unthreaded topoisomers is not trivial and generally requires the use of complementary biochemical and spectroscopic methods. Here we investigated two antimicrobial peptides belonging to the class II lasso peptide family and their corresponding unthreaded topoisomers: microcin J25 (MccJ25), which is known to yield two-peptide product ions specific of the lasso structure under collisioninduced dissociation (CID), and capistruin, for which CID does not permit to unambiguously assign the lasso structure. The two pairs of topoisomers were analyzed by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) upon CID, infrared multiple photon dissociation (IRMPD), and electron capture dissociation (ECD). CID and ECDspectra clearly permitted to differentiate MccJ25 from its non-lasso topoisomer MccJ25-Icm, while for capistruin, only ECD was informative and showed different extent of hydrogen migration (formation of c\bullet/z from c/z\bullet) for the threaded and unthreaded topoisomers. The ECD spectra of the triply-charged MccJ25 and MccJ25-lcm showed a series of radical b-type product ions {\eth}b0In{\TH}. We proposed that these ions are specific of cyclic-branched peptides and result from a dual c/z\bullet and y/b dissociation, in the ring and in the tail, respectively. This work shows the potentiality of ECD for structural characterization of peptide topoisomers, as well as the effect of conformation on hydrogen migration subsequent to electron capture

Electron-Spin Excitation Coupling in an Electron Doped Copper Oxide Superconductor

High-temperature (high-Tc) superconductivity in the copper oxides arises from electron or hole doping of their antiferromagnetic (AF) insulating parent compounds. The evolution of the AF phase with doping and its spatial coexistence with superconductivity are governed by the nature of charge and spin correlations and provide clues to the mechanism of high-Tc superconductivity. Here we use a combined neutron scattering and scanning tunneling spectroscopy (STS) to study the Tc evolution of electron-doped superconducting Pr0.88LaCe0.12CuO4-delta obtained through the oxygen annealing process. We find that spin excitations detected by neutron scattering have two distinct modes that evolve with Tc in a remarkably similar fashion to the electron tunneling modes in STS. These results demonstrate that antiferromagnetism and superconductivity compete locally and coexist spatially on nanometer length scales, and the dominant electron-boson coupling at low energies originates from the electron-spin excitations.Comment: 30 pages, 12 figures, supplementary information include

A gentle introduction to the functional renormalization group: the Kondo effect in quantum dots

The functional renormalization group provides an efficient description of the interplay and competition of correlations on different energy scales in interacting Fermi systems. An exact hierarchy of flow equations yields the gradual evolution from a microscopic model Hamiltonian to the effective action as a function of a continuously decreasing energy cutoff. Practical implementations rely on suitable truncations of the hierarchy, which capture nonuniversal properties at higher energy scales in addition to the universal low-energy asymptotics. As a specific example we study transport properties through a single-level quantum dot coupled to Fermi liquid leads. In particular, we focus on the temperature T=0 gate voltage dependence of the linear conductance. A comparison with exact results shows that the functional renormalization group approach captures the broad resonance plateau as well as the emergence of the Kondo scale. It can be easily extended to more complex setups of quantum dots.Comment: contribution to Les Houches proceedings 2006, Springer styl