Four small puzzles that Rosetta doesn't solve

A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular "puzzles" should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special Collectio

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Multiple sclerosis or neuromyelitis optica? Re-evaluating an 18th-century illness using 21st-century software

In this paper we report the application of an extensive database of symptoms, signs, laboratory findings and illnesses, to the diagnosis of an historical figure. The medical diagnosis of Augustus d'Este (1794–1848) – widely held to be the first documented case of multiple sclerosis – is reviewed, using the detailed symptom diary, which he kept over many years, as clinical data. Some of the reported features prompted the competing claim that d'Este suffered from acute porphyria, which in turn was used in support of the hypothesis that his grandfather, King George III, also suffered from the disease. We find that multiple sclerosis is statistically the most likely diagnosis, with neuromyelitis optica a strong alternative possibility. The database did not support a diagnosis of any of the acute porphyrias

A Supramolecular Ice Growth Inhibitor

Safranine O, a synthetic dye, was found to inhibit growth of ice at millimolar concentrations with an activity comparable to that of highly evolved antifreeze glycoproteins. Safranine inhibits growth of ice crystals along the crystallographic a-axis, resulting in bipyramidal needles extended along the directions as well as and plane-specific thermal hysteresis (TH) activity. The interaction of safranine with ice is reversible, distinct from the previously reported behavior of antifreeze proteins. Spectroscopy and molecular dynamics indicate that safranine forms aggregates in aqueous solution at micromolar concentrations. Metadynamics simulations and aggregation theory suggested that as many as 30 safranine molecules were preorganized in stacks at the concentrations where ice growth inhibition was observed. The simulations and single-crystal X-ray structure of safranine revealed regularly spaced amino and methyl substituents in the aggregates, akin to the ice-binding site of antifreeze proteins. Collectively, these observations suggest an unusual link between supramolecular assemblies of small molecules and functional proteins

The involvement of long-term serial-order memory in reading development : A longitudinal study

This document is the Accepted Manuscript version of the following article: Louisa Bogaerts, Arnaud Szmalec, Marjolijn De Maeyer, Mike P.A. Page, Wouter Duyck, “The involvement of long-term serial-order memory in reading development: A longitudinal study”, Journal of Experimental Child Psychology, Vol. 145: 139-156, May 2016. This Manuscript version is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.Recent findings suggest that Hebb repetition learning-a paradigmatic example of long-term serial-order learning-is impaired in adults with dyslexia. The current study further investigated the link between serial-order learning and reading using a longitudinal developmental design. With this aim, verbal and visual Hebb repetition learning performance and reading skills were assessed in 96 Dutch-speaking children who we followed from first through second grade of primary school. We observed a positive association between order learning capacities and reading ability as well as weaker Hebb learning performance in early readers with poor reading skills even at the onset of reading instruction. Hebb learning further predicted individual differences in later (nonword) reading skills. Finally, Hebb learning was shown to explain a significant part of the variance in reading performance above and beyond phonological awareness. These findings highlight the role of serial-order memory in reading ability.Peer reviewedFinal Accepted Versio

Matrix-assisted laser desorption ionization hydrogen/deuterium exchange studies to probe peptide conformational changes

AbstractHydrogen/deuterium (H/D) exchange chemistry monitored by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry is used to study solution phase conformational changes of bradykinin, α-melanocyte stimulating hormone, and melittin as water is added to methanol-d4, acetonitrile, and isopropanol-d8 solutions. The results are interpreted in terms of a preference for the peptides to acquire more compact conformations in organic solvents as compared to the random conformations. Our interpretation is supported by circular dichroism spectra of the peptides in the same solvent systems and by previously published structural data for the peptides. These results demonstrate the utility of MALDI-TOF as a method to monitor the H/D exchange chemistry of peptides and investigations of solution-phase conformations of biomolecules

Difference Hirshfeld fingerprint plots: A tool for studying polymorphs

A new tool has been developed to help elucidate the differences in packing between different polymorphs, especially when the differences of interest are small. The technique builds upon the Hirshfeld fingerprint plot pioneered by Spackman and co-workers by subtracting the value at every point in a fingerprint plot from the value at every point in another. This is found to reveal differences that are not readily apparent to the eye. By summing the absolute values of these differences, a quantitative measure of the difference between two fingerprint plots can be obtained. The technique was applied to Ni and Cu trans-bis(2-hydroxy-5-methylphenonethanoneoximato) complexes determined at two temperatures, with the Ni complex displaying temperature-dependent polymorphism. Difference Hirshfeld fingerprint plots were also generated for calculated structures from DFT simulations that were performed on the experimental structures. These demonstrated that the simulations reproduced the fine detail of the packing

Dark Sage: Next-generation semi-analytic galaxy evolution with multidimensional structure and minimal free parameters

After more than five years of development, we present a new version of Dark Sage, a semi-analytic model (SAM) of galaxy formation that breaks the mould for models of its kind. Included among the major changes is an overhauled treatment of stellar feedback that is derived from energy conservation, operates on local scales, affects gas gradually over time rather than instantaneously, and predicts a mass-loading factor for every galaxy. Building on the model's resolved angular-momentum structure of galaxies, we now consider the heating of stellar discs, delivering predictions for disc structure both radially and vertically. We add a further dimension to stellar discs by tracking the distribution of stellar ages in each annulus. Each annulus--age bin has its own velocity dispersion and metallicity evolved in the model. This allows Dark Sage to make structural predictions for galaxies that previously only hydrodynamic simulations could. We present the model as run on the merger trees of the highest-resolution gravity-only simulation of the MillenniumTNG suite. Despite its additional complexity relative to other SAMs, Dark Sage only has three free parameters, the least of any SAM, which we calibrate exclusively against the cosmic star formation history and the $z=0$ stellar and HI mass functions using a particle-swarm optimisation method. The Dark Sage codebase, written in C and Python, is publicly available at https://github.com/arhstevens/DarkSageComment: Updates post referee's report. Get in touch for data access. 34 pages before references; 14 figures, 2 tables, 130 equation