The ansamycin antibiotic, rifamycin SV, inhibits BCL6 transcriptional repression and forms a complex with the BCL6-BTB/POZ domain

BCL6 is a transcriptional repressor that is over-expressed due to chromosomal translocations, or other abnormalities, in ~40% of diffuse large B-cell lymphoma. BCL6 interacts with co-repressor, SMRT, and this is essential for its role in lymphomas. Peptide or small molecule inhibitors, which prevent the association of SMRT with BCL6, inhibit transcriptional repression and cause apoptosis of lymphoma cells in vitro and in vivo. In order to discover compounds, which have the potential to be developed into BCL6 inhibitors, we screened a natural product library. The ansamycin antibiotic, rifamycin SV, inhibited BCL6 transcriptional repression and NMR spectroscopy confirmed a direct interaction between rifamycin SV and BCL6. To further determine the characteristics of compounds binding to BCL6-POZ we analyzed four other members of this family and showed that rifabutin, bound most strongly. An X-ray crystal structure of the rifabutin-BCL6 complex revealed that rifabutin occupies a partly non-polar pocket making interactions with tyrosine58, asparagine21 and arginine24 of the BCL6-POZ domain. Importantly these residues are also important for the interaction of BLC6 with SMRT. This work demonstrates a unique approach to developing a structure activity relationship for a compound that will form the basis of a therapeutically useful BCL6 inhibitor

Temporal Convolution in Spiking Neural Networks: a Bio-mimetic Paradigm

Abstract Recent spectacular advances in Artificial Intelligence (AI), in large, be attributed to developments in Deep Learning (DL). In essence, DL is not a new concept. In many respects, DL shares characteristics of “traditional” types of Neural Network (NN). The main distinguishing feature is that it uses many more layers in order to learn increasingly complex features. Each layer convolutes into the previous by simplifying and applying a function upon a subsection of that layer. Deep Learning’s fantastic success can be attributed to dedicated researchers experimenting with many different groundbreaking techniques, but also some of its triumph can also be attributed to fortune. It was the right technique at the right time. To function effectively, DL mainly requires two things: (a) vast amounts of training data and (b) a very specific type of computational capacity. These two respective requirements have been amply met with the growth of the internet and the rapid development of GPUs. As such DL is an almost perfect fit for today’s technologies. However, DL is only a very rough approximation of how the brain works. More recently, Spiking Neural Networks (SNNs) have tried to simulate biological phenomena in a more realistic way. In SNNs information is transmitted as discreet spikes of data rather than a continuous weight or a differentiable activation function. In practical terms this means that far more nuanced interactions can occur between neurons and that the network can run far more efficiently (e.g. in terms of calculations needed and therefore overall power requirements). Nevertheless, the big problem with SNNs is that unlike DL it does not “fit” well with existing technologies. Worst still is that no one has yet come up with definitive way to make SNNs function at a “deep” level. The difficulty is that in essence "deep" and "spiking" refer to fundamentally different characteristics of a neural network: "spiking" focuses on the activation of individual neurons, whereas "deep" concerns itself to the network architecture itself [1]. However, these two methods are in fact not contradictory, but have so far been developed in isolation from each other due to the prevailing technology driving each technique and the fundamental conceptual distance between each of the two biological paradigms. If advances in AI are to continue at the present rate that new technologies are going to be developed and the contradictory aspects of DL and SNN are going to have to be reconciled. Very recently, there have been a handful of attempts to amalgamate DL and SNN in a variety of ways [2]-one of the most exciting being the creation of a specific hierarchical learning paradigm in Recurrent SNN (RSNNs) called e-prop [3]. However, this paper posits that this has been made problematic because a fundamental agent in the way the biological brain functions has been missing from each paradigm, and that if this is included in a new model then the union between DL and RSNN can be made in a more harmonious manner. The missing piece to the jigsaw, in fact, is the glial cell and the unacknowledged function it plays in neural processing. In this context, this paper examines how DL and SNN can be combined, and how glial dynamics cannot only address outstanding issues with the existing individual paradigms - for example the “weight transport” problem - but also act as the “glue” – e.g. pun intended - between these two paradigms. This idea has direct parallel with the idea of convolution in DL but has the added dimension of time. It is important not only where events happen but also when events occur in this new paradigm. The synergy between these two powerful paradigms give hints at the direction and potential of what could be an important part of the next wave of development in AI

Bio-Benchmarking of Electronic Nose Sensors

BACKGROUND:Electronic noses, E-Noses, are instruments designed to reproduce the performance of animal noses or antennae but generally they cannot match the discriminating power of the biological original and have, therefore, been of limited utility. The manner in which odorant space is sampled is a critical factor in the performance of all noses but so far it has been described in detail only for the fly antenna. METHODOLOGY:Here we describe how a set of metal oxide (MOx) E-Nose sensors, which is the most commonly used type, samples odorant space and compare it with what is known about fly odorant receptors (ORs). PRINCIPAL FINDINGS:Compared with a fly's odorant receptors, MOx sensors from an electronic nose are on average more narrowly tuned but much more highly correlated with each other. A set of insect ORs can therefore sample broader regions of odorant space independently and redundantly than an equivalent number of MOx sensors. The comparison also highlights some important questions about the molecular nature of fly ORs. CONCLUSIONS:The comparative approach generates practical learnings that may be taken up by solid-state physicists or engineers in designing new solid-state electronic nose sensors. It also potentially deepens our understanding of the performance of the biological system

The Location and Nature of General Anesthetic Binding Sites on the Active Conformation of Firefly Luciferase; A Time Resolved Photolabeling Study

Firefly luciferase is one of the few soluble proteins that is acted upon by a wide variety of general anesthetics and alcohols; they inhibit the ATP–driven production of light. We have used time–resolved photolabeling to locate the binding sites of alcohols during the initial light output, some 200 ms after adding ATP. The photolabel 3-azioctanol inhibited the initial light output with an IC50 of 200 µM, close to its general anesthetic potency. Photoincorporation of [3H]3-azioctanol into luciferase was saturable but weak. It was enhanced 200 ms after adding ATP but was negligible minutes later. Sequencing of tryptic digests by HPLC–MSMS revealed a similar conformation–dependence for photoincorporation of 3-azioctanol into Glu-313, a residue that lines the bottom of a deep cleft (vestibule) whose outer end binds luciferin. An aromatic diazirine analog of benzyl alcohol with broader side chain reactivity reported two sites. First, it photolabeled two residues in the vestibule, Ser-286 and Ile-288, both of which are implicated with Glu-313 in the conformation change accompanying activation. Second, it photolabeled two residues that contact luciferin, Ser-316 and Ser-349. Thus, time resolved photolabeling supports two mechanisms of action. First, an allosteric one, in which anesthetics bind in the vestibule displacing water molecules that are thought to be involved in light output. Second, a competitive one, in which anesthetics bind isosterically with luciferin. This work provides structural evidence that supports the competitive and allosteric actions previously characterized by kinetic studies

Structure of an Engineered β-Lactamase Maltose Binding Protein Fusion Protein: Insights into Heterotropic Allosteric Regulation

Engineering novel allostery into existing proteins is a challenging endeavor to obtain novel sensors, therapeutic proteins, or modulate metabolic and cellular processes. The RG13 protein achieves such allostery by inserting a circularly permuted TEM-1 β-lactamase gene into the maltose binding protein (MBP). RG13 is positively regulated by maltose yet is, serendipitously, inhibited by Zn2+ at low µM concentration. To probe the structure and allostery of RG13, we crystallized RG13 in the presence of mM Zn2+ concentration and determined its structure. The structure reveals that the MBP and TEM-1 domains are in close proximity connected via two linkers and a zinc ion bridging both domains. By bridging both TEM-1 and MBP, Zn2+ acts to “twist tie” the linkers thereby partially dislodging a linker between the two domains from its original catalytically productive position in TEM-1. This linker 1 contains residues normally part of the TEM-1 active site including the critical β3 and β4 strands important for activity. Mutagenesis of residues comprising the crystallographically observed Zn2+ site only slightly affected Zn2+ inhibition 2- to 4-fold. Combined with previous mutagenesis results we therefore hypothesize the presence of two or more inter-domain mutually exclusive inhibitory Zn2+ sites. Mutagenesis and molecular modeling of an intact TEM-1 domain near MBP within the RG13 framework indicated a close surface proximity of the two domains with maltose switching being critically dependent on MBP linker anchoring residues and linker length. Structural analysis indicated that the linker attachment sites on MBP are at a site that, upon maltose binding, harbors both the largest local Cα distance changes and displays surface curvature changes, from concave to relatively flat becoming thus less sterically intrusive. Maltose activation and zinc inhibition of RG13 are hypothesized to have opposite effects on productive relaxation of the TEM-1 β3 linker region via steric and/or linker juxtapositioning mechanisms

Loss of Pluripotency in Human Embryonic Stem Cells Directly Correlates with an Increase in Nuclear Zinc

The pluripotency of human embryonic stem cells (hESCs) is important to investigations of early development and to cell replacement therapy, but the mechanism behind pluripotency is incompletely understood. Zinc has been shown to play a key role in differentiation of non-pluripotent cell types, but here its role in hESCs is directly examined. By mapping the distribution of metals in hESCs at high resolution by x-ray fluorescence microprobe (XFM) and by analyzing subcellular metal content, we have found evidence that loss of pluripotency is directly correlated with an increase in nuclear zinc. Zinc elevation not only redefines our understanding of the mechanisms that support pluripotency, but also may act as a biomarker and an intervention point for stem cell differentiation

Minimal Functional Sites Allow a Classification of Zinc Sites in Proteins

Zinc is indispensable to all forms of life as it is an essential component of many different proteins involved in a wide range of biological processes. Not differently from other metals, zinc in proteins can play different roles that depend on the features of the metal-binding site. In this work, we describe zinc sites in proteins with known structure by means of three-dimensional templates that can be automatically extracted from PDB files and consist of the protein structure around the metal, including the zinc ligands and the residues in close spatial proximity to the ligands. This definition is devised to intrinsically capture the features of the local protein environment that can affect metal function, and corresponds to what we call a minimal functional site (MFS). We used MFSs to classify all zinc sites whose structures are available in the PDB and combined this classification with functional annotation as available in the literature. We classified 77% of zinc sites into ten clusters, each grouping zinc sites with structures that are highly similar, and an additional 16% into seven pseudo-clusters, each grouping zinc sites with structures that are only broadly similar. Sites where zinc plays a structural role are predominant in eight clusters and in two pseudo-clusters, while sites where zinc plays a catalytic role are predominant in two clusters and in five pseudo-clusters. We also analyzed the amino acid composition of the coordination sphere of zinc as a function of its role in the protein, highlighting trends and exceptions. In a period when the number of known zinc proteins is expected to grow further with the increasing awareness of the cellular mechanisms of zinc homeostasis, this classification represents a valuable basis for structure-function studies of zinc proteins, with broad applications in biochemistry, molecular pharmacology and de novo protein design

Genetically-Directed, Cell Type-Specific Sparse Labeling for the Analysis of Neuronal Morphology

Background: In mammals, genetically-directed cell labeling technologies have not yet been applied to the morphologic analysis of neurons with very large and complex arbors, an application that requires extremely sparse labeling and that is only rendered practical by limiting the labeled population to one or a few predetermined neuronal subtypes. Methods and Findings: In the present study we have addressed this application by using CreER technology to noninvasively label very small numbers of neurons so that their morphologies can be fully visualized. Four lines of IRES-CreER knock-in mice were constructed to permit labeling selectively in cholinergic or catecholaminergic neurons [choline acetyltransferase (ChAT)-IRES-CreER or tyrosine hydroxylase (TH)-IRES-CreER], predominantly in projection neurons [neurofilament light chain (NFL)-IRES-CreER], or broadly in neurons and some glia [vesicle-associated membrane protein2 (VAMP2)-IRES-CreER]. When crossed to the Z/AP reporter and exposed to 4-hydroxytamoxifen in the early postnatal period, the number of neurons expressing the human placental alkaline phosphatase reporter can be reproducibly lowered to fewer than 50 per brain. Sparse Cre-mediated recombination in ChAT-IRES-CreER;Z/AP mice shows the full axonal and dendritic arbors of individual forebrain cholinergic neurons, the first time that the complete morphologies of these very large neurons have been revealed in any species. Conclusions: Sparse genetically-directed, cell type-specific neuronal labeling with IRES-creER lines should prove useful fo