Principals in Programming Languages: Technical Results

This is the companion technical report for ``Principals in Programming Languages'' [20]. See that document for a more readable version of these results. In this paper, we describe two variants of the simply typed $\lambda$-calculus extended with a notion of {\em principal}. The results are languages in which intuitive statements like ``the client must call $\mathtt{open}$ to obtain a file handle'' can be phrased and proven formally. The first language is a two-agent calculus with references and recursive types, while the second language explores the possibility of multiple agents with varying amounts of type information. We use these calculi to give syntactic proofs of some type abstraction results that traditionally require semantic arguments

Structure-Based Prediction of G‑Protein-Coupled Receptor Ligand Function: A β‑Adrenoceptor Case Study

The spectacular advances in G-protein-coupled receptor (GPCR) structure determination have opened up new possibilities for structure-based GPCR ligand discovery. The structure-based prediction of whether a ligand stimulates (full/partial agonist), blocks (antagonist), or reduces (inverse agonist) GPCR signaling activity is, however, still challenging. A total of 31 β₁ (β₁R) and β₂ (β₂R) adrenoceptor crystal structures, including antagonist, inverse agonist, and partial/full agonist-bound structures, allowed us to explore the possibilities and limitations of structure-based prediction of GPCR ligand function. We used all unique protein–ligand interaction fingerprints (IFPs) derived from all ligand-bound β-adrenergic crystal structure monomers to post-process the docking poses of known β₁R/β₂R partial/full agonists, antagonists/inverse agonists, and physicochemically similar decoys in each of the β₁R/β₂R structures. The systematic analysis of these 1920 unique IFP–structure combinations offered new insights into the relative impact of protein conformation and IFP scoring on selective virtual screening (VS) for ligands with a specific functional effect. Our studies show that ligands with the same function can be efficiently classified on the basis of their protein–ligand interaction profile. Small differences between the receptor conformation (used for docking) and reference IFP (used for scoring of the docking poses) determine, however, the enrichment of specific ligand types in VS hit lists. Interestingly, the selective enrichment of partial/full agonists can be achieved by using agonist IFPs to post-process docking poses in agonist-bound as well as antagonist-bound structures. We have identified optimal structure–IFP combinations for the identification and discrimination of antagonists/inverse agonist and partial/full agonists, and defined a predicted IFP for the small full agonist norepinephrine that gave the highest retrieval rate of agonists over antagonists for <i>all</i> structures (with an enrichment factor of 46 for agonists and 8 for antagonists on average at a 1% false-positive rate). This β-adrenoceptor case study provides new insights into the opportunities for selective structure-based discovery of GPCR ligands with a desired function and emphasizes the importance of IFPs in scoring docking poses

Kripo PDB Dec 2015

<p>KRIPO stands for Key Representation of Interaction in POckets.</p> <p>All fragments form all proteins-ligand complexes in PDB compared with all.<br> Data set contains PDB entries that where available at 23 December 2015.</p> <p>* Kripo.*.sqlite - Fragments sqlite database<br> * Distance matrix is too big to ship with VM so use http://3d-e-chem.vu-compmedchem.nl/kripodb webservice url to query.<br> * kripo_fingerprint_2015_*.fp.gz - Fragment fingerprints, see https://github.com/3D-e-Chem/kripodb/blob/master/README.md#create-distance-matrix-from-text-files for instructions how to convert to a distance matrix.</p> <p>Dataset was generated using http://dx.doi.org/10.5281/zenodo.53891</p> <p> </p

ORF74 recruits β-arrestin1 and β-arrestin2 in response to human chemokines.

<p>HEK293T cells co-expressing ORF74-Rluc8 and β-arrestin1-eYFP (A, C) or β-arrestin2-eYFP (B, D) were stimulated with increasing concentrations of CXCL1 (open squares), CXCL8 (filled squares) or CXCL10 (open circles) (A, B) or co-stimulated with CXCL1 and CXCL10 (C, D). β-arrestin recruitment to the receptor was measured as an increase in BRET ratio (BRETr). Data are shown as fold over basal and represent the mean of pooled data from at least three independent experiments each performed in triplicate. Error bars indicate SEM values. Significant differences between vehicle and chemokine-stimulation were determined by one-way ANOVA followed by a Bonferroni test (**** p ≤ 0.0001). NS = not significant.</p

ORF74 trafficking is β-arrestin-dependent.

<p>HEK293T cells were transiently transfected with ORF74-Rluc8 and Venus-K-Ras (plasma membrane marker) (A, C) or Venus-Rab5a (early endosome marker) (B, D) in combination with control (Contr) or β-arrestin1/2 (βarr1/2) siRNA. (A, B) Downregulation of β-arrestin1/2 levels was determined by immunoblotting. STAT3 levels were determined as loading control. (C, D) Cells were stimulated with CXCL1 for indicated time and BRET was measured. Data are shown as the mean of pooled data from three independent experiments each performed in triplicate. Data is presented as fold over vehicle-stimulated cells (dotted line) and error bars indicate SEM values. Statistical differences between the area under the curve of cells treated with control or β-arrestin1/2 siRNA (baseline = 1) were determined by a Student t test (** p ≤ 0.01).</p

Characterization and β-arrestin recruitment to ORF74-ST/A.

<p>(A) Schematic representation of the C-tail of ORF74, starting at the conserved VPxxY-motif in TM7. Serine and threonine residues mutated to alanine in ORF74-ST/A are shown in bold brown. The location of TM7 (delineated) and helix 8 (marked red) are based on the CCR5 crystal structure (PDB-code 4MBS) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124486#pone.0124486.ref035" target="_blank">35</a>]. (B-F) HEK293T cells were transiently transfected with WT-ORF74 (WT) (B-E) or ORF74-ST/A (ST/A) (B-F) or empty vector DNA (mock-transfected) (B, E). (B) Relative receptor expression at the cell surface was determined by ELISA. Binding of ¹²⁵I-CXCL10 (C) or ¹²⁵I-CXCL8 (D) to intact HEK293T cells was measured in the presence of increasing concentrations unlabeled homologous chemokines. Constitutive (E) or chemokine-induced (F) activation of PLC was determined by measuring InsP accumulation. (G) HEK293T cells expressing ORF74-Rluc8 (WT) or ORF74-ST/A-Rluc8 (ST/A) in combination with β-arrestin1-eYFP (βarr1) or β-arrestin2-eYFP (βarr2) were vehicle-stimulated (white bars) or stimulated with 300 nM CXCL1 (black bars) before measurement of BRET. Data are presented as fold over mock-transfected cells (dotted line) (B, E), percentage of specific binding (C, D) or fold over basal (F, G). All data are represented as the mean of pooled data from at least three independent experiments each performed in triplicate and error bars indicate SEM values. Statistical differences of cell surface expression (B) or constitutive PLC activation (E) between WT-ORF74 and ORF74-ST/A or between vehicle- and corresponding CXCL1-treated cells (G) were determined by a Student t test (**** p ≤ 0.0001, ** p ≤ 0.01, * p ≤ 0.05). NS = not significant.</p

Serines and threonines at the distal end of the C-tail are essential for β-arrestin recruitment.

<p>(A) Schematic representation of the C-tail of ORF74, starting at the conserved VPxxY-motif in TM7. Serine and threonine residues mutated to alanine are shown in bold brown and clustered to indicate the different ORF74-ST/A mutants (ST/A1, ST/A2 and ST/A3). The location of TM7 (delineated) and helix 8 (marked red) are based on the CCR5 crystal structure (PDB-code 4MBS) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124486#pone.0124486.ref035" target="_blank">35</a>]. (B) HEK293T cells were transiently transfected with ORF74-Rluc8 (WT), ORF74-ST/A1-Rluc8 (ST/A1), ORF74-ST/A2-Rluc8 (ST/A2) or ORF74-ST/A3-Rluc8 (ST/A3) or empty vector DNA (mock-transfected) and receptor cell surface expression was determined by ELISA. (C-F) HEK293T cells expressing ORF74-Rluc8 (WT) or one of the Rluc8-tagged ORF74-ST/A mutants in combination with β-arrestin1-eYFP (C, E) or β-arrestin2-eYFP (D, F) were treated with increasing concentrations CXCL1 (C, D) or were vehicle-stimulated (white bars) or stimulated with 300 nM CXCL1 (black bars) (E, F) before measurement of BRET. Data are shown as the mean of pooled data from three independent experiments each performed in triplicate. Data is presented as fold over mock-transfected cells (dotted line) (B) or fold over basal (C-F) and error bars indicate SEM values. Statistical differences between ORF74 WT and mutant cell surface expression (B) or difference between vehicle- and corresponding CXCL1-treated cells (E, F) were determined by one-way ANOVA followed by a Bonferroni test (B) or a Student t test (E, F), respectively (**** p ≤ 0.0001, ** p ≤ 0.01). NS = not significant.</p

ORF74 internalizes and traffics via early, recycling and late endosomes.

<p>HEK293T cells were transiently transfected with ORF74-Rluc8 (WT) (A-D) or ORF74-ST/A2-Rluc8 (ST/A2) (E-H) in combination with Venus-K-Ras (plasma membrane marker) (A, E), Venus-Rab5a (early endosome marker) (B, F), Venus-Rab7a (late endosome/lysosome marker) (C, G) or Venus-Rab11 (recycling endosome marker) (D, H) and stimulated with CXCL1, CXCL8 or CXCL10 for indicated time and BRET was measured. Data are shown as the mean of pooled data from three independent experiments each performed in triplicate. Data is presented as fold over vehicle-stimulated cells (dotted line) and error bars indicate SEM values. Statistical differences between the area under the curve of vehicle- and corresponding CXCL1-, CXCL8- or CXCL10-treated cells (baseline = 1) were determined by one-way ANOVA followed by a Bonferroni test (**** p ≤ 0.0001, *** p≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05). NS = not significant.</p

Characterization and β-arrestin recruitment to ORF74-R^3.50A.

<p>(A, B) HEK293T cells were transiently transfected with WT-ORF74 (WT), ORF74-R^3.50A (R^3.50A) or empty vector DNA (mock-transfected). Relative receptor expression at the cell surface was determined by ELISA (A) and constitutive activation of PLC was determined by measuring InsP accumulation (B). Data are presented as fold over mock-transfected cells (dotted line). (C, D) HEK293T cells expressing ORF74-Rluc8 (WT) (filled circles) or ORF74-R^3.50A-Rluc8 (R^3.50A) (open squares) in combination with β-arrestin1-eYFP (C) or β-arrestin2-eYFP (D) were stimulated with increasing concentrations of CXCL1. Data are shown as fold over basal. All data are represented as the mean of pooled data from at least three independent experiments each performed in triplicate and error bars indicate SEM values. Statistical differences of cell surface expression (A) or constitutive PLC activation (B) between WT-ORF74 and ORF74-R^3.50A were determined by a Student t test (**** p ≤ 0.0001, *** p ≤ 0.001).</p

Virtual Fragment Screening: Discovery of Histamine H₃ Receptor Ligands Using Ligand-Based and Protein-Based Molecular Fingerprints

Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active <i>and</i> inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using <i>molecular fingerprints</i> derived from a unique set of fragment affinity data for the histamine H₃ receptor (H₃R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H₃R binders from inactive molecules were successfully used for the identification of new H₃R ligands. Prospective virtual screening of 156 090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H₃R ligands that offer new potential starting points for the design of H₃R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein–fragment interaction space <i>in silico</i>