eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

© 2017 About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/

Large-scale computational drug repositioning to find treatments for rare diseases

© 2018, The Author(s). Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed eMatchSite, a new computer program to compare drug-binding sites. In this study, eMatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/

Binding site matching in rational drug design: Algorithms and applications

© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning

BionoiNet: Ligand-binding site classification with off-the-shelf deep neural network

© The 2020 Author(s). Published by Oxford University Press. All rights reserved. Motivation: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide-and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results: We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide-and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures

Beryllium and Alpha-Element Abundances in a Large Sample of Metal-Poor Stars

The light elements, Li, Be, and B, provide tracers for many aspects of astronomy including stellar structure, Galactic evolution, and cosmology. We have taken spectra of Be in 117 metal-poor stars ranging in metallicity from [Fe/H] = -0.5 to -3.5 with Keck I + HIRES at a resolution of 42,000 and signal-to-noise ratios of near 100. We have determined the stellar parameters spectroscopically from lines of Fe I, Fe II, Ti I and Ti II. The abundances of Be and O were derived by spectrum synthesis techniques, while abundances of Fe, Ti, and Mg were found from many spectral line measurements. There is a linear relationship between [Fe/H] and A(Be) with a slope of +0.88 +-0.03 over three orders of magnitude in [Fe/H]. We fit the relationship between A(Be) and [O/H] with both a single slope and with two slopes. The relationship between [Fe/H] and [O/H] seems robustly linear and we conclude that the slope change in Be vs. O is due to the Be abundance. Although Be is a by-product of CNO, we have used Ti and Mg abundances as alpha-element surrogates for O in part because O abundances are rather sensitive to both stellar temperature and surface gravity. We find that A(Be) tracks [Ti/H] very well with a slope of 1.00 +-0.04. It also tracks [Mg/H] very well with a slope of 0.88 +-0.03. We find that there are distinct differences in the relationships of A(Be) and [Fe/H] and of A(Be) and [O/H] for our dissipative stars and our accretive stars. We suggest that the Be in the dissipative stars was primarily formed by GCR spallation and Be in the accretive stars was formed in the vicinity of SN II.Comment: Accepted for Ap.J. Nov. 10, 2011, v. 741 70 pages, 27 figures, 5 table

Oxford SWIFT IFS and multi-wavelength observations of the Eagle galaxy at z=0.77

The `Eagle' galaxy at a redshift of 0.77 is studied with the Oxford Short Wavelength Integral Field Spectrograph (SWIFT) and multi-wavelength data from the All-wavelength Extended Groth strip International Survey (AEGIS). It was chosen from AEGIS because of the bright and extended emission in its slit spectrum. Three dimensional kinematic maps of the Eagle reveal a gradient in velocity dispersion which spans 35-75 +/- 10 km/s and a rotation velocity of 25 +/- 5 km/s uncorrected for inclination. Hubble Space Telescope images suggest it is close to face-on. In comparison with galaxies from AEGIS at similar redshifts, the Eagle is extremely bright and blue in the rest-frame optical, highly star-forming, dominated by unobscured star-formation, and has a low metallicity for its size. This is consistent with its selection. The Eagle is likely undergoing a major merger and is caught in the early stage of a star-burst when it has not yet experienced metal enrichment or formed the mass of dust typically found in star-forming galaxies.Comment: accepted for publication in MNRA

Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales

Losses of corals worldwide emphasize the need to understand what drives reef decline. Stressors such as overfishing and nutrient pollution may reduce resilience of coral reefs by increasing coral?algal competition and reducing coral recruitment, growth and survivorship. Such effects may themselves develop via several mechanisms, including disruption of coral microbiomes. Here we report the results of a 3-year field experiment simulating overfishing and nutrient pollution. These stressors increase turf and macroalgal cover, destabilizing microbiomes, elevating putative pathogen loads, increasing disease more than twofold and increasing mortality up to eightfold. Above-average temperatures exacerbate these effects, further disrupting microbiomes of unhealthy corals and concentrating 80% of mortality in the warmest seasons. Surprisingly, nutrients also increase bacterial opportunism and mortality in corals bitten by parrotfish, turning normal trophic interactions deadly for corals. Thus, overfishing and nutrient pollution impact reefs down to microbial scales, killing corals by sensitizing them to predation, above-average temperatures and bacterial opportunism