1,721,021 research outputs found
Targeting the Hsp90 interactome using in silico polypharmacology approaches
No full textIn recent years, polypharmacology has gained popularity in drug discovery. [1] Especially for complex diseases such as cancer, the ability of a drug to bind to and interfere with multiple targets provides new opportunities for therapeutic intervention In this article, we focus on Hsp90 and its interactome, whose pivotal role in survival and proliferation of cancer cells renders this array of targets particularly attractive polypharmacological drug design strategies.
The primary goal of our work is the identification and selection of suitable target proteins from the interactome that might be combined with Hsp90 to explore and exploit a multi-target inhibition approach. This task is accomplished by applying computational methods to mine the structural and biological information associated with potential ligands in public databases and assess the degree of structural similarity between known inhibitors of different targets. Therefore, we propose an integrated ligand- and structure-based approach to select small molecules from databases suitable for consideration as multi-target inhibitors
Virtual screening for dual Hsp90/B-Raf inhibitors
No full textIn this chapter, we describe a computational strategy leading to the identification of the first dual inhibitors of Heat Shock Protein 90 (Hsp90) and protein kinase B-Raf. Both proteins are validated targets for anti-cancer drug discovery. There is strong evidence that the simultaneous inhibition of Hsp90 and B-Raf provides therapeutic benefits compared to exclusive engagement of one or the other target. Hence, we have been interested in searching for dual Hsp90/B-Raf inhibitors. Virtual compound screening led to the identification of two compounds with micromolar activity against both targets. The computational approach faced a number of challenges that needed to be overcome, as described herein
Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space
No full textHeat shock protein 90 (Hsp90) and B-Raf are validated targets for anticancer drug discovery. Although there is strong evidence that concomitant inhibition of Hsp90 and B-Raf may provide significant therapeutic benefits, molecules endowed with dual activity against the two targets have not been reported. For the first time, we show that Hsp90 and B-Raf inhibitors have overlapping chemical space and we disclose the first-in-class dual inhibitors. The compounds were identified through a computational strategy especially devised for detecting ligands with dual-target activity. Although the two targets had only remote binding site similarity, we were able to identify dual inhibitors with well-balanced in vitro potencies and relatively low molecular weight. Remarkably, they also inhibited the V600E mutant form of B-Raf with similar potency. This study provides the first direct proof that designing dual ligands of Hsp90 and a kinase is possible, thus opening the way to new interesting possibilities in drug discovery
ccbmlib – a Python Package for Modeling Tanimoto Coefficient Distributions for Molecular Fingerprints
No full textccbmlib is a Python package for modeling Tanimoto coefficient distributions for molecular fingerprints. It is based on RDKit and contains methods for determining fingerprint statistics, which are the basis for generating conditional and unconditional distribution models. For the theoretical background see:
Vogt M & Bajorath J. Introduction of the Conditional Correlated Bernoulli Model of similarity value distributions and its application to the prospective prediction of fingerprint search performance. J Chem Inf Model 51, 2496-2506, 2011. https://dx.doi.org/10.1021/ci2003472
Vogt M & Bajorath J. Modeling Tanimoto similarity value distributions and predicting search results. Mol Inf 36, 1600131, 2017. https://doi.org/10.1002/minf.201600131
Vogt M & Bajorath J. ccbmlib – a Python package for modeling Tanimoto similarity value distributions. F1000Research 9(Chem Inf Sci), e100, 2020. https://doi.org/10.12688/f1000research.22292.1
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Predicting Isoform-Selective Carbonic Anhydrase Inhibitors via Machine Learning and Rationalizing Structural Features Important for Selectivity
Full text linkCarbonic anhydrases (CAs) catalyze the physiological hydration of carbon dioxide and are among the most intensely studied pharmaceutical target enzymes. A hallmark of CA inhibition is the complexation of the catalytic zinc cation in the active site. Human (h) CA isoforms belonging to different families are implicated in a wide range of diseases and of very high interest for therapeutic intervention. Given the conserved catalytic mechanisms and high similarity of many hCA isoforms, a major challenge for CA-based therapy is achieving inhibitor selectivity for hCA isoforms that are associated with specific pathologies over other widely distributed isoforms such as hCA I or hCA II that are of critical relevance for the integrity of many physiological processes. To address this challenge, we have attempted to predict compounds that are selective for isoform hCA IX, which is a tumor-associated protein and implicated in metastasis, over hCA II on the basis of a carefully curated data set of selective and nonselective inhibitors. Machine learning achieved surprisingly high accuracy in predicting hCA IX-selective inhibitors. The results were further investigated, and compound features determining successful predictions were identified. These features were then studied on the basis of X-ray structures of hCA isoform-inhibitor complexes and found to include substructures that explain compound selectivity. Our findings lend credence to selectivity predictions and indicate that the machine learning models derived herein have considerable potential to aid in the identification of new hCA IX-selective compounds
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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