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352 Publications visible to you, out of a total of 352
Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model

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Abstract Background With the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated …er, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans ( Pomphorhynchus laevis, Neoechinorhynchus agilis , Neoechinorhynchus buttnerae ) from four fish species (common barbel, European eel, thinlip mullet, tambaqui). Results The workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel. Conclusions The combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7 .

Authors: Hanno Schmidt, Katharina Mauer, Manuel Glaser, Bahram Sayyaf Dezfuli, Sören Lukas Hellmann, Ana Lúcia Silva Gomes, Falk Butter, Rebecca C. Wade, Thomas Hankeln, Holger Herlyn

Date Published: 1st Dec 2022

Publication Type: Journal

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I

Abstract

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Authors: Marcus Fabiano de Almeida Mendes, Marcelo de Souza Bragatte, Priscila Vianna, Martiela Vaz de Freitas, Ina Pöhner, Stefan Richter, Rebecca C. Wade, Francisco Mauro Salzano, Gustavo Fioravanti Vieira

Date Published: 28th Oct 2022

Publication Type: Journal

Diffusion of small molecule drugs is affected by surface interactions and crowder proteins

Abstract

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Authors: Debabrata Dey, Ariane Nunes-Alves, Rebecca C. Wade, Gideon Schreiber

Date Published: 1st Oct 2022

Publication Type: Journal

Anti-trypanosomatid structure-based drug design – lessons learned from targeting the folate pathway

Abstract

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Authors: Joanna Panecka-Hofman, Ina Poehner, Rebecca C. Wade

Date Published: 2nd Sep 2022

Publication Type: Journal

Graphene BioFET sensors for SARS-CoV-2 detection: a multiscale simulation approach

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Two-dimensional (2D) materials BioFETs have already demonstrated their potential for detecting low amounts of molecules. Here, we present a multiscale simulation platform in the context of Graphene …xt of Graphene BioFET for the detection of SARS-CoV-2.

Authors: A. Toral-Lopez, D. B. Kokh, E. G. Marin, R. C. Wade, A. Godoy

Date Published: 15th Jul 2022

Publication Type: Journal

Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1

Abstract

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Authors: Ina Pöhner, Antonio Quotadamo, Joanna Panecka-Hofman, Rosaria Luciani, Matteo Santucci, Pasquale Linciano, Giacomo Landi, Flavio Di Pisa, Lucia Dello Iacono, Cecilia Pozzi, Stefano Mangani, Sheraz Gul, Gesa Witt, Bernhard Ellinger, Maria Kuzikov, Nuno Santarem, Anabela Cordeiro-da-Silva, Maria P. Costi, Alberto Venturelli, Rebecca C. Wade

Date Published: 14th Jul 2022

Publication Type: Journal

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

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Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to …constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Authors: Olivia Eriksson, Upinder Singh Bhalla, Kim T Blackwell, Sharon M Crook, Daniel Keller, Andrei Kramer, Marja-Leena Linne, Ausra Saudargienė, Rebecca C Wade, Jeanette Hellgren Kotaleski

Date Published: 6th Jul 2022

Publication Type: Journal

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