19767
2018
eng
81
3
4
article
1
--
2018-07-04
--
Identification of antifungal targets based on computer modeling
Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host–pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
Journal of Fungi
2309-608X
10.3390/jof4030081
urn:nbn:de:bvb:20-opus-197670
Journal of Fungi 2018, 4(3), 81; https://doi.org/10.3390/jof4030081
031A408B
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Elena Bencurova
Shishir K. Gupta
Edita Sarukhanyan
Thomas Dandekar
eng
uncontrolled
Aspergillus
eng
uncontrolled
metabolic pathways
eng
uncontrolled
computational modelling
eng
uncontrolled
drug design
Biowissenschaften; Biologie
open_access
Theodor-Boveri-Institut für Biowissenschaften
OpenAIRE
Import
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/19767/jof-04-00081.pdf
17683
2018
eng
e0205348
10
13
article
1
2019-02-19
--
--
Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning
Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical “clear core” vesicles (CCV) and the typically larger “dense core” vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3].
To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.
PLoS ONE
10.1371/journal.pone.0205348
urn:nbn:de:bvb:20-opus-176831
PLoS ONE 2018, 13(10):e0205348. DOI: 10.1371/journal.pone.0205348
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Kristin Verena Kaltdorf
Maria Theiss
Sebastian Matthias Markert
Mei Zhen
Thomas Dandekar
Christian Stigloher
Philipp Kollmannsberger
eng
uncontrolled
synaptic vesicles
eng
uncontrolled
Caenorhabditis elegans
eng
uncontrolled
machine learning
Biowissenschaften; Biologie
open_access
Theodor-Boveri-Institut für Biowissenschaften
Center for Computational and Theoretical Biology
Förderzeitraum 2018
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/17683/Kaltdorf_PLoS_ONE.pdf
17673
2018
eng
5281-5290
5
3
article
1
2019-02-15
--
--
In silico designed Axl receptor blocking drug candidates against Zika virus infection
After a large outbreak in Brazil, novel drugs against Zika virus became extremely necessary. Evaluation of virus-based pharmacological strategies concerning essential host factors brought us to the idea that targeting the Axl receptor by blocking its dimerization function could be critical for virus entry. Starting from experimentally validated compounds, such as RU-301, RU-302, warfarin, and R428, we identified a novel compound 2′ (R428 derivative) to be the most potent for this task amongst a number of alternative compounds and leads. The improved affinity of compound 2′ was confirmed by molecular docking as well as molecular dynamics simulation techniques using implicit solvation models. The current study summarizes a new possibility for inhibition of the Axl function as a potential target for future antiviral therapies.
ACS Omega
10.1021/acsomega.8b00223
urn:nbn:de:bvb:20-opus-176739
ACS Omega 2018, 3(5), 5281-5290. DOI: 10.1021/acsomega.8b00223
false
true
CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International
Edita Sarukhanyan
Sergey Shityakov
Thomas Dandekar
eng
uncontrolled
free energy
eng
uncontrolled
molecular docking
eng
uncontrolled
molecular dynamics
eng
uncontrolled
simulation
eng
uncontrolled
pharmacology
eng
uncontrolled
proteins
eng
uncontrolled
structure-activity relationship
eng
uncontrolled
viruses
eng
uncontrolled
Zika virus
Pharmakologie, Therapeutik
open_access
Klinik und Poliklinik für Anästhesiologie (ab 2004)
Theodor-Boveri-Institut für Biowissenschaften
Förderzeitraum 2018
Universität Würzburg
https://opus.bibliothek.uni-wuerzburg.de/files/17673/Sarukhanyan_ACS_Omega.pdf