@article{BencurovaGuptaSarukhanyanetal.2018,
  author    = {Bencurova, Elena and Gupta, Shishir K. and Sarukhanyan, Edita and Dandekar, Thomas},
  title     = {Identification of antifungal targets based on computer modeling},
  series = {Journal of Fungi},
  volume    = {4},
  journal   = {Journal of Fungi},
  number    = {3},
  issn      = {2309-608X},
  doi       = {10.3390/jof4030081},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197670},
  pages     = {81},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{KaltdorfTheissMarkertetal.2018,
  author    = {Kaltdorf, Kristin Verena and Theiss, Maria and Markert, Sebastian Matthias and Zhen, Mei and Dandekar, Thomas and Stigloher, Christian and Kollmannsberger, Philipp},
  title     = {Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning},
  series = {PLoS ONE},
  volume    = {13},
  journal   = {PLoS ONE},
  number    = {10},
  doi       = {10.1371/journal.pone.0205348},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176831},
  pages     = {e0205348},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{SarukhanyanShityakovDandekar2018,
  author    = {Sarukhanyan, Edita and Shityakov, Sergey and Dandekar, Thomas},
  title     = {In silico designed Axl receptor blocking drug candidates against Zika virus infection},
  series = {ACS Omega},
  volume    = {3},
  journal   = {ACS Omega},
  number    = {5},
  doi       = {10.1021/acsomega.8b00223},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176739},
  pages     = {5281-5290},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}