@article{ScharkoiFackeldeyMerkulowetal.2013, author = {Scharkoi, Olga and Fackeldey, Konstantin and Merkulow, Igor and Andrae, Karsten and Weber, Marcus and Nehls, Irene}, title = {Conformational Analysis of Alternariol on the Quantum Level}, series = {J. Mol. Model.}, volume = {19}, journal = {J. Mol. Model.}, number = {6}, doi = {10.1007/s00894-013-1803-2}, pages = {2567 -- 2572}, year = {2013}, language = {en} } @article{BirkRaharinirinaFackeldeyetal., author = {Birk, Ralph and Raharinirina, N. Alexia and Fackeldey, Konstantin and Richter, Tonio Sebastian and Weber, Marcus}, title = {Inferring cultural and social processes based on patterns of statistical relationships between Synodal texts}, abstract = {In this paper, we explore the relationship patterns between Ancient Egyptian texts of the corpus ``Synodal decrees'', which are originating between 243 and 185 BCE, during the Ptolemaic period. Particularly, we are interested in analyzing the grammatical features of the different texts. Conventional data analysis methods such as correspondence Analysis are very useful to explore the patterns of statistical interdependence between categories of variables. However, it is based on a PCA-like dimension-reduction method and turned out to be unsuitable for our dataset due to the high dimensionality of our data representations. Additionally, the similarity between pairs of texts and pairs of grammatical features is observed through the distance between their representation, but the degree of association between a particular grammatical feature and a text is not. Here, we applied a qualitative Euclidean embedding method that provides a new Euclidean representation of the categories of variables. This new representation of the categories is constructed in such a way that all the patterns of statistical interdependence, similarity, and association, are seen through the Euclidean distance between them. Nevertheless, the PCA-like dimension-reduction method also performed poorly on our new representation. Therefore, we obtained a two-dimensional visualization using non-linear methods such UMAP or t-SNE. Although these dimension-reduction methods reduced the interpretability of interpoint distances, we were still able to identify important similarity patterns between the Synodal text as well as their association patterns with the grammatical features.}, language = {en} } @article{GorgullaNigamKoopetal., author = {Gorgulla, Christoph and Nigam, AkshatKumar and Koop, Matt and Selim {\c{C}}{\i}naroğlu, S{\"u}leyman and Secker, Christopher and Haddadnia, Mohammad and Kumar, Abhishek and Malets, Yehor and Hasson, Alexander and Li, Minkai and Tang, Ming and Levin-Konigsberg, Roni and Radchenko, Dmitry and Kumar, Aditya and Gehev, Minko and Aquilanti, Pierre-Yves and Gabb, Henry and Alhossary, Amr and Wagner, Gerhard and Aspuru-Guzik, Al{\´a}n and Moroz, Yurii S. and Fackeldey, Konstantin and Arthanari, Haribabu}, title = {VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules}, series = {bioRxiv}, journal = {bioRxiv}, doi = {10.1101/2023.04.25.537981}, language = {en} } @article{RayFackeldeySteinetal., author = {Ray, Sourav and Fackeldey, Konstantin and Stein, Christoph and Weber, Marcus}, title = {Coarse Grained MD Simulations of Opioid interactions with the µ-opioid receptor and the surrounding lipid membrane}, series = {Biophysica}, volume = {3}, journal = {Biophysica}, number = {2}, doi = {10.3390/biophysica3020017}, pages = {263 -- 275}, abstract = {In our previous studies, a new opioid (NFEPP) was developed to only selectively bind to the 𝜇-opoid receptor (MOR) in inflamed tissue and thus avoid the severe side effects of fentanyl. We know that NFEPP has a reduced binding affinity to MOR in healthy tissue. Inspired by the modelling and simulations performed by Sutcliffe et al., we present our own results of coarse-grained molecular dynamics simulations of fentanyl and NFEPP with regards to their interaction with the 𝜇-opioid receptor embedded within the lipid cell membrane. For technical reasons, we have slightly modified Sutcliffe's parametrisation of opioids. The pH-dependent opioid simulations are of interest because while fentanyl is protonated at the physiological pH, NFEPP is deprotonated due to its lower pKa value than that of fentanyl. Here, we analyse for the first time whether pH changes have an effect on the dynamical behaviour of NFEPP when it is inside the cell membrane. Besides these changes, our analysis shows a possible alternative interaction of NFEPP at pH 7.4 outside the binding region of the MOR. The interaction potential of NFEPP with MOR is also depicted by analysing the provided statistical molecular dynamics simulations with the aid of an eigenvector analysis of a transition rate matrix. In our modelling, we see differences in the XY-diffusion profiles of NFEPP compared with fentanyl in the cell membrane.}, language = {en} } @article{DonatiFackeldeyWeber2023, author = {Donati, Luca and Fackeldey, Konstantin and Weber, Marcus}, title = {Augmented ant colony algorithm for virtual drug discovery}, series = {Journal of Mathematical Chemistry}, volume = {62}, journal = {Journal of Mathematical Chemistry}, doi = {10.1007/s10910-023-01549-6}, pages = {367 -- 385}, year = {2023}, abstract = {Docking is a fundamental problem in computational biology and drug discovery that seeks to predict a ligand's binding mode and affinity to a target protein. However, the large search space size and the complexity of the underlying physical interactions make docking a challenging task. Here, we review a docking method, based on the ant colony optimization algorithm, that ranks a set of candidate ligands by solving a minimization problem for each ligand individually. In addition, we propose an augmented version that takes into account all energy functions collectively, allowing only one minimization problem to be solved. The results show that our modification outperforms in accuracy and efficiency.}, language = {en} } @article{SchimunekSeidlElezetal.2023, author = {Schimunek, Johannes and Seidl, Philipp and Elez, Katarina and Hempel, Tim and Le, Tuan and No{\´e}, Frank and Olsson, Simon and Raich, Llu{\´i}s and Winter, Robin and Gokcan, Hatice and Gusev, Filipp and Gutkin, Evgeny M. and Isayev, Olexandr and Kurnikova, Maria G. and Narangoda, Chamali H. and Zubatyuk, Roman and Bosko, Ivan P. and Furs, Konstantin V. and Karpenko, Anna D. and Kornoushenko, Yury V. and Shuldau, Mikita and Yushkevich, Artsemi and Benabderrahmane, Mohammed B. and Bousquet-Melou, Patrick and Bureau, Ronan and Charton, Beatrice and Cirou, Bertrand C. and Gil, G{\´e}rard and Allen, William J. and Sirimulla, Suman and Watowich, Stanley and Antonopoulos, Nick and Epitropakis, Nikolaos and Krasoulis, Agamemnon and Itsikalis, Vassilis and Theodorakis, Stavros and Kozlovskii, Igor and Maliutin, Anton and Medvedev, Alexander and Popov, Petr and Zaretckii, Mark and Eghbal-Zadeh, Hamid and Halmich, Christina and Hochreiter, Sepp and Mayr, Andreas and Ruch, Peter and Widrich, Michael and Berenger, Francois and Kumar, Ashutosh and Yamanishi, Yoshihiro and Zhang, Kam Y. J. and Bengio, Emmanuel and Bengio, Yoshua and Jain, Moksh J. and Korablyov, Maksym and Liu, Cheng-Hao and Marcou, Gilles and Glaab, Enrico and Barnsley, Kelly and Iyengar, Suhasini M. and Ondrechen, Mary Jo and Haupt, V. Joachim and Kaiser, Florian and Schroeder, Michael and Pugliese, Luisa and Albani, Simone and Athanasiou, Christina and Beccari, Andrea and Carloni, Paolo and D'Arrigo, Giulia and Gianquinto, Eleonora and Goßen, Jonas and Hanke, Anton and Joseph, Benjamin P. and Kokh, Daria B. and Kovachka, Sandra and Manelfi, Candida and Mukherjee, Goutam and Mu{\~n}iz-Chicharro, Abraham and Musiani, Francesco and Nunes-Alves, Ariane and Paiardi, Giulia and Rossetti, Giulia and Sadiq, S. Kashif and Spyrakis, Francesca and Talarico, Carmine and Tsengenes, Alexandros and Wade, Rebecca C. and Copeland, Conner and Gaiser, Jeremiah and Olson, Daniel R. and Roy, Amitava and Venkatraman, Vishwesh and Wheeler, Travis J. and Arthanari, Haribabu and Blaschitz, Klara and Cespugli, Marco and Durmaz, Vedat and Fackeldey, Konstantin and Fischer, Patrick D. and Gorgulla, Christoph and Gruber, Christian and Gruber, Karl and Hetmann, Michael and Kinney, Jamie E. and Padmanabha Das, Krishna M. and Pandita, Shreya and Singh, Amit and Steinkellner, Georg and Tesseyre, Guilhem and Wagner, Gerhard and Wang, Zi-Fu and Yust, Ryan J. and Druzhilovskiy, Dmitry S. and Filimonov, Dmitry A. and Pogodin, Pavel V. and Poroikov, Vladimir and Rudik, Anastassia V. and Stolbov, Leonid A. and Veselovsky, Alexander V. and De Rosa, Maria and De Simone, Giada and Gulotta, Maria R. and Lombino, Jessica and Mekni, Nedra and Perricone, Ugo and Casini, Arturo and Embree, Amanda and Gordon, D. Benjamin and Lei, David and Pratt, Katelin and Voigt, Christopher A. and Chen, Kuang-Yu and Jacob, Yves and Krischuns, Tim and Lafaye, Pierre and Zettor, Agn{\`e}s and Rodr{\´i}guez, M. Luis and White, Kris M. and Fearon, Daren and Von Delft, Frank and Walsh, Martin A. and Horvath, Dragos and Brooks III, Charles L. and Falsafi, Babak and Ford, Bryan and Garc{\´i}a-Sastre, Adolfo and Yup Lee, Sang and Naffakh, Nadia and Varnek, Alexandre and Klambauer, G{\"u}nter and Hermans, Thomas M.}, title = {A community effort in SARS-CoV-2 drug discovery}, series = {Molecular Informatics}, volume = {43}, journal = {Molecular Informatics}, number = {1}, doi = {https://doi.org/10.1002/minf.202300262}, pages = {e202300262}, year = {2023}, language = {en} } @misc{Secker, author = {Secker, Christopher}, title = {Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists (Dataset)}, doi = {10.12752/9622}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-96220}, abstract = {Virtual Screening Dataset for the paper "Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists" by Secker et al. (https://doi.org/10.1186/s13321-023-00746-4)}, language = {en} } @article{Raharinirina, author = {Raharinirina, Nomenjanahary Alexia AND Sunkara, Vikram AND von Kleist, Max AND Fackeldey, Konstantin AND Weber, Marcus}, title = {Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables}, series = {PLOS ONE}, volume = {19}, journal = {PLOS ONE}, number = {5}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0302425}, pages = {1 -- 26}, language = {en} } @article{SeckerFackeldeyWeberetal., author = {Secker, Christopher and Fackeldey, Konstantin and Weber, Marcus and Ray, Sourav and Gorgulla, Christoph and Sch{\"u}tte, Christof}, title = {Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists}, series = {Journal of Cheminformatics}, volume = {15}, journal = {Journal of Cheminformatics}, doi = {10.1186/s13321-023-00746-4}, abstract = {Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the μ-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported β-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.}, language = {en} }