@misc{WeberBeckerKoeppenetal.2007, author = {Weber, Marcus and Becker, Roland and K{\"o}ppen, Robert and Durmaz, Vedat}, title = {Classical hybrid Monte-Carlo simulations of the interconversion of hexabromocyclododecane}, organization = {Zuse-Institut Berlin}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-10308}, number = {07-31}, year = {2007}, abstract = {In this paper, we investigate the interconversion processes of the major flame retardant -- 1,2,5,6,9,10-hexabromocyclododecane (HBCD) -- by the means of statistical thermodynamics based on classical force-fields. Three ideas will be presented. First, the application of classical hybrid Monte-Carlo simulations for quantum mechanical processes will be justified. Second, the problem of insufficient convergence properties of hybrid Monte-Carlo methods for the generation of low temperature canonical ensembles will be solved by an interpolation approach. Furthermore, it will be shown how free energy differences can be used for a rate matrix computation. The results of our numerical simulations will be compared to experimental results.}, language = {en} } @article{SpahnDelVecchioLabuzetal.2017, author = {Spahn, Viola and Del Vecchio, Giovanna and Labuz, Dominika and Rodriguez-Gaztelumendi, Antonio and Massaly, N. and Temp, Julia and Durmaz, Vedat and Sabri, P. and Reidelbach, Marco and Machelska, Halina and Weber, Marcus and Stein, Christoph}, title = {A nontoxic pain killer designed by modeling of pathological receptor conformations}, volume = {355}, journal = {Science}, number = {6328}, doi = {10.1126/science.aai8636}, pages = {966 -- 969}, year = {2017}, language = {en} } @incollection{DurmazFackeldeyWeber2011, author = {Durmaz, Vedat and Fackeldey, Konstantin and Weber, Marcus}, title = {A rapidly Mixing Monte Carlo Method for the Simulation of Slow Molecular Processes}, booktitle = {Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science}, editor = {Mode, Ch.}, publisher = {InTech}, year = {2011}, language = {en} } @article{WeberBeckerKoeppenetal.2008, author = {Weber, Marcus and Becker, Roland and K{\"o}ppen, Robert and Durmaz, Vedat}, title = {Classical hybrid Monte-Carlo simulations of the interconversion of hexabromocyclododecane}, volume = {34}, journal = {Journal of Molecular Simulation}, number = {7}, pages = {727 -- 736}, year = {2008}, language = {en} } @article{KoeppenRiedelProskeetal.2012, author = {K{\"o}ppen, Robert and Riedel, Juliane and Proske, Matthias and Drzymala, Sarah and Rasenko, Tatjana and Durmaz, Vedat and Weber, Marcus and Koch, Matthias}, title = {Photochemical trans-/cis-isomerization and quantification of zearalenone in edible oils}, volume = {60}, journal = {J. Agric. Food Chem.}, doi = {10.1021/jf3037775}, pages = {11733 -- 11740}, year = {2012}, language = {en} } @article{DurmazWeberBecker2012, author = {Durmaz, Vedat and Weber, Marcus and Becker, Roland}, title = {How to Simulate Affinities for Host-Guest Systems Lacking Binding Mode Information: application to the liquid chromatographic separation of hexabromocyclododecane stereoisomers}, volume = {18}, journal = {Journal of Molecular Modeling}, doi = {10.1007/s00894-011-1239-5}, pages = {2399 -- 2408}, year = {2012}, language = {en} } @article{KoeppenBeckerWeberetal.2009, author = {K{\"o}ppen, Robert and Becker, Roland and Weber, Marcus and Durmaz, Vedat and Nehls, Irene}, title = {HBCD stereoisimers: Thermal interconversion and enantiospecific trace analysis in biota}, volume = {70}, journal = {Organohalogen Compounds}, pages = {910 -- 913}, year = {2009}, language = {en} } @article{WeberDurmazBeckeretal.2009, author = {Weber, Marcus and Durmaz, Vedat and Becker, Roland and Esslinger, Susanne}, title = {Predictive Identification of Pentabromocyclododecane (PBCD) Isomers with high Binding Affinity to hTTR}, volume = {71}, journal = {Organohalogen Compounds}, pages = {247 -- 252}, year = {2009}, language = {en} } @misc{Durmaz2009, type = {Master Thesis}, author = {Durmaz, Vedat}, title = {Theoretical Investigations on HBCD and PBCD}, year = {2009}, language = {en} } @article{GorgullaDasLeighetal.2021, author = {Gorgulla, Christoph and Das, Krishna M. Padmanabha and Leigh, Kendra E and Cespugli, Marco and Fischer, Patrick D. and Wang, Zi-Fu and Tesseyre, Guilhem and Pandita, Shreya and Shnapir, Alex and Calderaio, Anthony and Hutcheson, Colin and Gechev, Minko and Rose, Alexander and Lewis, Noam and Yaffe, Erez and Luxenburg, Roni and Herce, Henry D. and Durmaz, Vedat and Halazonetis, Thanos D. and Fackeldey, Konstantin and Patten, Justin J. and Chuprina, Alexander and Dziuba, Igor and Plekhova, Alla and Moroz, Yurii and Radchenko, Dmytro and Tarkhanova, Olga and Yavnyuk, Irina and Gruber, Christian C. and Yust, Ryan and Payne, Dave and N{\"a}{\"a}r, Anders M. and Namchuk, Mark N. and Davey, Robert A. and Wagner, Gerhard and Kinney, Jamie and Arthanari, Haribabu}, title = {A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening}, volume = {24}, journal = {iScience}, number = {2}, publisher = {CellPress}, doi = {10.26434/chemrxiv.12682316}, pages = {102021}, year = {2021}, abstract = {Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.}, language = {en} } @phdthesis{Durmaz2016, author = {Durmaz, Vedat}, title = {Atomistic Binding Free Energy Estimations for Biological Host-Guest Systems}, publisher = {FU Dissertationen Online}, pages = {216}, year = {2016}, abstract = {Accurate quantifications of protein-ligand binding affinities by means of in silico methods increasingly gain importance in various scientific branches including toxicology and pharmacology. In silico techniques not only are generally less demanding than laboratory experiments regarding time as well as cost, in particular, if binding assays or synthesis protocols need to be developed in advance. At times, they also provide the only access to risk assessments on novel chemical compounds arising from biotic or abiotic degradation of anthropogenic substances. However, despite the continuous technological and algorithmic progress over the past decades, binding free energy estimations through molecular dynamics simulations still pose an enormous computational challenge owed to the mathematical complexity of solvated macromolecular systems often consisting of hundreds of thousands of atoms. The goals of this thesis can roughly be divided into two categories dealing with different aspects of host-guest binding quantification. On the one side algorithmic strategies for a comprehensive exploration and decomposition of conformational space in conjunction with an automated selection of representative molecular geometries and binding poses have been elaborated providing initial structures for free energy calculations. In light of the dreaded trapping problem typically associated with molecular dynamics simulations, the focus was laid on a particularly systematic generation of representatives covering a broad range of physically accessible molecular conformations and interaction modes. On the other side and ensuing from these input geometries, binding affinity models based on the linear interaction energy (LIE) method have been developed for a couple of (bio)molecular systems. The applications included a successful prediction of the liquid-chromatographic elution order as well as retention times of highly similar hexabromocyclododecane (HBCD) stereoisomers, a novel empirical LIE-QSAR hybrid binding affinity model related to the human estrogen receptor α (ERα), and, finally, the (eco)toxicological prioritization of transformation products originating from the antibiotic sulfamethoxazole with respect to their binding affinities to the bacterial enzyme dihydropteroate synthase. Altogether, a fully automated approach to binding mode and affinity estimation has been presented that is content with an arbitrary geometry of a small molecule under observation and a spatial vector specifying the binding site of a potential target molecule. According to our studies, it is superior to conventional docking and thermodynamic average methods and primarily suggesting binding free energy calculation on the basis of several heavily distinct complex geometries. Both chromatographic retention times of HBCD and binding affinities to ERα yielded squared coefficients of correlation with experimental results significantly higher than 0.8. Approximately 85 \% (100 \%) of predicted receptor-ligand binding modes deviated less than 1.53 {\AA} (2.05 {\AA}) from available crystallographic structures.}, language = {en} } @misc{WeberDurmazSabrietal.2017, author = {Weber, Marcus and Durmaz, Vedat and Sabri, Peggy and Reidelbach, Marco}, title = {Supplementary simulation data for Science Manuscript ai8636}, doi = {10.12752/5.MWB.1.0}, year = {2017}, abstract = {The simulation data has been produced by Vedat Durmaz, Peggy Sabri and Marco Reidelbach inside the "Computational Molecular Design" Group headed by Marcus Weber at Zuse-Institut Berlin, Takustr. 7, D-14195 Berlin, Germany. The file contains classical simulation data for different fentanyl derivates in the MOR binding pocket at different pHs. It also includes instruction files for quantum-chemical pKa-value estimations and a description of how we derived the pKa-values from the Gaussian09 log-files.}, language = {en} } @article{KoschekDurmazKrylovaetal.2015, author = {Koschek, and Durmaz, Vedat and Krylova, and Wieczorek, and Gupta, Pooja and Richter, and Bujotzek, Alexander and Fischer, and Haag, Rainer and Freund, and Weber, Marcus and Rademann,}, title = {Peptide polymer ligands for a tandem WW-domain, a soft multivalent protein-protein interaction: lessons on the thermodynamic fitness of flexible ligands}, volume = {11}, journal = {Beilstein J. Org. Chem.}, pages = {837 -- 847}, year = {2015}, language = {en} } @article{DurmazWeberMeyeretal.2015, author = {Durmaz, Vedat and Weber, Marcus and Meyer, and M{\"u}ckter,}, title = {Computergest{\"u}tzte Simulationen zur Absch{\"a}tzung gesundheitlicher Risiken durch anthropogene Spurenstoffe der Wassermatrix}, volume = {3/15}, journal = {KA Korrespondenz Abwasser, Abfall}, pages = {264 -- 267}, year = {2015}, language = {de} } @article{AndraeMerkelDurmazetal.2014, author = {Andrae, Karsten and Merkel, Stefan and Durmaz, Vedat and Fackeldey, Konstantin and K{\"o}ppen, Robert and Weber, Marcus and Koch, Matthias}, title = {Investigation of the Ergopeptide Epimerization Process}, volume = {2}, journal = {Computation}, number = {3}, doi = {10.3390/computation2030102}, pages = {102 -- 111}, year = {2014}, abstract = {Ergopeptides, like ergocornine and a-ergocryptine, exist in an S- and in an R-configuration. Kinetic experiments imply that certain configurations are preferred depending on the solvent. The experimental methods are explained in this article. Furthermore, computational methods are used to understand this configurational preference. Standard quantum chemical methods can predict the favored configurations by using minimum energy calculations on the potential energy landscape. However, the explicit role of the solvent is not revealed by this type of methods. In order to better understand its influence, classical mechanical molecular simulations are applied. It appears from our research that "folding" the ergopeptide molecules into an intermediate state (between the S- and the R-configuration) is mechanically hindered for the preferred configurations.}, language = {en} } @article{AndraeDurmazFackeldeyetal.2013, author = {Andrae, Karsten and Durmaz, Vedat and Fackeldey, Konstantin and Scharkoi, Olga and Weber, Marcus}, title = {Medizin aus dem Computer}, volume = {62}, journal = {Der Anaesthesist}, number = {7}, publisher = {Springer}, doi = {10.1007/s00101-013-2202-x}, pages = {561 -- 557}, year = {2013}, language = {de} } @article{DurmazSchmidtSabrietal.2013, author = {Durmaz, Vedat and Schmidt, Sebastian and Sabri, Peggy and Piechotta, Christian and Weber, Marcus}, title = {A hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens}, volume = {53}, journal = {Journal of Chemical Information and Modeling}, number = {10}, pages = {2681 -- 2688}, year = {2013}, language = {en} } @article{Durmaz2015, author = {Durmaz, Vedat}, title = {Markov model-based polymer assembly from force field-parameterized building blocks}, volume = {29}, journal = {Journal of Computer-Aided Molecular Design}, doi = {10.1007/s10822-014-9817-0}, pages = {225 -- 232}, year = {2015}, abstract = {A conventional by hand construction and parameterization of a polymer model for the purpose of molecular simulations can quickly become very workintensive and time-consuming. Using the example of polyglycerol, I present a polymer decompostion strategy yielding a set of five monomeric residues that are convenient for an instantaneous assembly and subsequent force field simulation of a polyglycerol polymer model. Force field parameters have been developed in accordance with the classical Amber force field. Partial charges of each unit were fitted to the electrostatic potential using quantumchemical methods and slightly modified in order to guarantee a neutral total polymer charge. In contrast to similarly constructed models of amino acid and nucleotide sequences, the glycerol building blocks may yield an arbitrary degree of bifurcations depending on the underlying probabilistic model. The iterative development of the overall structure as well as the relation of linear to branching units is controlled by a simple Markov model which is presented with few algorithmic details. The resulting polymer is highly suitable for classical explicit water molecular dynamics simulations on the atomistic level after a structural relaxation step. Moreover, the decomposition strategy presented here can easily be adopted to many other (co)polymers.}, language = {en} } @article{HeyeBeckerLuetkeEverslohetal.2016, author = {Heye, Katharina and Becker, Dennis and L{\"u}tke-Eversloh, Christian and Durmaz, Vedat and Ternes, Thomas and Oetken, Matthias and Oehlmann, J{\"o}rg}, title = {Effects of carbamazepine and two of its metabolites on the non-biting midge Chironomus riparius in a sediment full life cycle toxicity test}, volume = {98}, journal = {Water Research}, pages = {19 -- 27}, year = {2016}, abstract = {The antiepileptic drug carbamazepine (CBZ) and its main metabolites carbamazepine-10,11-epoxide (EP-CBZ) and 10,11-dihydro-10,11-dihydroxy-carbamazepine (DiOH-CBZ) were chosen as test substances to assess chronic toxicity on the non-biting midge Chironomus riparius. All three substances were tested in a 40-day sediment full life cycle test (according to OECD 233) in which mortality, emergence, fertility, and clutch size were evaluated. In addition, these parameters were integrated into the population growth rate to reveal population relevant effects. With an LC50 of 0.203 mg/kg (time-weighted mean), the metabolite EP-CBZ was significantly more toxic than the parent substance CBZ (LC50: 1.11 mg/kg). Especially mortality, emergence, and fertility showed to be sensitive parameters under the exposure to CBZ and EP-CBZ. By using classical molecular dynamics (MD) simulations, the binding of CBZ to the ecdysone receptor was investigated as one possible mode of action but showed to be unlikely. The second metabolite DiOH-CBZ did not show any effects within the tested concentration rage (0.171 - 1.22 mg/kg). Even though CBZ was less toxic compared to EP-CBZ, CBZ is found in the environment at much higher concentrations and causes therefore a higher potential risk for sediment dwelling organisms compared to its metabolites. Nevertheless, the current study illustrates the importance of including commonly found metabolites into the risk assessment of parent substances.}, language = {en} } @article{SchimunekSeidlElezetal.2024, 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}, volume = {43}, journal = {Molecular Informatics}, number = {1}, doi = {https://doi.org/10.1002/minf.202300262}, pages = {e202300262}, year = {2024}, language = {en} }