TY - JOUR A1 - Weber, Marcus T1 - Transformationsprodukte im Klärwerk: Mathematische Ansätze der Bewertung JF - KA Korrespondenz Abwasser, Abfall Y1 - 2019 VL - 7 SP - 551 EP - 557 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Boeszoermnyi, Andras A1 - Wang, Zi-Fu A1 - Fischer, Patrick D. A1 - Coote, Paul A1 - Das, Krishna M. Padmanabha A1 - Malets, Yehor S. A1 - Radchenko, Dmytro S. A1 - Moroz, Yurii A1 - Scott, David A. A1 - Fackeldey, Konstantin A1 - Hoffmann, Moritz A1 - Iavniuk, Iryna A1 - Wagner, Gerhard A1 - Arthanari, Haribabu T1 - An open-source drug discovery platform enables ultra-large virtual screens JF - Nature N2 - On average, an approved drug today costs $2-3 billion and takes over ten years to develop1. In part, this is due to expensive and time-consuming wet-lab experiments, poor initial hit compounds, and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening (SBVS) has the potential to mitigate these problems. With SBVS, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large compound databases exist, the ability to carry out large-scale SBVSs on computer clusters in an accessible, efficient, and flexible manner has remained elusive. Here we designed VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large ligand libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we have prepared the largest and freely available ready-to-dock ligand library available, with over 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened over 1 billion compounds and discovered a small molecule inhibitor (iKeap1) that engages KEAP1 with nanomolar affinity (Kd = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. We also identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify binders with high affinity for target proteins. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1038/s41586-020-2117-z VL - 580 SP - 663 EP - 668 PB - Springer Nature ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Fackeldey, Konstantin A1 - Wagner, Gerhard A1 - Arthanari, Haribabu T1 - Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method JF - Supercomputing Frontiers and Innovations N2 - Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role. Y1 - 2020 U6 - https://doi.org/10.14529/jsfi200301 VL - 7 IS - 3 SP - 4 EP - 12 ER - TY - THES A1 - Durmaz, Vedat T1 - Atomistic Binding Free Energy Estimations for Biological Host–Guest Systems N2 - 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 Å (2.05 Å) from available crystallographic structures. KW - molecular modelling KW - molecular dynamics KW - molecular simulation KW - binding affinity KW - prediction KW - free energy KW - receptor ligand KW - retention time KW - liquid chromatography Y1 - 2016 UR - http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000103765 PB - FU Dissertationen Online ER - TY - JOUR A1 - Durmaz, Vedat A1 - Weber, Marcus A1 - Meyer, A1 - Mückter, T1 - Computergestützte Simulationen zur Abschätzung gesundheitlicher Risiken durch anthropogene Spurenstoffe der Wassermatrix JF - KA Korrespondenz Abwasser, Abfall Y1 - 2015 VL - 3/15 SP - 264 EP - 267 ER - TY - JOUR A1 - Weber, Marcus A1 - Zoschke, Christian A1 - Sedighi, Amir A1 - Fleige, Emanuel A1 - Haag, Rainer A1 - Schäfer-Korting, Monika T1 - Free Energy Simulations of Drug loading for Core-Multishell Nanotransporters JF - J Nanomed Nanotechnol Y1 - 2014 U6 - https://doi.org/10.4172/2157-7439.1000234 VL - 5 IS - 5 SP - 234 ER - TY - THES A1 - Josten, Jan-Martin T1 - Östrogenitäts-Approximation mittels physikochemischem Oberflächenvergleich Y1 - 2012 ER - TY - JOUR A1 - Durmaz, Vedat A1 - Schmidt, Sebastian A1 - Sabri, Peggy A1 - Piechotta, Christian A1 - Weber, Marcus T1 - A hands-off linear interaction energy approach to binding mode and affinity estimation of estrogens JF - Journal of Chemical Information and Modeling Y1 - 2013 VL - 53 IS - 10 SP - 2681 EP - 2688 ER - TY - JOUR A1 - Gorgulla, Christoph A1 - Çınaroğlu, Süleyman A1 - Fischer, Patrick D. A1 - Fackeldey, Konstantin A1 - Wagner, Gerhard A1 - Arthanari, Haribabu T1 - VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization JF - Special Issue Artificial Intelligence & Deep Learning Approaches for Structural Bioinformatics N2 - The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.3390/ijms22115807 VL - 22 IS - 11 SP - 5807 ER - TY - JOUR A1 - Fackeldey, Konstantin A1 - Gorgulla, Christoph A1 - Weber, Marcus T1 - Neue Medikamente dank Supercomputern JF - Spektrum der Wissenschaft N2 - Die aktuelle Pandemie verdeutlicht, wie wichtig es ist, rasch geeignete Arzneimittel zu finden. In Computer­simulationen gelingt das erheblich schneller als im Labor. Gegen das Coronavirus ließen sich auf diese Weise bereits Wirkstoffkandidaten identifizieren. Y1 - 2021 IS - 11 SP - 40 EP - 46 ER -