@inproceedings{FackeldeyKrause2007, author = {Fackeldey, Konstantin and Krause, Rolf}, title = {Solving Frictional Contact Problems with Multigrid Efficiency}, volume = {50}, booktitle = {Proc.of the 16th International Conference on Domain Decomposition Methods}, editor = {Widlund, Olof}, pages = {547 -- 554}, year = {2007}, language = {en} } @inproceedings{FackeldeyKrause2007, author = {Fackeldey, Konstantin and Krause, Rolf}, title = {Weak coupling in function space}, volume = {7}, booktitle = {Proc. Appl. Math. Mech.}, number = {1}, pages = {2020113pp}, year = {2007}, language = {en} } @inproceedings{FackeldeyKrause2008, author = {Fackeldey, Konstantin and Krause, Rolf}, title = {CM/MD Coupling - A Function Space Oriented Multiscale-Coupling Approach}, volume = {8}, booktitle = {Proc. Appl. Math. Mech.}, number = {1}, pages = {10495pp}, year = {2008}, language = {en} } @inproceedings{FackeldeyBujotzekWeber2012, author = {Fackeldey, Konstantin and Bujotzek, Alexander and Weber, Marcus}, title = {A meshless discretization method for Markov state models applied to explicit water peptide folding simulations}, volume = {89}, booktitle = {Meshfree Methods for Partial Differential Equations VI}, publisher = {Springer}, pages = {141 -- 154}, year = {2012}, language = {en} } @inproceedings{Fackeldey2012, author = {Fackeldey, Konstantin}, title = {Multiscale Methods in Time and Space}, volume = {17}, booktitle = {Progress in Industrial Mathematics at ECMI 2010}, pages = {619 -- 626}, year = {2012}, language = {en} } @article{FackeldeyKlimmWeber2012, author = {Fackeldey, Konstantin and Klimm, Martina and Weber, Marcus}, title = {A Coarse Graining Method for the Dimension Reduction of the State Space of Biomolecules}, volume = {5}, journal = {Journal of Mathematical Chemistry}, number = {9}, pages = {2623 -- 2635}, year = {2012}, language = {en} } @article{KrauseFackeldeyKrause2014, author = {Krause, Dorian and Fackeldey, Konstantin and Krause, Rolf}, title = {A Parallel Multiscale Simulation Toolbox for Coupling Molecular Dynamics and Finite Elements}, journal = {Singular Phenomena and Scaling in Mathematical Models}, editor = {Griebel, Michael}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-319-00786-1_14}, pages = {327 -- 346}, year = {2014}, language = {en} } @article{HaackFackeldeyRoeblitzetal.2013, author = {Haack, Fiete and Fackeldey, Konstantin and R{\"o}blitz, Susanna and Scharkoi, Olga and Weber, Marcus and Schmidt, Burkhard}, title = {Adaptive spectral clustering with application to tripeptide conformation analysis}, volume = {139}, journal = {The Journal of Chemical Physics}, doi = {10.1063/1.4830409}, pages = {110 -- 194}, year = {2013}, language = {en} } @article{ReuterWeberFackeldeyetal.2018, author = {Reuter, Bernhard and Weber, Marcus and Fackeldey, Konstantin and R{\"o}blitz, Susanna and Garcia, Martin E.}, title = {Generalized Markov State Modeling Method for Nonequilibrium Biomolecular Dynamics: Exemplified on Amyloid β Conformational Dynamics Driven by an Oscillating Electric Field}, volume = {14}, journal = {Journal of Chemical Theory and Computation}, number = {7}, doi = {10.1021/acs.jctc.8b00079}, pages = {3579 -- 3594}, year = {2018}, abstract = {Markov state models (MSMs) have received an unabated increase in popularity in recent years, as they are very well suited for the identification and analysis of metastable states and related kinetics. However, the state-of-the-art Markov state modeling methods and tools enforce the fulfillment of a detailed balance condition, restricting their applicability to equilibrium MSMs. To date, they are unsuitable to deal with general dominant data structures including cyclic processes, which are essentially associated with nonequilibrium systems. To overcome this limitation, we developed a generalization of the common robust Perron Cluster Cluster Analysis (PCCA+) method, termed generalized PCCA (G-PCCA). This method handles equilibrium and nonequilibrium simulation data, utilizing Schur vectors instead of eigenvectors. G-PCCA is not limited to the detection of metastable states but enables the identification of dominant structures in a general sense, unraveling cyclic processes. This is exemplified by application of G-PCCA on nonequilibrium molecular dynamics data of the Amyloid β (1-40) peptide, periodically driven by an oscillating electric field.}, language = {en} } @article{GorgullaBoeszoermnyiWangetal.2020, author = {Gorgulla, Christoph and Boeszoermnyi, Andras and Wang, Zi-Fu and Fischer, Patrick D. and Coote, Paul and Das, Krishna M. Padmanabha and Malets, Yehor S. and Radchenko, Dmytro S. and Moroz, Yurii and Scott, David A. and Fackeldey, Konstantin and Hoffmann, Moritz and Iavniuk, Iryna and Wagner, Gerhard and Arthanari, Haribabu}, title = {An open-source drug discovery platform enables ultra-large virtual screens}, volume = {580}, journal = {Nature}, publisher = {Springer Nature}, doi = {https://doi.org/10.1038/s41586-020-2117-z}, pages = {663 -- 668}, year = {2020}, abstract = {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.}, language = {en} } @article{GorgullaFackeldeyWagneretal.2020, author = {Gorgulla, Christoph and Fackeldey, Konstantin and Wagner, Gerhard and Arthanari, Haribabu}, title = {Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method}, volume = {7}, journal = {Supercomputing Frontiers and Innovations}, number = {3}, doi = {10.14529/jsfi200301}, pages = {4 -- 12}, year = {2020}, abstract = {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.}, language = {en} } @article{FackeldeyRoehmNiknejadetal.2021, author = {Fackeldey, Konstantin and R{\"o}hm, Jonas and Niknejad, Amir and Chewle, Surahit and Weber, Marcus}, title = {Analyzing Raman Spectral Data without Separabiliy Assumption}, volume = {3}, journal = {Journal of Mathematical Chemistry}, number = {59}, publisher = {Springer}, arxiv = {http://arxiv.org/abs/2007.06428}, doi = {10.1007/s10910-020-01201-7}, pages = {575 -- 596}, year = {2021}, abstract = {Raman spectroscopy is a well established tool for the analysis of vibration spectra, which then allow for the determination of individual substances in a chemical sample, or for their phase transitions. In the Time-Resolved-Raman-Sprectroscopy the vibration spectra of a chemical sample are recorded sequentially over a time interval, such that conclusions for intermediate products (transients) can be drawn within a chemical process. The observed data-matrix M from a Raman spectroscopy can be regarded as a matrix product of two unknown matrices W and H, where the first is representing the contribution of the spectra and the latter represents the chemical spectra. One approach for obtaining W and H is the non-negative matrix factorization. We propose a novel approach, which does not need the commonly used separability assumption. The performance of this approach is shown on a real world chemical example.}, language = {en} } @article{RoehlWeberFackeldey2021, author = {R{\"o}hl, Susanne and Weber, Marcus and Fackeldey, Konstantin}, title = {Computing the minimal rebinding effect for non-reversible processes}, volume = {19}, journal = {Multiscale Modeling and Simulation}, number = {1}, arxiv = {http://arxiv.org/abs/2007.08403}, doi = {https://doi.org/10.1137/20M1334966}, pages = {460 -- 477}, year = {2021}, abstract = {The aim of this paper is to investigate the rebinding effect, a phenomenon describing a "short-time memory" which can occur when projecting a Markov process onto a smaller state space. For guaranteeing a correct mapping by the Markov State Model, we assume a fuzzy clustering in terms of membership functions, assigning degrees of membership to each state. The macro states are represented by the membership functions and may be overlapping. The magnitude of this overlap is a measure for the strength of the rebinding effect, caused by the projection and stabilizing the system. A minimal bound for the rebinding effect included in a given system is computed as the solution of an optimization problem. Based on membership functions chosen as a linear combination of Schur vectors, this generalized approach includes reversible as well as non-reversible processes.}, 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} }