TY - JOUR A1 - Senne, M. A1 - Trendelkamp-Schroer, B. A1 - Mey, A. A1 - Schütte, Christof A1 - Noé, Frank T1 - EMMA - A software package for Markov model building and analysis JF - Journal of Chemical Theory and Computation Y1 - 2012 U6 - https://doi.org/10.1021/ct300274u VL - 8 SP - 2223 EP - 2238 ER - TY - GEN A1 - Schütte, Christof A1 - Deuflhard, Peter A1 - Noé, Frank A1 - Weber, Marcus ED - Deuflhard, Peter ED - Grötschel, Martin ED - Hömberg, Dietmar ED - Horst, Ulrich ED - Kramer, Jürg ED - Mehrmann, Volker ED - Polthier, Konrad ED - Schmidt, Frank ED - Schütte, Christof ED - Skutella, Martin ED - Sprekels, Jürgen T1 - Design of functional molecules T2 - MATHEON-Mathematics for Key Technologies Y1 - 2014 VL - 1 SP - 49 EP - 65 PB - European Mathematical Society ER - TY - JOUR A1 - Koltai, Peter A1 - Wu, Hao A1 - Noé, Frank A1 - Schütte, Christof T1 - Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics JF - Computation Y1 - 2018 U6 - https://doi.org/10.3390/computation6010022 VL - 6 IS - 1 PB - MDPI CY - Basel, Switzerland ER - TY - JOUR A1 - Dibak, Manuel A1 - del Razo, Mauricio J. A1 - de Sancho, David A1 - Schütte, Christof A1 - Noé, Frank T1 - MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations JF - Journal of Chemical Physics N2 - Molecular dynamics (MD) simulations can model the interactions between macromolecules with high spatiotemporal resolution but at a high computational cost. By combining high-throughput MD with Markov state models (MSMs), it is now possible to obtain long time-scale behavior of small to intermediate biomolecules and complexes. To model the interactions of many molecules at large length scales, particle-based reaction-diffusion (RD) simulations are more suitable but lack molecular detail. Thus, coupling MSMs and RD simulations (MSM/RD) would be highly desirable, as they could efficiently produce simulations at large time and length scales, while still conserving the characteristic features of the interactions observed at atomic detail. While such a coupling seems straightforward, fundamental questions are still open: Which definition of MSM states is suitable? Which protocol to merge and split RD particles in an association/dissociation reaction will conserve the correct bimolecular kinetics and thermodynamics? In this paper, we make the first step toward MSM/RD by laying out a general theory of coupling and proposing a first implementation for association/dissociation of a protein with a small ligand (A + B ⇌ C). Applications on a toy model and CO diffusion into the heme cavity of myoglobin are reported. Y1 - 2018 U6 - https://doi.org/10.1063/1.5020294 VL - 148 IS - 21 ER - TY - JOUR A1 - Klus, Stefan A1 - Nüske, Feliks A1 - Koltai, Peter A1 - Wu, Hao A1 - Kevrekidis, Ioannis A1 - Schütte, Christof A1 - Noé, Frank T1 - Data-driven model reduction and transfer operator approximation JF - Journal of Nonlinear Science Y1 - 2018 UR - https://link.springer.com/article/10.1007/s00332-017-9437-7 U6 - https://doi.org/10.1007/s00332-017-9437-7 VL - 28 IS - 3 SP - 985 EP - 1010 ER - TY - JOUR A1 - Kostre, Margarita A1 - Schütte, Christof A1 - Noé, Frank A1 - del Razo Sarmina, Mauricio T1 - Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs JF - Multiscale Modeling & Simulation N2 - Open biochemical systems of interacting molecules are ubiquitous in life-related processes. However, established computational methodologies, like molecular dynamics, are still mostly constrained to closed systems and timescales too small to be relevant for life processes. Alternatively, particle-based reaction-diffusion models are currently the most accurate and computationally feasible approach at these scales. Their efficiency lies in modeling entire molecules as particles that can diffuse and interact with each other. In this work, we develop modeling and numerical schemes for particle-based reaction-diffusion in an open setting, where the reservoirs are mediated by reaction-diffusion PDEs. We derive two important theoretical results. The first one is the mean-field for open systems of diffusing particles; the second one is the mean-field for a particle-based reaction-diffusion system with second-order reactions. We employ these two results to develop a numerical scheme that consistently couples particle-based reaction-diffusion processes with reaction-diffusion PDEs. This allows modeling open biochemical systems in contact with reservoirs that are time-dependent and spatially inhomogeneous, as in many relevant real-world applications. Y1 - 2021 U6 - https://doi.org/10.1137/20M1352739 VL - 19 IS - 4 SP - 1659 EP - 1683 PB - Society for Industrial and Applied Mathematics ER - TY - JOUR A1 - Dibak, Manuel A1 - Fröhner, Christoph A1 - Noé, Frank A1 - Höfling, Felix T1 - Diffusion-influenced reaction rates in the presence of pair interactions JF - The Journal of Chemical Physics N2 - The kinetics of bimolecular reactions in solution depends, among other factors, on intermolecular forces such as steric repulsion or electrostatic interaction. Microscopically, a pair of molecules first has to meet by diffusion before the reaction can take place. In this work, we establish an extension of Doi’s volume reaction model to molecules interacting via pair potentials, which is a key ingredient for interacting-particle-based reaction–diffusion (iPRD) simulations. As a central result, we relate model parameters and macroscopic reaction rate constants in this situation. We solve the corresponding reaction–diffusion equation in the steady state and derive semi- analytical expressions for the reaction rate constant and the local concentration profiles. Our results apply to the full spectrum from well-mixed to diffusion-limited kinetics. For limiting cases, we give explicit formulas, and we provide a computationally inexpensive numerical scheme for the general case, including the intermediate, diffusion-influenced regime. The obtained rate constants decompose uniquely into encounter and formation rates, and we discuss the effect of the potential on both subprocesses, exemplified for a soft harmonic repulsion and a Lennard-Jones potential. The analysis is complemented by extensive stochastic iPRD simulations, and we find excellent agreement with the theoretical predictions. Y1 - 2019 U6 - https://doi.org/10.1063/1.5124728 VL - 151 SP - 164105 ER - TY - JOUR A1 - del Razo, Mauricio J. A1 - Dibak, Manuel A1 - Schütte, Christof A1 - Noé, Frank T1 - Multiscale molecular kinetics by coupling Markov state models and reaction-diffusion dynamics JF - The Journal of Chemical Physics Y1 - 2021 U6 - https://doi.org/10.1063/5.0060314 VL - 155 IS - 12 ER - TY - JOUR A1 - Dibak, Manuel A1 - J. del Razo, Mauricio A1 - De Sancho, David A1 - Schütte, Christof A1 - Noé, Frank T1 - MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations JF - Journal of Chemical Physics N2 - Molecular dynamics (MD) simulations can model the interactions between macromolecules with high spatiotemporal resolution but at a high computational cost. By combining high-throughput MD with Markov state models (MSMs), it is now possible to obtain long-timescale behavior of small to intermediate biomolecules and complexes. To model the interactions of many molecules at large lengthscales, particle-based reaction-diffusion (RD) simulations are more suitable but lack molecular detail. Thus, coupling MSMs and RD simulations (MSM/RD) would be highly desirable, as they could efficiently produce simulations at large time- and lengthscales, while still conserving the characteristic features of the interactions observed at atomic detail. While such a coupling seems straightforward, fundamental questions are still open: Which definition of MSM states is suitable? Which protocol to merge and split RD particles in an association/dissociation reaction will conserve the correct bimolecular kinetics and thermodynamics? In this paper, we make the first step towards MSM/RD by laying out a general theory of coupling and proposing a first implementation for association/dissociation of a protein with a small ligand (A + B <--> C). Applications on a toy model and CO diffusion into the heme cavity of myoglobin are reported. Y1 - 2018 U6 - https://doi.org/10.1063/1.5020294 VL - 148 IS - 214107 ER - TY - JOUR A1 - Klus, Stefan A1 - Husic, Brooke E. A1 - Mollenhauer, Mattes A1 - Noe, Frank T1 - Kernel methods for detecting coherent structures in dynamical data JF - Chaos: An Interdisciplinary Journal of Nonlinear Science Y1 - 2019 U6 - https://doi.org/10.1063/1.5100267 VL - 29 IS - 12 ER - TY - JOUR A1 - Noé, Frank A1 - Schütte, Christof A1 - Vanden-Eijnden, E. A1 - Reich, L. A1 - Weikl, T. T1 - Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations JF - Proc. Natl. Acad. Sci. USA Y1 - 2009 UR - http://publications.imp.fu-berlin.de/826/ U6 - https://doi.org/10.1073/pnas.0905466106 VL - 106 IS - 45 SP - 19011 EP - 19016 ER - TY - JOUR A1 - Metzner, Ph. A1 - Noé, Frank A1 - Schütte, Christof T1 - Estimating the Sampling Error JF - Phys. Rev. E Y1 - 2009 UR - http://publications.imp.fu-berlin.de/17/ U6 - https://doi.org/10.1103/PhysRevE.80.021106 VL - 80 IS - 2 SP - 021106 PB - American Physical Society ER - TY - JOUR A1 - Sarich, Marco A1 - Noé, Frank A1 - Schütte, Christof T1 - On the Approximation Quality of Markov State Models JF - Multiscale Model. Simul. Y1 - 2010 UR - http://publications.imp.fu-berlin.de/771/ U6 - https://doi.org/10.1137/090764049 VL - 8 IS - 4 SP - 1154 EP - 1177 ER - TY - JOUR A1 - Prinz, J.-H. A1 - Wu, Hao A1 - Sarich, Marco A1 - Keller, B. A1 - Fischbach, M. A1 - Held, M. A1 - Chodera, J. A1 - Schütte, Christof A1 - Noé, Frank T1 - Markov models of molecular kinetics JF - J. Chem. Phys. Y1 - 2011 UR - http://publications.imp.fu-berlin.de/944/ U6 - https://doi.org/10.1063/1.3565032 VL - 134 SP - 174105 ER - TY - JOUR A1 - Schütte, Christof A1 - Noé, Frank A1 - Lu, Jianfeng A1 - Sarich, Marco A1 - Vanden-Eijnden, E. T1 - Markov State Models Based on Milestoning JF - J. Chem. Phys. Y1 - 2011 UR - http://publications.imp.fu-berlin.de/1022/ U6 - https://doi.org/10.1063/1.3590108 VL - 134 IS - 20 SP - 204105 ER - TY - JOUR A1 - Noé, Frank A1 - Horenko, Illia A1 - Schütte, Christof A1 - Smith, J. T1 - Hierarchical Analysis of Conformational Dynamics in Biomolecules JF - J. Chem. Phys. Y1 - 2007 UR - http://publications.imp.fu-berlin.de/37/ U6 - https://doi.org/10.1063/1.2714539 VL - 126 IS - 15 SP - 155102 ER - TY - JOUR A1 - Noé, Frank A1 - Smith, J. A1 - Schütte, Christof T1 - A network-based approach to biomolecular dynamics JF - From Computational Biophysics to Systems Biology (CBSB07). Editors Y1 - 2007 UR - http://publications.imp.fu-berlin.de/319/ VL - NIC Series 36 PB - John von Neumann Institute for Computing, Jülich ER - TY - JOUR A1 - Horenko, Illia A1 - Hartmann, Carsten A1 - Schütte, Christof A1 - Noé, Frank T1 - Data-based Parameter Estimation of Generalized Multidimensional Langevin Processes JF - Phys. Rev. E Y1 - 2007 UR - http://publications.imp.fu-berlin.de/31/ U6 - https://doi.org/10.1103/PhysRevE.76.016706 VL - 76 IS - 01 SP - 016706 ER - TY - JOUR A1 - Schütte, Christof A1 - Noé, Frank A1 - Meerbach, E. A1 - Metzner, Ph. A1 - Hartmann, Carsten T1 - Conformation Dynamics JF - Proceedings of the 6th International Congress on Industrial and Applied Mathematics, I. Jeltsch and G. Wanner (eds.), Y1 - 2009 UR - http://publications.imp.fu-berlin.de/110/ U6 - https://doi.org/10.4171/056-1/15 SP - 297 EP - 335 PB - EMS publishing house ER - TY - JOUR A1 - Charron, Nicholas A1 - Musil, Félix A1 - Guljas, Andrea A1 - Chen, Yaoyi A1 - Bonneau, Klara A1 - Pasos-Trejo, Aldo A1 - Jacopo, Venturin A1 - Daria, Gusew A1 - Zaporozhets, Iryna A1 - Krämer, Andreas A1 - Templeton, Clark A1 - Atharva, Kelkar A1 - Durumeric, Aleksander A1 - Olsson, Simon A1 - Pérez, Adrià A1 - Majewski, Maciej A1 - Husic, Brooke A1 - Patel, Ankit A1 - De Fabritiis, Gianni A1 - Noé, Frank A1 - Clementi, Cecilia T1 - Navigating protein landscapes with a machine-learned transferable coarse-grained model JF - Arxiv N2 - The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins. Y1 - 2023 U6 - https://doi.org/https://doi.org/10.48550/arXiv.2310.18278 ER - TY - JOUR A1 - Majewski, Maciej A1 - Pérez, Adrià A1 - Thölke, Philipp A1 - Doerr, Stefan A1 - Charron, Nicholas A1 - Giorgino, Toni A1 - Husic, Brooke A1 - Clementi, Cecilia A1 - Noé, Frank A1 - De Fabritiis, Gianni T1 - Machine learning coarse-grained potentials of protein thermodynamics JF - Nature Communications N2 - A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics. Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-41343-1 VL - 14 ER - TY - JOUR A1 - Durumeric, Aleksander A1 - Charron, Nicholas A1 - Templeton, Clark A1 - Musil, Félix A1 - Bonneau, Klara A1 - Pasos-Trejo, Aldo A1 - Chen, Yaoyi A1 - Kelkar, Atharva A1 - Noé, Frank A1 - Clementi, Cecilia T1 - Machine learned coarse-grained protein force-fields: Are we there yet? JF - Current Opinion in Structural Biology N2 - The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges. Y1 - 2023 U6 - https://doi.org/10.1016/j.sbi.2023.102533 VL - 79 ER - TY - JOUR A1 - Krämer, Andreas A1 - Durumeric, Aleksander A1 - Charron, Nicholas A1 - Chen, Yaoyi A1 - Clementi, Cecilia A1 - Noé, Frank T1 - Statistically optimal force aggregation for coarse-graining molecular dynamics JF - The Journal of Physical Chemistry Letters N2 - Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning bottom-up CG force fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force field on average. We show that there is flexibility in how to map all-atom forces to the CG representation and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins chignolin and tryptophan cage and published as open-source code. Y1 - 2023 U6 - https://doi.org/10.1021/acs.jpclett.3c00444 VL - 14 IS - 17 SP - 3970 EP - 3979 ER - TY - JOUR A1 - Schimunek, Johannes A1 - Seidl, Philipp A1 - Elez, Katarina A1 - Hempel, Tim A1 - Le, Tuan A1 - Noé, Frank A1 - Olsson, Simon A1 - Raich, Lluís A1 - Winter, Robin A1 - Gokcan, Hatice A1 - Gusev, Filipp A1 - Gutkin, Evgeny M. A1 - Isayev, Olexandr A1 - Kurnikova, Maria G. A1 - Narangoda, Chamali H. A1 - Zubatyuk, Roman A1 - Bosko, Ivan P. A1 - Furs, Konstantin V. A1 - Karpenko, Anna D. A1 - Kornoushenko, Yury V. A1 - Shuldau, Mikita A1 - Yushkevich, Artsemi A1 - Benabderrahmane, Mohammed B. A1 - Bousquet-Melou, Patrick A1 - Bureau, Ronan A1 - Charton, Beatrice A1 - Cirou, Bertrand C. A1 - Gil, Gérard A1 - Allen, William J. A1 - Sirimulla, Suman A1 - Watowich, Stanley A1 - Antonopoulos, Nick A1 - Epitropakis, Nikolaos A1 - Krasoulis, Agamemnon A1 - Itsikalis, Vassilis A1 - Theodorakis, Stavros A1 - Kozlovskii, Igor A1 - Maliutin, Anton A1 - Medvedev, Alexander A1 - Popov, Petr A1 - Zaretckii, Mark A1 - Eghbal-Zadeh, Hamid A1 - Halmich, Christina A1 - Hochreiter, Sepp A1 - Mayr, Andreas A1 - Ruch, Peter A1 - Widrich, Michael A1 - Berenger, Francois A1 - Kumar, Ashutosh A1 - Yamanishi, Yoshihiro A1 - Zhang, Kam Y. J. A1 - Bengio, Emmanuel A1 - Bengio, Yoshua A1 - Jain, Moksh J. A1 - Korablyov, Maksym A1 - Liu, Cheng-Hao A1 - Marcou, Gilles A1 - Glaab, Enrico A1 - Barnsley, Kelly A1 - Iyengar, Suhasini M. A1 - Ondrechen, Mary Jo A1 - Haupt, V. Joachim A1 - Kaiser, Florian A1 - Schroeder, Michael A1 - Pugliese, Luisa A1 - Albani, Simone A1 - Athanasiou, Christina A1 - Beccari, Andrea A1 - Carloni, Paolo A1 - D’Arrigo, Giulia A1 - Gianquinto, Eleonora A1 - Goßen, Jonas A1 - Hanke, Anton A1 - Joseph, Benjamin P. A1 - Kokh, Daria B. A1 - Kovachka, Sandra A1 - Manelfi, Candida A1 - Mukherjee, Goutam A1 - Muñiz-Chicharro, Abraham A1 - Musiani, Francesco A1 - Nunes-Alves, Ariane A1 - Paiardi, Giulia A1 - Rossetti, Giulia A1 - Sadiq, S. Kashif A1 - Spyrakis, Francesca A1 - Talarico, Carmine A1 - Tsengenes, Alexandros A1 - Wade, Rebecca C. A1 - Copeland, Conner A1 - Gaiser, Jeremiah A1 - Olson, Daniel R. A1 - Roy, Amitava A1 - Venkatraman, Vishwesh A1 - Wheeler, Travis J. A1 - Arthanari, Haribabu A1 - Blaschitz, Klara A1 - Cespugli, Marco A1 - Durmaz, Vedat A1 - Fackeldey, Konstantin A1 - Fischer, Patrick D. A1 - Gorgulla, Christoph A1 - Gruber, Christian A1 - Gruber, Karl A1 - Hetmann, Michael A1 - Kinney, Jamie E. A1 - Padmanabha Das, Krishna M. A1 - Pandita, Shreya A1 - Singh, Amit A1 - Steinkellner, Georg A1 - Tesseyre, Guilhem A1 - Wagner, Gerhard A1 - Wang, Zi-Fu A1 - Yust, Ryan J. A1 - Druzhilovskiy, Dmitry S. A1 - Filimonov, Dmitry A. A1 - Pogodin, Pavel V. A1 - Poroikov, Vladimir A1 - Rudik, Anastassia V. A1 - Stolbov, Leonid A. A1 - Veselovsky, Alexander V. A1 - De Rosa, Maria A1 - De Simone, Giada A1 - Gulotta, Maria R. A1 - Lombino, Jessica A1 - Mekni, Nedra A1 - Perricone, Ugo A1 - Casini, Arturo A1 - Embree, Amanda A1 - Gordon, D. Benjamin A1 - Lei, David A1 - Pratt, Katelin A1 - Voigt, Christopher A. A1 - Chen, Kuang-Yu A1 - Jacob, Yves A1 - Krischuns, Tim A1 - Lafaye, Pierre A1 - Zettor, Agnès A1 - Rodríguez, M. Luis A1 - White, Kris M. A1 - Fearon, Daren A1 - Von Delft, Frank A1 - Walsh, Martin A. A1 - Horvath, Dragos A1 - Brooks III, Charles L. A1 - Falsafi, Babak A1 - Ford, Bryan A1 - García-Sastre, Adolfo A1 - Yup Lee, Sang A1 - Naffakh, Nadia A1 - Varnek, Alexandre A1 - Klambauer, Günter A1 - Hermans, Thomas M. T1 - A community effort in SARS-CoV-2 drug discovery JF - Molecular Informatics KW - COVID-19 KW - drug discovery KW - machine learning KW - SARS-CoV-2 Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1002/minf.202300262 VL - 43 IS - 1 SP - e202300262 ER -