1
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Kanekal KH, Rudzinski JF, Bereau T. Broad chemical transferability in structure-based coarse-graining. J Chem Phys 2022; 157:104102. [DOI: 10.1063/5.0104914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher-resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parametrizations. Here we present an approach to reconcile bottom-up, structure-preserving CG models with chemical transferability. We consider the bottom-up CG parametrization of 3,441 C7O2 small-molecule isomers. Our approach combines atomic representations, unsupervised learning, and a large-scale extended-ensemble force-matching parametrization. We first identify a subset of 19 representative molecules, which maximally encode the local environment of all gas-phase conformers. Reference interactions between the 19 representative molecules were obtained from both homogeneous bulk liquids and various binary mixtures. An extended-ensemble parametrization over all 703 state points leads to a CG model that is both structure-based and chemically transferable. Remarkably, the resulting force field is on average more structurally accurate than single-state-point equivalents. Averaging over the extended ensemble acts as a mean-force regularizer, smoothing out both force and structural correlations that are overly specific to a single state point. Our approach aims at transferability through a set of CG bead types that can be used to easily construct new molecules, while retaining the benefits of a structure-based parametrization.
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Affiliation(s)
- Kiran H. Kanekal
- AK Kremer - Theory Group, Max Planck Institute for Polymer Research, Germany
| | | | - Tristan Bereau
- Van 't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Netherlands
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2
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Marrink SJ, Monticelli L, Melo MN, Alessandri R, Tieleman DP, Souza PCT. Two decades of Martini: Better beads, broader scope. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1620] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Siewert J. Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials University of Groningen Groningen The Netherlands
| | - Luca Monticelli
- Molecular Microbiology and Structural Biochemistry (MMSB ‐ UMR 5086) CNRS & University of Lyon Lyon France
| | - Manuel N. Melo
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa Oeiras Portugal
| | - Riccardo Alessandri
- Pritzker School of Molecular Engineering University of Chicago Chicago Illinois USA
| | - D. Peter Tieleman
- Centre for Molecular Simulation and Department of Biological Sciences University of Calgary Alberta Canada
| | - Paulo C. T. Souza
- Molecular Microbiology and Structural Biochemistry (MMSB ‐ UMR 5086) CNRS & University of Lyon Lyon France
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3
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Mohr B, Shmilovich K, Kleinwächter IS, Schneider D, Ferguson AL, Bereau T. Data-driven discovery of cardiolipin-selective small molecules by computational active learning. Chem Sci 2022; 13:4498-4511. [PMID: 35656132 PMCID: PMC9019913 DOI: 10.1039/d2sc00116k] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/24/2022] [Indexed: 12/23/2022] Open
Abstract
Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design.
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Affiliation(s)
- Bernadette Mohr
- Van't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam Amsterdam 1098 XH The Netherlands
| | - Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA
| | - Isabel S Kleinwächter
- Department of Chemistry - Biochemistry, Johannes Gutenberg University Mainz 55128 Mainz Germany
| | - Dirk Schneider
- Department of Chemistry - Biochemistry, Johannes Gutenberg University Mainz 55128 Mainz Germany
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago Chicago Illinois 60637 USA
| | - Tristan Bereau
- Van't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam Amsterdam 1098 XH The Netherlands
- Max Planck Institute for Polymer Research 55128 Mainz Germany
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4
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Potter T, Barrett EL, Miller MA. Automated Coarse-Grained Mapping Algorithm for the Martini Force Field and Benchmarks for Membrane-Water Partitioning. J Chem Theory Comput 2021; 17:5777-5791. [PMID: 34472843 PMCID: PMC8444346 DOI: 10.1021/acs.jctc.1c00322] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Indexed: 01/08/2023]
Abstract
With a view to high-throughput simulations, we present an automated system for mapping and parameterizing organic molecules for use with the coarse-grained Martini force field. The method scales to larger molecules and a broader chemical space than existing schemes. The core of the mapping process is a graph-based analysis of the molecule's bonding network, which has the advantages of being fast, general, and preserving symmetry. The parameterization process pays special attention to coarse-grained beads in aromatic rings. It also includes a method for building efficient and stable frameworks of constraints for molecules with structural rigidity. The performance of the method is tested on a diverse set of 87 neutral organic molecules and the ability of the resulting models to capture octanol-water and membrane-water partition coefficients. In the latter case, we introduce an adaptive method for extracting partition coefficients from free-energy profiles to take into account the interfacial region of the membrane. We also use the models to probe the response of membrane-water partitioning to the cholesterol content of the membrane.
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Affiliation(s)
- Thomas
D. Potter
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, United
Kingdom
| | - Elin L. Barrett
- Unilever
Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Mark A. Miller
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, United
Kingdom
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5
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Dutta A, Vreeken J, Ghiringhelli LM, Bereau T. Data-driven equation for drug-membrane permeability across drugs and membranes. J Chem Phys 2021; 154:244114. [PMID: 34241352 DOI: 10.1063/5.0053931] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 106 small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.
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Affiliation(s)
- Arghya Dutta
- Max Planck Institute for Polymer Research, Mainz, Germany
| | - Jilles Vreeken
- CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
| | - Luca M Ghiringhelli
- The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany
| | - Tristan Bereau
- Max Planck Institute for Polymer Research, Mainz, Germany
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6
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Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-Inspired Structural Representations for Molecules and Materials. Chem Rev 2021; 121:9759-9815. [PMID: 34310133 DOI: 10.1021/acs.chemrev.1c00021] [Citation(s) in RCA: 145] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
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Affiliation(s)
- Felix Musil
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Andrea Grisafi
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Albert P Bartók
- Department of Physics and Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Christoph Ortner
- University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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7
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Weinreich J, Browning NJ, von Lilienfeld OA. Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation. J Chem Phys 2021; 154:134113. [PMID: 33832231 DOI: 10.1063/5.0041548] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes, or homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling of configurational space. Using the FreeSolv database, FML's out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental uncertainty (0.6 kcal/mol) is reached after training on 490 molecules (80% of FreeSolv). Corresponding FML model errors are on par with state-of-the art physics based approaches. To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs. We showcase its usefulness through analysis of solvation free energies for 116k organic molecules (all force-field compatible molecules in the QM9 database), identifying the most and least solvated systems and rediscovering quasi-linear structure-property relationships in terms of simple descriptors such as hydrogen-bond donors, number of NH or OH groups, number of oxygen atoms in hydrocarbons, and number of heavy atoms. FML's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. The sampling time for the representation converges rapidly with respect to the prediction error.
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Affiliation(s)
- Jan Weinreich
- University of Vienna, Faculty of Physics, Kolingasse 14-16, AT-1090 Wien, Austria
| | - Nicholas J Browning
- Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
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8
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Empereur-Mot C, Pesce L, Doni G, Bochicchio D, Capelli R, Perego C, Pavan GM. Swarm-CG: Automatic Parametrization of Bonded Terms in MARTINI-Based Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization. ACS OMEGA 2020; 5:32823-32843. [PMID: 33376921 PMCID: PMC7758974 DOI: 10.1021/acsomega.0c05469] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 11/26/2020] [Indexed: 05/23/2023]
Abstract
We present Swarm-CG, a versatile software for the automatic iterative parametrization of bonded parameters in coarse-grained (CG) models, ideal in combination with popular CG force fields such as MARTINI. By coupling fuzzy self-tuning particle swarm optimization to Boltzmann inversion, Swarm-CG performs accurate bottom-up parametrization of bonded terms in CG models composed of up to 200 pseudo atoms within 4-24 h on standard desktop machines, using default settings. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of complex molecular systems interesting for bio- and nanotechnology. Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity, and size. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.
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Affiliation(s)
- Charly Empereur-Mot
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland
| | - Luca Pesce
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland
| | - Giovanni Doni
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland
| | - Davide Bochicchio
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland
| | - Riccardo Capelli
- Department of Applied Science and Techology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Claudio Perego
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland
| | - Giovanni M. Pavan
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland
- Department of Applied Science and Techology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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9
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Shen K, Sherck N, Nguyen M, Yoo B, Köhler S, Speros J, Delaney KT, Fredrickson GH, Shell MS. Learning composition-transferable coarse-grained models: Designing external potential ensembles to maximize thermodynamic information. J Chem Phys 2020; 153:154116. [DOI: 10.1063/5.0022808] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Kevin Shen
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Nicholas Sherck
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - My Nguyen
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - Brian Yoo
- BASF Corporation, Tarrytown, New York 10591, USA
| | | | - Joshua Speros
- California Research Alliance (CARA) by BASF, Berkeley, California 94720, USA
| | - Kris T. Delaney
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Glenn H. Fredrickson
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
- Department of Materials Engineering, University of California, Santa Barbara, California 93106, USA
| | - M. Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
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10
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Chakraborty M, Xu J, White AD. Is preservation of symmetry necessary for coarse-graining? Phys Chem Chem Phys 2020; 22:14998-15005. [DOI: 10.1039/d0cp02309d] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This work investigates if preserving the symmetry of the underlying molecular graph of a given molecule when choosing a coarse-grained (CG) mapping significantly affects the CG model accuracy.
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Affiliation(s)
| | - Jinyu Xu
- Department of Chemical Engineering
- University of Rochester
- Rochester
- USA
| | - Andrew D. White
- Department of Chemical Engineering
- University of Rochester
- Rochester
- USA
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