Building an ab initio solvated DNA model using Euclidean neural networks

Lee, Alex J.; Rackers, Joshua A.; Pathak, Shivesh; Bricker, William P.; Dubey, ed., Kshatresh Dutta

doi:10.1371/journal.pone.0297502

Title: Building an ab initio solvated DNA model using Euclidean neural networks

Journal Article · 14 February 2024 · PLoS ONE

DOI: https://doi.org/10.1371/journal.pone.0297502 · OSTI ID:2305563

Lee, Alex J.;

; Pathak, Shivesh;

; Dubey, ed., Kshatresh Dutta

Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine-learned electron density model based on a Euclidean neural network framework that includes a built-in understanding of equivariance to model explicitly solvated double-stranded DNA. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level.

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Sponsoring Organization:: USDOE

OSTI ID:: 2305563

Alternate ID(s):: OSTI ID: 2311440

Journal Information:: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 2 Vol. 19; ISSN 1932-6203

Publisher:: Public Library of Science (PLoS)Copyright Statement

Country of Publication:: United States

Language:: English

References (53)

DNA structure and function Travers, Andrew; Muskhelishvili, Georgi FEBS Journal, Vol. 282, Issue 12 https://doi.org/10.1111/febs.13307	journal	June 2015
Accurate Coulomb-fitting basis sets for H to Rn Weigend, Florian Physical Chemistry Chemical Physics, Vol. 8, Issue 9 https://doi.org/10.1039/b515623h	journal	January 2006
On the role of gradients for machine learning of molecular energies and forces Christensen, Anders S.; von Lilienfeld, O. Anatole Machine Learning: Science and Technology, Vol. 1, Issue 4 https://doi.org/10.1088/2632-2153/abba6f	journal	October 2020
Density functional theory is straying from the path toward the exact functional Medvedev, Michael G.; Bushmarinov, Ivan S.; Sun, Jianwei Science, Vol. 355, Issue 6320 https://doi.org/10.1126/science.aah5975	journal	January 2017
Crystal structure of the MATa1/MATalpha2 homeodomain heterodimer in complex with DNA containing an A-tract Li, T. Nucleic Acids Research, Vol. 26, Issue 24 https://doi.org/10.1093/nar/26.24.5707	journal	December 1998
The missing term in effective pair potentials Berendsen, H. J. C.; Grigera, J. R.; Straatsma, T. P. The Journal of Physical Chemistry, Vol. 91, Issue 24 https://doi.org/10.1021/j100308a038	journal	November 1987
Quantum chemical accuracy from density functional approximations via machine learning Bogojeski, Mihail; Vogt-Maranto, Leslie; Tuckerman, Mark E. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19093-1	journal	October 2020
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials Gao, Xiang; Ramezanghorbani, Farhad; Isayev, Olexandr Journal of Chemical Information and Modeling, Vol. 60, Issue 7 https://doi.org/10.1021/acs.jcim.0c00451	journal	June 2020
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials Batzner, Simon; Musaelian, Albert; Sun, Lixin Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29939-5	journal	May 2022
Electron density learning of non-covalent systems Fabrizio, Alberto; Grisafi, Andrea; Meyer, Benjamin Chemical Science, Vol. 10, Issue 41 https://doi.org/10.1039/C9SC02696G	journal	January 2019
Learning local equivariant representations for large-scale atomistic dynamics Musaelian, Albert; Batzner, Simon; Johansson, Anders Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-36329-y	journal	February 2023
An Interpretation of the Enhancement of the Water Dipole Moment Due to the Presence of Other Water Molecules Kemp, Daniel D.; Gordon, Mark S. The Journal of Physical Chemistry A, Vol. 112, Issue 22 https://doi.org/10.1021/jp801921f	journal	June 2008
Rationale for mixing exact exchange with density functional approximations Perdew, John P.; Ernzerhof, Matthias; Burke, Kieron The Journal of Chemical Physics, Vol. 105, Issue 22, p. 9982-9985 https://doi.org/10.1063/1.472933	journal	December 1996
Euclidean Symmetry and Equivariance in Machine Learning Smidt, Tess E. Trends in Chemistry, Vol. 3, Issue 2 https://doi.org/10.1016/j.trechm.2020.10.006	journal	February 2021
Predicting DNA duplex stability from the base sequence. Breslauer, K. J.; Frank, R.; Blocker, H. Proceedings of the National Academy of Sciences, Vol. 83, Issue 11 https://doi.org/10.1073/pnas.83.11.3746	journal	June 1986
Predicting accurate ab initio DNA electron densities with equivariant neural networks Lee, Alex J.; Rackers, Joshua A.; Bricker, William P. Biophysical Journal, Vol. 121, Issue 20 https://doi.org/10.1016/j.bpj.2022.08.045	journal	October 2022
Genome-wide prediction of minor-groove electrostatic potential enables biophysical modeling of protein–DNA binding Chiu, Tsu-Pei; Rao, Satyanarayan; Mann, Richard S. Nucleic Acids Research, Vol. 45, Issue 21 https://doi.org/10.1093/nar/gkx915	journal	October 2017
PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data Roe, Daniel R.; Cheatham, Thomas E. Journal of Chemical Theory and Computation, Vol. 9, Issue 7 https://doi.org/10.1021/ct400341p	journal	June 2013
A recipe for cracking the quantum scaling limit with machine learned electron densities Rackers, Joshua A.; Tecot, Lucas; Geiger, Mario Machine Learning: Science and Technology, Vol. 4, Issue 1 https://doi.org/10.1088/2632-2153/acb314	journal	February 2023
The unique structure of A-tracts and intrinsic DNA bending Haran, Tali E.; Mohanty, Udayan Quarterly Reviews of Biophysics, Vol. 42, Issue 1 https://doi.org/10.1017/S0033583509004752	journal	February 2009
Learning Electron Densities in the Condensed Phase Lewis, Alan M.; Grisafi, Andrea; Ceriotti, Michele Journal of Chemical Theory and Computation, Vol. 17, Issue 11 https://doi.org/10.1021/acs.jctc.1c00576	journal	October 2021
Explicit solvation modulates intra- and inter-molecular interactions within DNA: Electronic aspects revealed by the ab initio fragment molecular orbital (FMO) method Fukuzawa, Kaori; Kurisaki, Ikuo; Watanabe, Chiduru Computational and Theoretical Chemistry, Vol. 1054 https://doi.org/10.1016/j.comptc.2014.11.020	journal	February 2015
Psi4: an open-source ab initio electronic structure program : Psi4: an electronic structure program Turney, Justin M.; Simmonett, Andrew C.; Parrish, Robert M. Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 2, Issue 4 https://doi.org/10.1002/wcms.93	journal	October 2011
High-Strain-Rate Plastic Flow Studied via Nonequilibrium Molecular Dynamics Hoover, William G.; Ladd, Anthony J. C.; Moran, Bill Physical Review Letters, Vol. 48, Issue 26 https://doi.org/10.1103/PhysRevLett.48.1818	journal	June 1982
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids Jørgensen, Peter Bjørn; Bhowmik, Arghya npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00863-y	journal	August 2022
Specific minor groove solvation is a crucial determinant of DNA binding site recognition Harris, Lydia-Ann; Williams, Loren Dean; Koudelka, Gerald B. Nucleic Acids Research, Vol. 42, Issue 22 https://doi.org/10.1093/nar/gku1259	journal	November 2014
Electronic-Structure Properties from Atom-Centered Predictions of the Electron Density Grisafi, Andrea; Lewis, Alan M.; Rossi, Mariana Journal of Chemical Theory and Computation, Vol. 19, Issue 14 https://doi.org/10.1021/acs.jctc.2c00850	journal	December 2022
The role of DNA shape in protein–DNA recognition Rohs, Remo; West, Sean M.; Sosinsky, Alona Nature, Vol. 461, Issue 7268 https://doi.org/10.1038/nature08473	journal	October 2009
Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing Cuevas-Zuviría, Bruno; Pacios, Luis F. Journal of Chemical Information and Modeling, Vol. 61, Issue 6 https://doi.org/10.1021/acs.jcim.1c00227	journal	May 2021
Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks Zepeda-Núñez, Leonardo; Chen, Yixiao; Zhang, Jiefu Journal of Computational Physics, Vol. 443 https://doi.org/10.1016/j.jcp.2021.110523	journal	October 2021
Finding symmetry breaking order parameters with Euclidean neural networks Smidt, Tess E.; Geiger, Mario; Miller, Benjamin Kurt Physical Review Research, Vol. 3, Issue 1 https://doi.org/10.1103/PhysRevResearch.3.L012002	journal	January 2021
Bypassing the Kohn-Sham equations with machine learning Brockherde, Felix; Vogt, Leslie; Li, Li Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/s41467-017-00839-3	journal	October 2017
The Water Dipole Moment in Water Clusters Gregory, J. K. Science, Vol. 275, Issue 5301 https://doi.org/10.1126/science.275.5301.814	journal	February 1997
Refinement of the AMBER Force Field for Nucleic Acids: Improving the Description of α/γ Conformers Pérez, Alberto; Marchán, Iván; Svozil, Daniel Biophysical Journal, Vol. 92, Issue 11 https://doi.org/10.1529/biophysj.106.097782	journal	June 2007
Molecular dynamics with coupling to an external bath Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F. The Journal of Chemical Physics, Vol. 81, Issue 8 https://doi.org/10.1063/1.448118	journal	October 1984
New Basis Set Exchange: An Open, Up-to-Date Resource for the Molecular Sciences Community Pritchard, Benjamin P.; Altarawy, Doaa; Didier, Brett Journal of Chemical Information and Modeling, Vol. 59, Issue 11 https://doi.org/10.1021/acs.jcim.9b00725	journal	October 2019
Pushing the frontiers of density functionals by solving the fractional electron problem Kirkpatrick, James; McMorrow, Brendan; Turban, David H. P. Science, Vol. 374, Issue 6573 https://doi.org/10.1126/science.abj6511	journal	December 2021
Machine Learning Force Fields Unke, Oliver T.; Chmiela, Stefan; Sauceda, Huziel E. Chemical Reviews https://doi.org/10.1021/acs.chemrev.0c01111	journal	March 2021
VMD: Visual molecular dynamics Humphrey, William; Dalke, Andrew; Schulten, Klaus Journal of Molecular Graphics, Vol. 14, Issue 1 https://doi.org/10.1016/0263-7855(96)00018-5	journal	February 1996
Understanding Nucleic Acid–Ion Interactions Lipfert, Jan; Doniach, Sebastian; Das, Rhiju Annual Review of Biochemistry, Vol. 83, Issue 1 https://doi.org/10.1146/annurev-biochem-060409-092720	journal	June 2014
Parmbsc1: a refined force field for DNA simulations Ivani, Ivan; Dans, Pablo D.; Noy, Agnes Nature Methods, Vol. 13, Issue 1 https://doi.org/10.1038/nmeth.3658	journal	November 2015
Accurate Hellmann–Feynman forces from density functional calculations with augmented Gaussian basis sets Pathak, Shivesh; López, Ignacio Ema; Lee, Alex J. The Journal of Chemical Physics, Vol. 158, Issue 1 https://doi.org/10.1063/5.0130668	journal	January 2023
Completing density functional theory by machine learning hidden messages from molecules Nagai, Ryo; Akashi, Ryosuke; Sugino, Osamu npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0310-0	journal	May 2020
UCSF Chimera?A visualization system for exploratory research and analysis Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C. Journal of Computational Chemistry, Vol. 25, Issue 13 https://doi.org/10.1002/jcc.20084	journal	January 2004
Computer ‘‘experiment’’ for nonlinear thermodynamics of Couette flow Evans, D. J. The Journal of Chemical Physics, Vol. 78, Issue 6 https://doi.org/10.1063/1.445195	journal	March 1983
Applications of large-scale density functional theory in biology Cole, Daniel J.; Hine, Nicholas D. M. Journal of Physics: Condensed Matter, Vol. 28, Issue 39 https://doi.org/10.1088/0953-8984/28/39/393001	journal	August 2016
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer Ko, Tsz Wai; Finkler, Jonas A.; Goedecker, Stefan Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-020-20427-2	journal	January 2021
Understanding the Sequence-Dependence of DNA Groove Dimensions: Implications for DNA Interactions Oguey, Christophe; Foloppe, Nicolas; Hartmann, Brigitte PLoS ONE, Vol. 5, Issue 12 https://doi.org/10.1371/journal.pone.0015931	journal	December 2010
Investigations of Stacked DNA Base-Pair Steps: Highly Accurate Stacking Interaction Energies, Energy Decomposition, and Many-Body Stacking Effects Kruse, Holger; Banáš, Pavel; Šponer, Jiřı́ Journal of Chemical Theory and Computation, Vol. 15, Issue 1 https://doi.org/10.1021/acs.jctc.8b00643	journal	November 2018
Comparison of simple potential functions for simulating liquid water Jorgensen, William L.; Chandrasekhar, Jayaraman; Madura, Jeffry D. The Journal of Chemical Physics, Vol. 79, Issue 2 https://doi.org/10.1063/1.445869	journal	July 1983
Solving the electronic structure problem with machine learning Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0162-7	journal	February 2019
Multi-scale approach for the prediction of atomic scale properties Grisafi, Andrea; Nigam, Jigyasa; Ceriotti, Michele Chemical Science, Vol. 12, Issue 6 https://doi.org/10.1039/D0SC04934D	journal	January 2021
Transferable Machine-Learning Model of the Electron Density Grisafi, Andrea; Fabrizio, Alberto; Meyer, Benjamin ACS Central Science, Vol. 5, Issue 1 https://doi.org/10.1021/acscentsci.8b00551	journal	December 2018

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