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Glick ZL, Metcalf DP, Glick CS, Spronk SA, Koutsoukas A, Cheney DL, Sherrill CD. A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy. Chem Sci 2024; 15:13313-13324. [PMID: 39183910 PMCID: PMC11339967 DOI: 10.1039/d4sc01029a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 07/09/2024] [Indexed: 08/27/2024] Open
Abstract
Quantifying intermolecular interactions with quantum chemistry (QC) is useful for many chemical problems, including understanding the nature of protein-ligand interactions. Unfortunately, QC computations on protein-ligand systems are too computationally expensive for most use cases. The flourishing field of machine-learned (ML) potentials is a promising solution, but it is limited by an inability to easily capture long range, non-local interactions. In this work we develop an atomic-pairwise neural network (AP-Net) specialized for modeling intermolecular interactions. This model benefits from a number of physical constraints, including a two-component equivariant message passing neural network architecture that predicts interaction energies via an intermediate prediction of monomer electron densities. The AP-Net model is trained on a comprehensive dataset composed of paired ligand and protein fragments. This model accurately predicts QC-quality interaction energies of protein-ligand systems at a computational cost reduced by orders of magnitude. Applications of the AP-Net model to molecular crystal structure prediction are explored, as well as limitations in modeling highly polarizable systems.
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Affiliation(s)
- Zachary L Glick
- School of Chemistry and Biochemistry, School of Computational Science and Engineering, Georgia Institute of Technology Atlanta Georgia 30332-0400 USA
| | - Derek P Metcalf
- School of Chemistry and Biochemistry, School of Computational Science and Engineering, Georgia Institute of Technology Atlanta Georgia 30332-0400 USA
| | - Caroline S Glick
- School of Chemistry and Biochemistry, School of Computational Science and Engineering, Georgia Institute of Technology Atlanta Georgia 30332-0400 USA
| | - Steven A Spronk
- Molecular Structure and Design, Bristol Myers Squibb Company P.O. Box 5400 Princeton New Jersey 08543 USA
| | - Alexios Koutsoukas
- Molecular Structure and Design, Bristol Myers Squibb Company P.O. Box 5400 Princeton New Jersey 08543 USA
| | - Daniel L Cheney
- Molecular Structure and Design, Bristol Myers Squibb Company P.O. Box 5400 Princeton New Jersey 08543 USA
| | - C David Sherrill
- School of Chemistry and Biochemistry, School of Computational Science and Engineering, Georgia Institute of Technology Atlanta Georgia 30332-0400 USA
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2
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Yang Y, Zhang S, Ranasinghe KD, Isayev O, Roitberg AE. Machine Learning of Reactive Potentials. Annu Rev Phys Chem 2024; 75:371-395. [PMID: 38941524 DOI: 10.1146/annurev-physchem-062123-024417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.
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Affiliation(s)
- Yinuo Yang
- Department of Chemistry, University of Florida, Gainesville, Florida;
| | - Shuhao Zhang
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | | | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | - Adrian E Roitberg
- Department of Chemistry, University of Florida, Gainesville, Florida;
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3
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Kaiser S, Yue Z, Peng Y, Nguyen TD, Chen S, Teng D, Voth GA. Molecular Dynamics Simulation of Complex Reactivity with the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) Software Package. J Phys Chem B 2024; 128:4959-4974. [PMID: 38742764 PMCID: PMC11129700 DOI: 10.1021/acs.jpcb.4c01987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/05/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
Simulating chemically reactive phenomena such as proton transport on nanosecond to microsecond and beyond time scales is a challenging task. Ab initio methods are unable to currently access these time scales routinely, and traditional molecular dynamics methods feature fixed bonding arrangements that cannot account for changes in the system's bonding topology. The Multiscale Reactive Molecular Dynamics (MS-RMD) method, as implemented in the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) software package for the LAMMPS molecular dynamics code, offers a method to routinely sample longer time scale reactive simulation data with statistical precision. RAPTOR may also be interfaced with enhanced sampling methods to drive simulations toward the analysis of reactive rare events, and a number of collective variables (CVs) have been developed to facilitate this. Key advances to this methodology, including GPU acceleration efforts and novel CVs to model water wire formation are reviewed, along with recent applications of the method which demonstrate its versatility and robustness.
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Affiliation(s)
- Scott Kaiser
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, James Franck
Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
| | - Zhi Yue
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, James Franck
Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
| | - Yuxing Peng
- NVIDIA
Corporation, Santa
Clara, California 95051, United States
| | - Trung Dac Nguyen
- Research
Computing Center, The University of Chicago, Chicago, Illinois 60637, United States
| | - Sijia Chen
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, James Franck
Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
| | - Da Teng
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, James Franck
Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
| | - Gregory A. Voth
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, James Franck
Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
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4
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Li Y, Zhai Y, Li H. MLRNet: Combining the Physics-Motivated Potential Models with Neural Networks for Intermolecular Potential Energy Surface Construction. J Chem Theory Comput 2023; 19:1421-1431. [PMID: 36826225 DOI: 10.1021/acs.jctc.2c01049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
A physics-based machine learning model called MLRNet has been developed to construct the high-accuracy two-body intermolecular potential energy surface (IPES). The outputs of the neural network are integrated into the physically realistic Morse/long-range (MLR) function, which ensures that the MLRNet has meaningful extrapolation at both short and long ranges and solves the asymptotic problem in common neural network potential (NNP) models. The neural network representation of the MLR parameters is more flexible and more efficient than the polynomial expansion in the conventional mdMLR model, especially for systems containing nonrigid monomer(s). The present work illustrates the basic framework of the current MLRNet model, including (i) how to combine the physically meaningful MLR function with different possible NN structures, (ii) the preservation of permutation symmetry, and (iii) the predetermination of the long-range function uLR. We choose two realistic systems to demonstrate the performance of MLRNet: the three-dimensional IPES of CO2-He including the CO2 antisymmetric vibration Q3 and the six-dimensional IPES of the H2O-Ar system. In both cases, the fitting errors of the MLRNet are several times smaller than those of the conventional mdMLR model. Both short-range and long-range extrapolation tests were performed to illustrate the extrapolation ability of the MLRNet and its damping function version. Moreover, for the 6-D H2O-Ar system, the MLRNet only needs 1596 trainable parameters, which is almost equal to the number needed for the 5-D mdMLR model (1509) and half that needed for the PIP-NN model (3501) within similar accuracy, which illustrates the model efficiency in high-dimensional IPES fitting.
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Affiliation(s)
- You Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
| | - Yu Zhai
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
| | - Hui Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, P. R. China
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Jeong KJ, Jeong S, Lee S, Son CY. Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204272. [PMID: 36373701 DOI: 10.1002/adma.202204272] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/05/2022] [Indexed: 06/16/2023]
Abstract
Electrostatic interactions play a dominant role in charged materials systems. Understanding the complex correlation between macroscopic properties with microscopic structures is of critical importance to develop rational design strategies for advanced materials. But the complexity of this challenging task is augmented by interfaces present in the charged materials systems, such as electrode-electrolyte interfaces or biological membranes. Over the last decades, predictive molecular simulations that are founded in fundamental physics and optimized for charged interfacial systems have proven their value in providing molecular understanding of physicochemical properties and functional mechanisms for diverse materials. Novel design strategies utilizing predictive models have been suggested as promising route for the rational design of materials with tailored properties. Here, an overview of recent advances in the understanding of charged interfacial systems aided by predictive molecular simulations is presented. Focusing on three types of charged interfaces found in energy materials and biomacromolecules, how the molecular models characterize ion structure, charge transport, morphology relation to the environment, and the thermodynamics/kinetics of molecular binding at the interfaces is discussed. The critical analysis brings two prominent field of energy materials and biological science under common perspective, to stimulate crossover in both research field that have been largely separated.
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Affiliation(s)
- Kyeong-Jun Jeong
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Seungwon Jeong
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Sangmin Lee
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Chang Yun Son
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
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Abstract
Carbenes are highly reactive compounds with unique value to synthetic chemistry. However, a small number of natural enzymes have been shown to utilize carbene chemistry, and artificial enzymes engineered with directed evolution required transition metal ions to stabilize the carbene intermediates. To facilitate the design of broader classes of enzymes that can take advantage of the rich carbene chemistry, it is thus important to better understand how to stabilize carbene species in enzyme active sites without metal ions. Motivated by our recent studies of the anaerobic ergothioneine biosynthesis enzyme EanB, we examine carbene-protein interaction with both cluster models and QM/MM simulations. The cluster calculations find that an N-heterocyclic carbene interacts strongly with polar and positively charged protein motifs. In particular, the interaction between a guanidinium group and carbene is as strong as ∼30 kcal/mol, making arginine a great choice for the preferential stabilization of carbenes. We also compare the WT EanB and its mutant in which the key tyrosine was replaced by a non-natural analogue (F2Tyr) using DFTB3/MM simulations. The calculations suggest that the carbene intermediate in the F2Tyr mutant is more stable than that in the WT enzyme by ∼3.5 kcal/mol, due to active site rearrangements that enable a nearby arginine to better stabilize the carbene in the mutant. Overall, the current work lays the foundation for the pursuit of enzyme designs that can take advantage of the unique chemistry offered by carbenes without the requirement of metal ions.
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Affiliation(s)
- Rui Lai
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Dalian Institute of Chemical Physics, Chinese Academy of Science, 457 Zhongshan Road, Dalian 116023, China
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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Stoppelman JP, McDaniel JG. Erratum: "Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM +][OAc -]" [J. Chem. Phys. 155, 104112 (2021)]. J Chem Phys 2022; 156:029901. [PMID: 35032983 DOI: 10.1063/5.0082195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- John P Stoppelman
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
| | - Jesse G McDaniel
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
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