1
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Wu Z, Zhou T. Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation. J Chem Theory Comput 2024; 20:2605-2617. [PMID: 38483262 DOI: 10.1021/acs.jctc.3c01348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
In the realm of multiscale molecular simulations, structure-based coarse-graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems, such as polymers. This involves optimizing CG interactions by matching static correlation functions of the corresponding degrees of freedom in all-atom (AA) models. Here, we present a versatile method, namely, differentiable coarse-graining (DiffCG), which combines multiobjective optimization and differentiable simulation. The DiffCG approach is capable of constructing robust CG models by iteratively optimizing the effective potentials to simultaneously match multiple target properties. We demonstrate our approach by concurrently optimizing bonded and nonbonded potentials of a CG model of polystyrene (PS) melts. The resulting CG-PS model effectively reproduces both the structural characteristics, such as the equilibrium probability distribution of microscopic degrees of freedom and the thermodynamic pressure of the AA counterpart. More importantly, leveraging the multiobjective optimization capability, we develop a precise and efficient CG model for PS melts that is transferable across a wide range of temperatures, i.e., from 400 to 600 K. It is achieved via optimizing a pairwise potential with nonlinear temperature dependence in the CG model to simultaneously match target data from AA-MD simulations at multiple thermodynamic states. The temperature transferable CG-PS model demonstrates its ability to accurately predict the radial distribution functions and density at different temperatures, including those that are not included in the target thermodynamic states. Our work opens up a promising route for developing accurate and transferable CG models of complex soft-matter systems through multiobjective optimization with differentiable simulation.
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Affiliation(s)
- Zhenghao Wu
- Department of Chemistry, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, P. R. China
| | - Tianhang Zhou
- College of Carbon Neutrality Future Technology, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, P. R. China
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2
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Christofi E, Bačová P, Harmandaris VA. Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers. J Chem Inf Model 2024; 64:1853-1867. [PMID: 38427962 PMCID: PMC10966642 DOI: 10.1021/acs.jcim.3c01870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.
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Affiliation(s)
- Eleftherios Christofi
- Computation-based
Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
| | - Petra Bačová
- Departamento
de Ciencia de los Materiales e Ingeniería Metalúrgica
y Química Inorgánica, Facultad de Ciencias, IMEYMAT, Campus Universitario Río San Pedro s/n.,
Puerto Real, Cádiz 11510, Spain
| | - Vagelis A. Harmandaris
- Computation-based
Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus
- Department
of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece
- Institute
of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas, Heraklion GR-71110, Crete, Greece
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3
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Brown BP, Stein RA, Meiler J, Mchaourab HS. Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations. J Chem Theory Comput 2024; 20:1434-1447. [PMID: 38215214 PMCID: PMC10867840 DOI: 10.1021/acs.jctc.3c01081] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024]
Abstract
Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, computational and experimental methods capable of quantifying the energetic determinants of these equilibria are limited to systems of modest size. Recently, it has been demonstrated that the artificial intelligence system AlphaFold2 can be manipulated to produce structurally valid protein conformational ensembles. Here, we extend these studies and explore the extent to which AlphaFold2 contact distance distributions can approximate projections of the conformational Boltzmann distributions. For this purpose, we examine the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Our studies suggest that AlphaFold2 normalized contact distance distributions can correlate with conformation probabilities obtained with other methods but that they suffer from peak broadening. We also find that the AlphaFold2 contact distance distributions can be sensitive to point mutations. Overall, we anticipate that our findings will be valuable as the community seeks to model the thermodynamics of conformational changes in large biomolecular systems.
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Affiliation(s)
- Benjamin P. Brown
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
| | - Richard A. Stein
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig, SAC 04103, Germany
| | - Hassane S. Mchaourab
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Center
for Applied AI in Protein Dynamics, Vanderbilt
University, Nashville, Tennessee 37232, United States
- Department
of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, United States
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4
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Maier JC, Wang CI, Jackson NE. Distilling coarse-grained representations of molecular electronic structure with continuously gated message passing. J Chem Phys 2024; 160:024109. [PMID: 38193551 DOI: 10.1063/5.0179253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/14/2023] [Indexed: 01/10/2024] Open
Abstract
Bottom-up methods for coarse-grained (CG) molecular modeling are critically needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a target molecule, the ideal reduced CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We introduce continuously gated message passing (CGMP), a graph neural network (GNN) method for atomically decomposing molecular electronic structure sampled over conformational ensembles. CGMP integrates 3D-invariant GNNs and a novel gated message passing system to continuously reduce the atomic degrees of freedom accessible for electronic predictions, resulting in a one-shot importance ranking of atoms contributing to a target molecular property. Moreover, CGMP provides the first approach by which to quantify the degeneracy of "good" CG representations conditioned on specific prediction targets, facilitating the development of more transferable CG representations. We further show how CGMP can be used to highlight multiatom correlations, illuminating a path to developing CG electronic Hamiltonians in terms of interpretable collective variables for arbitrarily complex molecules.
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Affiliation(s)
- J Charlie Maier
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Chun-I Wang
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Nicholas E Jackson
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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5
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Jones MS, Shmilovich K, Ferguson AL. DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces. J Chem Theory Comput 2023; 19:7908-7923. [PMID: 37906711 DOI: 10.1021/acs.jctc.3c00840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.
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Affiliation(s)
- Michael S Jones
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Kirill Shmilovich
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
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6
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Brown BP, Stein RA, Meiler J, Mchaourab H. Approximating conformational Boltzmann distributions with AlphaFold2 predictions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.06.552168. [PMID: 37609301 PMCID: PMC10441281 DOI: 10.1101/2023.08.06.552168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Protein dynamics are intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria are evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, the computational and experimental methods capable of quantifying them are limited to systems of modest size. Here, we demonstrate that AlphaFold2 contact distance distributions can approximate conformational Boltzmann distributions, which we evaluate through examination of the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Further, we show that contact distance probability distributions generated by AlphaFold2 are sensitive to points mutations thus AF2 can predict the structural effects of mutations in some systems. We anticipate that our approach will be a valuable tool to model the thermodynamics of conformational changes in large biomolecular systems.
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Affiliation(s)
- Benjamin P. Brown
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
| | - Richard A. Stein
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA. Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, SAC 04103, Germany
| | - Hassane Mchaourab
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Center for Applied AI in Protein Dynamics, Vanderbilt University, Nashville, TN, USA. Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA. Nashville, TN 37232, USA
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7
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Gravelle S, Beyer D, Brito M, Schlaich A, Holm C. Assessing the Validity of NMR Relaxation Rates Obtained from Coarse-Grained Simulations of PEG-Water Mixtures. J Phys Chem B 2023. [PMID: 37327109 DOI: 10.1021/acs.jpcb.3c01646] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
NMR relaxometry is a powerful and well-established experimental approach for characterizing dynamic processes in soft matter systems. All-atom (AA) resolved simulations are typically employed to gain further microscopic insights while reproducing the relaxation rates R1. However, such approaches are limited to time and length scales that prevent to model systems such as long polymer chains or hydrogels. Coarse graining (CG) can overcome this barrier at the cost of losing atomistic details that impede the calculation of NMR relaxation rates. Here, we address this issue by performing a systematic characterization of dipolar relaxation rates R1 on a PEG-H2O mixture at two different levels of details: AA and CG. Remarkably, we show that NMR relaxation rates R1 obtained at the CG level obey the same trends when compared to AA calculations but with a systematic offset. This offset is due to, on the one hand, the lack of an intramonomer component and, on the other hand, the inexact positioning of the spin carriers. We show that the offset can be corrected for quantitatively by reconstructing a posteriori the atomistic details for the CG trajectories.
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Affiliation(s)
- Simon Gravelle
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
| | - David Beyer
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
| | - Mariano Brito
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
| | - Alexander Schlaich
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
- Stuttgart Center for Simulation Science (SC SimTech), University of Stuttgart, D-70569 Stuttgart, Germany
| | - Christian Holm
- Institute for Computational Physics, University of Stuttgart, D-70569 Stuttgart, Germany
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8
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Bhatia H, Aydin F, Carpenter TS, Lightstone FC, Bremer PT, Ingólfsson HI, Nissley DV, Streitz FH. The confluence of machine learning and multiscale simulations. Curr Opin Struct Biol 2023; 80:102569. [PMID: 36966691 DOI: 10.1016/j.sbi.2023.102569] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 06/04/2023]
Abstract
Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.
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Affiliation(s)
- Harsh Bhatia
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA. https://twitter.com/@harshbhatia85
| | - Fikret Aydin
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Timothy S Carpenter
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Felice C Lightstone
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Peer-Timo Bremer
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Helgi I Ingólfsson
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Dwight V Nissley
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA.
| | - Frederick H Streitz
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA.
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9
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Ricci E, Vergadou N. Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers. J Phys Chem B 2023; 127:2302-2322. [PMID: 36888553 DOI: 10.1021/acs.jpcb.2c06354] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.
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Affiliation(s)
- Eleonora Ricci
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
- Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
| | - Niki Vergadou
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
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10
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Shmilovich K, Stieffenhofer M, Charron NE, Hoffmann M. Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution. J Phys Chem A 2022; 126:9124-9139. [PMID: 36417670 PMCID: PMC9743211 DOI: 10.1021/acs.jpca.2c07716] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Coarse-graining offers a means to extend the achievable time and length scales of molecular dynamics simulations beyond what is practically possible in the atomistic regime. Sampling molecular configurations of interest can be done efficiently using coarse-grained simulations, from which meaningful physicochemical information can be inferred if the corresponding all-atom configurations are reconstructed. However, this procedure of backmapping to reintroduce the lost atomistic detail into coarse-grain structures has proven a challenging task due to the many feasible atomistic configurations that can be associated with one coarse-grain structure. Existing backmapping methods are strictly frame-based, relying on either heuristics to replace coarse-grain particles with atomic fragments and subsequent relaxation or parametrized models to propose atomic coordinates separately and independently for each coarse-grain structure. These approaches neglect information from previous trajectory frames that is critical to ensuring temporal coherence of the backmapped trajectory, while also offering information potentially helpful to producing higher-fidelity atomic reconstructions. In this work, we present a deep learning-enabled data-driven approach for temporally coherent backmapping that explicitly incorporates information from preceding trajectory structures. Our method trains a conditional variational autoencoder to nondeterministically reconstruct atomistic detail conditioned on both the target coarse-grain configuration and the previously reconstructed atomistic configuration. We demonstrate our backmapping approach on two exemplar biomolecular systems: alanine dipeptide and the miniprotein chignolin. We show that our backmapped trajectories accurately recover the structural, thermodynamic, and kinetic properties of the atomistic trajectory data.
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Affiliation(s)
- Kirill Shmilovich
- Pritzker
School of Molecular Engineering, University
of Chicago, Chicago, Illinois60637, United States,E-mail:
| | | | - Nicholas E. Charron
- Weiss
School of Natural Sciences, Department of Physics and Astronomy, Rice University, Houston, Texas77005, United States,Department
of Physics, Freie Universität Berlin, Berlin14195, Germany
| | - Moritz Hoffmann
- Fachbereich
Mathematik und Informatik, Freie Universität
Berlin, Berlin14195, Germany
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11
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Pantano S. Back and forth modeling through biological scales. Biochem Biophys Res Commun 2022; 633:39-41. [DOI: 10.1016/j.bbrc.2022.09.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/06/2022]
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12
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Jin J, Pak AJ, Durumeric AEP, Loose TD, Voth GA. Bottom-up Coarse-Graining: Principles and Perspectives. J Chem Theory Comput 2022; 18:5759-5791. [PMID: 36070494 PMCID: PMC9558379 DOI: 10.1021/acs.jctc.2c00643] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Indexed: 01/14/2023]
Abstract
Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.
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Affiliation(s)
- Jaehyeok Jin
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Alexander J. Pak
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Aleksander E. P. Durumeric
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Timothy D. Loose
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
| | - Gregory A. Voth
- Department of Chemistry,
Chicago Center for Theoretical Chemistry, Institute for Biophysical
Dynamics, and James Franck Institute, The
University of Chicago, Chicago, Illinois 60637, United States
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13
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López CA, Zhang X, Aydin F, Shrestha R, Van QN, Stanley CB, Carpenter TS, Nguyen K, Patel LA, Chen D, Burns V, Hengartner NW, Reddy TJE, Bhatia H, Di Natale F, Tran TH, Chan AH, Simanshu DK, Nissley DV, Streitz FH, Stephen AG, Turbyville TJ, Lightstone FC, Gnanakaran S, Ingólfsson HI, Neale C. Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework. J Chem Theory Comput 2022; 18:5025-5045. [PMID: 35866871 DOI: 10.1021/acs.jctc.2c00168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.
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Affiliation(s)
- Cesar A López
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Xiaohua Zhang
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Fikret Aydin
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Rebika Shrestha
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Que N Van
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Christopher B Stanley
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Timothy S Carpenter
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Kien Nguyen
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Lara A Patel
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - De Chen
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Violetta Burns
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicolas W Hengartner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Tyler J E Reddy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Harsh Bhatia
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Francesco Di Natale
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Timothy H Tran
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Albert H Chan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Dhirendra K Simanshu
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Dwight V Nissley
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Frederick H Streitz
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Andrew G Stephen
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Thomas J Turbyville
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Felice C Lightstone
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Sandrasegaram Gnanakaran
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Helgi I Ingólfsson
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Chris Neale
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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14
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Mahmoud AH, Masters M, Lee SJ, Lill MA. Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation. J Chem Inf Model 2022; 62:1602-1617. [PMID: 35352898 DOI: 10.1021/acs.jcim.1c01438] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent developments in generative deep neural networks rendered possible the parallel, statistically independent sampling of molecular configurations. To be able to accurately generate samples of large molecular systems from a high-dimensional multimodal equilibrium distribution function, we developed a hierarchical approach based on expressive normalizing flows with rational quadratic neural splines and coarse-grained representation. Furthermore, system specific priors and adaptive and property-based controlled learning was designed to diminish the likelihood for the generation of high-energy structures during sampling. Finally, backmapping from a coarse-grained to fully atomistic representation is performed through an equivariant transformer model. We demonstrate the applicability of the method on the one-shot configurational sampling of a protein system with more than a hundred amino acids. The results show enhanced expressivity that diminish the invertibility constraints inherent in the normalizing flow framework. Moreover, the capacity of the hierarchical normalizing flow model was tested on a challenging case study of the folding/unfolding dynamics of the peptide chignolin.
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Affiliation(s)
- Amr H Mahmoud
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Matthew Masters
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Soo Jung Lee
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Markus A Lill
- Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
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15
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Sivaraman G, Jackson NE. Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning. J Chem Theory Comput 2022; 18:1129-1141. [PMID: 35020388 DOI: 10.1021/acs.jctc.1c01001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.
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Affiliation(s)
- Ganesh Sivaraman
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Nicholas E Jackson
- Department of Chemistry, University of Illinois at Urbana-Champaign, 505 South Mathews Avenue, Urbana, Illinois 61801, United States
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16
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Hossain MS, Ji J, Lynch CJ, Guzman M, Nangia S, Mozhdehi D. Adaptive Recombinant Nanoworms from Genetically Encodable Star Amphiphiles. Biomacromolecules 2021; 23:863-876. [PMID: 34942072 PMCID: PMC8924867 DOI: 10.1021/acs.biomac.1c01314] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Recombinant nanoworms are promising candidates for materials and biomedical applications ranging from the templated synthesis of nanomaterials to multivalent display of bioactive peptides and targeted delivery of theranostic agents. However, molecular design principles to synthesize these assemblies (which are thermodynamically favorable only in a narrow region of the phase diagram) remain unclear. To advance the identification of design principles for the programmable assembly of proteins into well-defined nanoworms and to broaden their stability regimes, we were inspired by the ability of topologically engineered synthetic macromolecules to acess rare mesophases. To test this design principle in biomacromolecular assemblies, we used post-translational modifications (PTMs) to generate lipidated proteins with precise topological and compositional asymmetry. Using an integrated experimental and computational approach, we show that the material properties (thermoresponse and nanoscale assembly) of these hybrid amphiphiles are modulated by their amphiphilic architecture. Importantly, we demonstrate that the judicious choice of amphiphilic architecture can be used to program the assembly of proteins into adaptive nanoworms, which undergo a morphological transition (sphere-to-nanoworms) in response to temperature stimuli.
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Affiliation(s)
- Md Shahadat Hossain
- Department of Chemistry, Syracuse University, 1-014 Center for Science and Technology, 111 College Place, Syracuse, New York 13244, United States
| | - Jingjing Ji
- Department of Biomedical and Chemical Engineering, Syracuse University, 329 Link Hall, Syracuse, New York 13244, United States
| | - Christopher J Lynch
- Department of Chemistry, Syracuse University, 1-014 Center for Science and Technology, 111 College Place, Syracuse, New York 13244, United States
| | - Miguel Guzman
- Department of Chemistry, Syracuse University, 1-014 Center for Science and Technology, 111 College Place, Syracuse, New York 13244, United States
| | - Shikha Nangia
- Department of Biomedical and Chemical Engineering, Syracuse University, 329 Link Hall, Syracuse, New York 13244, United States.,BioInspired Syracuse: Institute for Material and Living Systems, Syracuse University, Syracuse, New York 13244, United States
| | - Davoud Mozhdehi
- Department of Chemistry, Syracuse University, 1-014 Center for Science and Technology, 111 College Place, Syracuse, New York 13244, United States.,BioInspired Syracuse: Institute for Material and Living Systems, Syracuse University, Syracuse, New York 13244, United States
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17
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Louison KA, Dryden IL, Laughton CA. GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling. J Chem Theory Comput 2021; 17:7930-7937. [PMID: 34852200 DOI: 10.1021/acs.jctc.1c00735] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility.
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18
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Vickery ON, Stansfeld PJ. CG2AT2: an Enhanced Fragment-Based Approach for Serial Multi-scale Molecular Dynamics Simulations. J Chem Theory Comput 2021; 17:6472-6482. [PMID: 34492188 PMCID: PMC8515810 DOI: 10.1021/acs.jctc.1c00295] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
![]()
Coarse-grained molecular
dynamics provides a means for simulating
the assembly and interactions of macromolecular complexes at a reduced
level of representation, thereby allowing both longer timescale and
larger sized simulations. Here, we describe an enhanced fragment-based
protocol for converting macromolecular complexes from coarse-grained
to atomistic resolution, for further refinement and analysis. While
the focus is upon systems that comprise an integral membrane protein
embedded in a phospholipid bilayer, the technique is also suitable
for membrane-anchored and soluble protein/nucleotide complexes. Overall,
this provides a method for generating an accurate and well-equilibrated
atomic-level description of a macromolecular complex. The approach
is evaluated using a diverse test set of 11 system configurations
of varying size and complexity. Simulations are assessed in terms
of protein stereochemistry, conformational drift, lipid/protein interactions,
and lipid dynamics.
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Affiliation(s)
- Owen N Vickery
- School of Life Sciences & Department of Chemistry, University of Warwick, Gibbet Hill Campus, Coventry CV4 7AL, U.K
| | - Phillip J Stansfeld
- School of Life Sciences & Department of Chemistry, University of Warwick, Gibbet Hill Campus, Coventry CV4 7AL, U.K
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19
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Dhamankar S, Webb MA. Chemically specific coarse‐graining of polymers: Methods and prospects. JOURNAL OF POLYMER SCIENCE 2021. [DOI: 10.1002/pol.20210555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Satyen Dhamankar
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
| | - Michael A. Webb
- Department of Chemical and Biological Engineering Princeton University Princeton New Jersey USA
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20
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Kubincová A, Riniker S, Hünenberger PH. Solvent-scaling as an alternative to coarse-graining in adaptive-resolution simulations: The adaptive solvent-scaling (AdSoS) scheme. J Chem Phys 2021; 155:094107. [PMID: 34496576 DOI: 10.1063/5.0057384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A new approach termed Adaptive Solvent-Scaling (AdSoS) is introduced for performing simulations of a solute embedded in a fine-grained (FG) solvent region itself surrounded by a coarse-grained (CG) solvent region, with a continuous FG ↔ CG switching of the solvent resolution across a buffer layer. Instead of relying on a distinct CG solvent model, the AdSoS scheme is based on CG models defined by a dimensional scaling of the FG solvent by a factor s, accompanied by an s-dependent modulation of the atomic masses and interaction parameters. The latter changes are designed to achieve an isomorphism between the dynamics of the FG and CG models, and to preserve the dispersive and dielectric solvation properties of the solvent with respect to a solute at FG resolution. This scaling approach offers a number of advantages compared to traditional coarse-graining: (i) the CG parameters are immediately related to those of the FG model (no need to parameterize a distinct CG model); (ii) nearly ideal mixing is expected for CG variants with similar s-values (ideal mixing holding in the limit of identical s-values); (iii) the solvent relaxation timescales should be preserved (no dynamical acceleration typical for coarse-graining); (iv) the graining level NG (number of FG molecules represented by one CG molecule) can be chosen arbitrarily (in particular, NG = s3 is not necessarily an integer); and (v) in an adaptive-resolution scheme, this level can be varied continuously as a function of the position (without requiring a bundling mechanism), and this variation occurs at a constant number of particles per molecule (no occurrence of fractional degrees of freedom in the buffer layer). By construction, the AdSoS scheme minimizes the thermodynamic mismatch between the different regions of the adaptive-resolution system, leading to a nearly homogeneous scaled solvent density s3ρ. Residual density artifacts in and at the surface of the boundary layer can easily be corrected by means of a grid-based biasing potential constructed in a preliminary pure-solvent simulation. This article introduces the AdSoS scheme and provides an initial application to pure atomic liquids (no solute) with Lennard-Jones plus Coulomb interactions in a slab geometry.
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Affiliation(s)
- Alžbeta Kubincová
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir Prelog-Weg 2, CH-8093 Zürich, Switzerland
| | - Sereina Riniker
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir Prelog-Weg 2, CH-8093 Zürich, Switzerland
| | - Philippe H Hünenberger
- Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir Prelog-Weg 2, CH-8093 Zürich, Switzerland
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21
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Joshi SY, Deshmukh SA. A review of advancements in coarse-grained molecular dynamics simulations. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1828583] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Soumil Y. Joshi
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, USA
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