Perspective: Coarse-grained models for biomolecular systems

2013/09/03 by W. G. Noid · 923 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Artificial intelligence #Block Copolymer Self-Assembly #Computational model #Computer science #Data science #Engineering #Field (mathematics) #Machine Learning in Materials Science #Management science #Perspective (graphical) #Popularity #Presentation (obstetrics) #Protein Structure and Dynamics #Scope (computer science)

paper · pdf · doi:10.1063/1.4818908

published in The Journal of Chemical Physics 139(9), 090901 (American Institute of Physics)

openalex publication_date 2013/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.

Citations

• On Information and Sufficiency
• The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models
• The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models
• The relative entropy is fundamental to multiscale and inverse thermodynamic problems
• Multiscale coarse graining of liquid-state systems
• Coarse-graining errors and numerical optimization using a relative entropy framework
• Anisotropy of Fluctuation Dynamics of Proteins with an Elastic Network Model

Cited by

• Coarse Graining Molecular Dynamics with Graph Neural Networks
• Multi-body effects in a coarse-grained protein force field
• A new one-site coarse-grained model for water: Bottom-up many-body projected water (BUMPer). I. General theory and model
• A new one-site coarse-grained model for water: Bottom-up many-body projected water (BUMPer). II. Temperature transferability and structural properties at low temperature
• Can a coarse-grained water model capture the key physical features of the hydrophobic effect?
• Quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM)
• Non-Markovian intelligent dissipative particle dynamics integrated with machine learning for enhancing coarse-grained simulations
• Intelligent dissipative particle dynamics: Bridging mesoscopic models from microscopic simulations via deep neural networks
• Machine Learning for Molecular Simulation
• Reactive Coarse Grained Force Field for Metal–Organic Frameworks Applied to Modeling ZIF-8 Self-Assembly
• Fast parameterization of Martini3 models for fragments and small molecules
• Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
• CGBack: Diffusion Model for Backmapping Large-Scale and Complex Coarse-Grained Molecular Systems
• Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
• Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions
• Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
• Gas Adsorption and Diffusion Behaviors in Interfacial Systems Composed of a Polymer of Intrinsic Microporosity and Amorphous Silica: A Molecular Simulation Study
• Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
• Field-Theoretic Simulation of Dean-Kawasaki Dynamics for Interacting Particles
• MolCluster: Integrating Graph Neural Network with Community Detection for Coarse-Grained Mapping
• Machine learning coarse-grained potentials of protein thermodynamics
• Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
• A Field-Theoretical Paradigm via Hierarchical Coarse-Graining: I. Generalized Mode Theory
• The tubular cavity of tobacco mosaic virus shields mechanical stress and regulates disassembly
• Recent advances in accelerated discovery through machine learning and statistical inference
• Protein Drift-Diffusion in Membranes with Non-equilibrium Fluctuations arising from Gradients in Concentration or Temperature
• Operator Forces For Coarse-Grained Molecular Dynamics
• The Mori-Zwanzig formalism for the derivation of a fluctuating heat conduction model from molecular dynamics
• Analytical coarse grained potential parameterization by Reinforcement Learning for anisotropic cellulose
• On finding optimal collective variables for complex systems by minimizing the deviation between effective and full dynamics
• Machine Learning of coarse-grained Molecular Dynamics Force Fields
• Linear Response Based Parameter Estimation in the Presence of Model Error
• An Iterative Framework for Generative Backmapping of Coarse Grained Proteins
• A coarse-grained model with implicit salt for RNAs: Predicting 3D structure, stability and salt effect
• Hierarchical modeling of polystyrene melts: From soft blobs to atomistic resolution
• Reducing Weighted Ensemble Variance With Optimal Trajectory Management
• Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data
• Sheaf-theoretic representation of the proteolipid code
• Unveiling the Role of Friction in Coarse-Grained Clay: A Hybrid Framework Integrating Long-Range Interactions and Granular Contact Mechanics
• Relative Resolution: A Hybrid Formalism for Fluid Mixtures
• Communication: Adaptive boundaries in multiscale simulations
• Inverse design of multicomponent assemblies
• Dynamic principle for ensemble control tools
• A multiscale method for simulating fluid interfaces covered with large molecules such as asphaltenes
• Coupling all-atom molecular dynamics simulations of ions in water with Brownian dynamics
• Interactions and design rules for assembly of porous colloidal mesophases
• Molecular Insights into the Mechanical Properties of Polymer–Fullerene Bulk Heterojunctions for Organic Photovoltaic Applications
• Adversarial Training for Dynamics Matching in Coarse-Grained Models
• The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems. [europepmc]
• Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions. [europepmc]
• Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules. [europepmc]
• Physical models of collective cell motility: from cell to tissue. [europepmc]
• RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview. [europepmc]
• IsRNA: An Iterative Simulated Reference State Approach to Modeling Correlated Interactions in RNA Folding. [europepmc]
• Advances in coarse-grained modeling of macromolecular complexes. [europepmc]
• Computational Modeling of Realistic Cell Membranes. [europepmc]
• Drug-Membrane Permeability across Chemical Space. [europepmc]
• A Review of Multiscale Computational Methods in Polymeric Materials. [europepmc]
• Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. [europepmc]
• Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications. [europepmc]
• Pitfalls of the Martini Model. [europepmc]
• Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. [europepmc]
• Coarse graining molecular dynamics with graph neural networks. [europepmc]
• Coarse-Grained Force Fields from the Perspective of Statistical Mechanics: Better Understanding of the Origins of a MARTINI Hangover. [europepmc]
• AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses. [europepmc]
• Physics-based computational and theoretical approaches to intrinsically disordered proteins. [europepmc]
• TorchMD: A Deep Learning Framework for Molecular Simulations. [europepmc]
• Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation. [europepmc]
• Multiscale modeling of genome organization with maximum entropy optimization. [europepmc]
• Biomolecular modeling thrives in the age of technology. [europepmc]
• Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy. [europepmc]
• Bottom-up Coarse-Graining: Principles and Perspectives. [europepmc]
• Understanding Nanocellulose-Water Interactions: Turning a Detriment into an Asset. [europepmc]

Related

• Coarse-Grained Protein Models and Their Applications
• Biomolecular Modeling: Goals, Problems, Perspectives
• Capturing the essence of folding and functions of biomolecules using coarse-grained models
• Multi-body effects in a coarse-grained protein force field
• Applications of coarse-grained models in metabolic engineering