Developing and applying new theoretical and computational methods to study complex condensed phase systems

RESEARCH NEWS

Entropy-Based Methods for Formulating Bottom-Up Ultra-Coarse-Grained Models

Bottom-up coarse-grained (CG) modeling extends the spatiotemporal reach of atomistic molecular dynamics while preserving key molecular details. However, balancing accuracy and efficiency remains a challenge, as many-body interactions—critical for capturing chemical heterogeneities—slow down simulations. The Ultra Coarse-Graining (UCG) method addresses this by incorporating discrete internal state variables that adapt the CG force field based on local environmental changes. To enhance UCG model development, this study introduces two complementary approaches: (1) a systematic force-field construction based on relative entropy minimization and (2) a machine-learning approach to identify optimal local order parameters, improving efficiency and transferability. Applications to methanol liquid-vapor interfaces and ripple-phase lipid bilayers demonstrate that UCG modeling captures phase coexistence effects missed by standard CG models.

On The Emergence of Machine-Learning Methods in Bottom-Up Coarse-graining

Machine-learning methods are increasingly being adopted in computational chemistry for molecular modeling and analysis. The success of neural networks in learning atomistic force fields and coarse-graining electronic structure has inspired their application to thermodynamic coarse-graining in chemical and biological systems. This review examines the feasibility and challenges of using machine learning to develop coarse-grained force fields, highlighting its potential to improve efficiency, accuracy, and transferability across different modeling approaches. Additionally, the role of machine learning in various aspects of coarse-grained modeling is explored, emphasizing its growing impact on computational chemistry.

Lipid Organization by the Caveolin-1 Complex

Caveolins are lipid-binding proteins that facilitate membrane remodeling and oligomerize into the 8S complex, a 15 nm disk-like structure with a central beta barrel. Further oligomerization of these complexes leads to caveolae formation, a process influenced by cholesterol concentration, though its molecular mechanisms remain unclear. Using atomistic molecular dynamics simulations and enhanced sampling techniques, this study reveals how the CAV1-8S complex bends the membrane and accumulates cholesterol. Simulations suggest that CAV1 palmitoylations enhance this effect, enabling the extraction and accommodation of cholesterol within the beta barrel. Additionally, backmapping to all-atom resolution indicates that the Martini v.2 coarse-grained force field overestimates membrane bending, as atomistic simulations reveal only localized deformation.

Quantitative insights into the mechanism of proton conduction and selectivity for the human voltage-gated proton channel Hv1

Human voltage-gated proton (hHv1) channels play a vital role in biological processes such as immune responses, sperm capacitation, and cancer cell migration. Despite the overwhelming presence of other ions, hHv1 exhibits remarkable proton selectivity. Free energy profile calculations reveal that this selectivity stems from the strong proton affinity of key residues D112 and D174, rather than kinetic exclusion of other ions. A two-proton knock-on model is proposed to mathematically describe the channel’s pH-dependent conductance. Additionally, the D112N mutant is shown to exhibit anion selectivity, which correlates with impaired proton transport and increased anion leakage, highlighting the crucial role of D112 in maintaining proton specificity.

QM/CG-MM: Systematic Embedding of Quantum Mechanical Systems in a Coarse-Grained Environment with Accurate Electrostatics

Quantum Mechanics/Molecular Mechanics (QM/MM) can describe chemical reactions in molecular dynamics (MD) simulations at a much lower cost than ab initio MD. Still, it is prohibitively expensive for many systems of interest because such systems usually require long simulations for sufficient statistical sampling. Additional MM degrees of freedom are often slow and numerous but secondary in interest. Coarse-graining (CG) is well-known to be able to speed up sampling through both reduction in simulation cost and the ability to accelerate the dynamics. Therefore, embedding a QM system in a CG environment can be a promising way of expediting sampling without compromising the information about the QM subsystem. Sinitskiy and Voth first proposed the theory of Quantum Mechanics/Coarse-grained Molecular Mechanics (QM/CG-MM) with a bottom-up CG mapping. Mironenko and Voth subsequently introduced the DFT-QM/CG-MM formalism to couple a Density Functional Theory (DFT) treated QM system and to an apolar environment. Here, we present a more complete theory that addresses MM environments with significant polarity by explicitly accounting for the electrostatic coupling. We demonstrate our QM/CG-MM method with a chloride-methyl chloride SN2 reaction system in acetone, which is sensitive to solvent polarity. The method accurately recapitulates the potential of mean force for the substitution reaction, and the reaction barrier from the best model agrees with the atomistic simulations within sampling error. These models also have generalizability. In two other reactive systems that they have not been trained on, the QM/CG-MM model still achieves the same level of agreement with the atomistic QM/MM models. Finally, we show that in these examples the speed-up in the sampling is proportional to the acceleration of the rotational dynamics of the solvent in the CG system.

Molecular Dynamics Simulation of Complex Reactivity with the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) Software Package

Simulating chemically reactive phenomena, such as proton transport, over extended time scales present significant challenges. Ab initio methods struggle to routinely reach nanosecond to microsecond time scales, while traditional molecular dynamics methods cannot account for dynamic bonding changes. The Multiscale Reactive Molecular Dynamics (MS-RMD) method, implemented in the RAPTOR software for LAMMPS, addresses this limitation by enabling statistically precise, long-time-scale reactive simulations. RAPTOR can also integrate with enhanced sampling techniques to analyze rare reactive events, supported by specialized collective variables (CVs). This review highlights key advancements, including GPU acceleration and novel CVs for modeling water wire formation, along with recent applications demonstrating RAPTOR’s versatility and robustness.

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