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

RESEARCH NEWS

Metastable Shared Proton Complexes in Aqueous Sulfuric Acid Solutions

This study investigates the formation of metastable, shared proton contact ion pairs (CIPs) in aqueous sulfuric acid (H₂SO₄) solutions at 0.5 mol/L concentration (pH = 0.3). Using ab initio molecular dynamics (AIMD) simulations and free energy sampling, the research estimates the free energy profile and dissociation barrier of these unusual CIPs formed by anions. These CIPs involve two anions sharing a single proton, resembling one of the limiting solvation structures of the proton in water known as the “Zundel cation.” Quantum mechanics (QM) calculations further analyze stabilizing interactions, while hydrogen bond analyses and radial distribution functions (RDFs) help resolve solvation structures. The study highlights the role of hydrogen bonds, induction interactions, and solvation effects in stabilizing CIPs, offering new insights into the complex behavior of aqueous acidic solutions beyond simple hydronium dissociation.

Data-Driven Equation-Free Dynamics Applied to Many-Protein Complexes: The Microtubule Tip Relaxation

This study investigates the structural dynamics of microtubule (MT) tips complexed with GDP and GTP using an “equation-free” multiscale computational method. By leveraging large-scale all-atom simulations (∼21–38 million atoms) and accelerating relaxation through coarse-projective techniques, the research effectively extends simulation time to 5.875 μs. The findings reveal critical differences in MT tip structures and lateral interactions depending on nucleotide binding, offering deeper insights into MT dynamic instability. This approach provides a powerful framework for studying large biomolecular systems at atomic resolution.

Molecular mechanism of Arp2/3 complex activation by nucleation-promoting factors and an actin monomer

Arp (actin-related protein) 2/3 complex, a large protein with seven subunits, assembles branched actin filaments, which generate forces for cell and organelle movements. Arp2/3 complex is natively inactive and must undergo large conformational changes to transition into the active state in the branch junction. Proteins called nucleation-promoting factors (NPF) and actin filaments assist in branch formation, but their mechanisms are incompletely characterized. Our molecular dynamics simulations of several cryo-electron microscopy (cryo-EM) structures of Arp2/3 complex revealed the mechanism of NPF mediated Arp2/3 activation and the role of actin monomer in stabilizing the transition state of activated Arp2/3 complex. Our free energy calculations reveal energy barriers and kinetic and thermodynamic parameters of actin filament branch formation which were previously unknown. We used these insights to propose a biophysically supported pathway for branch formation.

Understanding the Coarse-grained Free Energy Landscape of Phospholipids and Their Phase Separation

Molecular coarse-grained (CG) models, derived from statistical mechanical renormalization of atomistic models, are used to investigate the lateral heterogeneity of cell membranes, particularly the preferential association of cholesterol (CHOL) with saturated lipids to form ordered domains. These CG models enable the spontaneous assembly and phase separation of two model raft-like systems, DLiPC/DPPC/CHOL and DOPC/DPPC/CHOL, overcoming the limitations of conventional all-atom molecular dynamics due to large spatiotemporal scales. The resulting CG models accurately replicate the structural correlations of atomistic models, offering a powerful tool for exploring lipid phase separation and providing a basis for future work on transferable bottom-up CG models across various lipid compositions.

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.

More . . .