Prediction of the essential intermolecular contacts for side-binding of VASP on F-actin.

Vasodilator-stimulated phosphoprotein (VASP) family proteins play a crucial role in mediating the actin network architecture in the cytoskeleton. The Ena/VASP homology 2 (EVH2) domain in each of the four identical arms of the tetrameric VASP consists of a loading poly-Pro region, a G-actin-binding domain (GAB), and an F-actin-binding domain (FAB). Together, the poly-Pro, GAB, and FAB domains allow VASP to bind to sides of actin filaments in a bundle, and recruit profilin-G-actin to processively elongate the filaments. The atomic resolution structure of the ternary complex, consisting of the loading poly-Pro region and GAB domain of VASP with profilin-actin, has been solved over a decade ago; however, a detailed structure of the FAB-F-actin complex has not been resolved to date. Experimental insights, based on homology of the FAB domain with the C region of WASP, have been used to hypothesize that the FAB domain binds to the cleft between subdomains 1 and 3 of F-actin. Here, in order to develop our understanding of the VASP-actin complex, we first augment known structural information about the GAB domain binding to actin with the missing FAB domain-actin structure, which we predict using homology modeling and docking simulations. In earlier work, we used mutagenesis and kinetic modeling to study the role of domain-level binding-unbinding kinetics of Ena/VASP on actin filaments in a bundle, specifically on the side of actin filaments. We further look at the nature of the side-binding of the FAB domain of VASP at the atomistic level using our predicted structure, and tabulate effective mutation sites on the FAB domain that would disrupt the VASP-actin complex. We test the binding affinity of Ena with mutated FAB domain using total internal reflection fluorescence microscopy experiments. The binding affinity of VASP is affected significantly for the mutant, providing additional support for our predicted structure.

Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

Mechanistic Insights on Permeation of Water over Iron Cations in Nanoporous Silicon Oxide Films for Selective H2 and O2 Evolution.

Electrocatalysts encapsulated by an ultrathin and semipermeable oxide layer offer a promising avenue for efficient, selective, and cost-effective production of hydrogen through photoelectrochemical water splitting. This architecture is especially attractive for Z-scheme water splitting, for which a nanoporous oxide film can be leveraged to mitigate undesired, yet kinetically facile, reactions involving redox shuttles, such as aqueous iron cations, by limiting transport of these species to catalytically active sites. In this work, molecular dynamics simulations were combined with electrochemical measurements to provide a mechanistic understanding of permeation of water and Fe(III)/Fe(II) redox shuttles through nanoporous SiO2 films. It is shown that even for SiO2 pores with a width as small as 0.8 nm, water does not experience any energy barrier for permeating into the pores due to a favorable interaction with hydrophilic silanol groups on the oxide surface. In contrast, permeation of Fe(III) and Fe(II) into microporous SiO2 pores is limited due to high energy barriers, which stem from a combination of distortion and dehydration of the second and third ion solvation shells. Our simulations and experimental results show that SiO2 coatings can effectively mitigate undesired Fe(III)/Fe(II) redox reactions at underlying electrodes by attenuating permeation of iron cations, while allowing water to permeate and thus participate in water splitting reactions. In a broader context, our study demonstrates that selectivity of solvated cations can be manipulated by controlling the pore size and surface chemistry of oxide films.

Fluids and Electrolytes under Confinement in Single-Digit Nanopores.

Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.

The confluence of machine learning and multiscale simulations.

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.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

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.

Rectified and Salt Concentration Dependent Wetting of Hydrophobic Nanopores.

Nanopores lined with hydrophobic groups function as switches for water and all dissolved species, such that transport is allowed only when applying a sufficiently high transmembrane pressure difference or voltage. Here we show a hydrophobic nanopore system whose wetting and ability to transport water and ions is rectified and can be controlled with salt concentration. The nanopore we study contains a junction between a hydrophobic zone and a positively charged hydrophilic zone. The nanopore is closed for transport at low salt concentrations and exhibits finite current only when the concentration reaches a threshold value that is dependent on the pore opening diameter, voltage polarity and magnitude, and type of electrolyte. The smallest nanopore studied here had a 4 nm diameter and did not open for transport in any concentration of KCl or KI examined. A 12 nm nanopore was closed for all KCl solutions but conducted current in KI at concentrations above 100 mM for negative voltages and opened for both voltage polarities at 500 mM KI. Nanopores with a hydrophobic/hydrophilic junction can thus function as diodes, such that one can identify a range of salt concentrations where the pores transport water and ions for only one voltage polarity. Molecular dynamics simulations together with continuum models provided a multiscale explanation of the observed phenomena and linked the salt concentration dependence of wetting with an electrowetting model. Results presented are crucial for designing next-generation chemical and ionic separation devices as well as understanding fundamental properties of hydrophobic interfaces under nanoconfinement.

Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables.

Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug delivery, pathogen detection, and the development of new antibiotics. To this end, computer simulations are routinely used to probe the underlying mechanisms of membrane permeation. Despite great progress and continued development, permeation simulations of realistic systems (e.g., more complex drug molecules or biologics through heterogeneous membranes) remain extremely challenging if not intractable. In this work, we combine molecular dynamics simulations with transition-tempered metadynamics and techniques from the variational approach to conformational dynamics to study the permeation mechanism of a drug molecule, trimethoprim, through a multicomponent membrane. We show that collective variables (CVs) obtained from an unsupervised machine learning algorithm called time-structure based Independent Component Analysis (tICA) improve performance and substantially accelerate convergence of permeation potential of mean force (PMF) calculations. The addition of cholesterol to the lipid bilayer is shown to increase both the width and height of the free energy barrier due to a condensing effect (lower area per lipid) and increase bilayer thickness. Additionally, the tICA CVs reveal a subtle effect of cholesterol increasing the resistance to permeation in the lipid head group region, which is not observed when canonical CVs are used. We conclude that the use of tICA CVs can enable more efficient PMF calculations with additional insight into the permeation mechanism.

Unraveling the Ion Adsorption Kinetics in Microporous Carbon Electrodes: A Multiscale Quantum-Continuum Simulation and Experimental Approach.

Understanding sorption in porous carbon electrodes is crucial to many environmental and energy technologies, such as capacitive deionization (CDI), supercapacitor energy storage, and activated carbon filters. In each of these examples, a practical model that can describe ion electrosorption kinetics is highly desirable for accelerating material design. Here, we proposed a multiscale model to study the ion electrosorption kinetics in porous carbon electrodes by combining quantum mechanical simulations with continuum approaches. Our model integrates the Butler-Volmer (BV) equation for sorption kinetics and a continuously stirred tank reactor (CSTR) formulation with atomistic calculations of ion hydration and ion-pore interactions based on density functional theory (DFT). We validated our model experimentally by using ion mixtures in a flow-through electrode CDI device and developed an in-line UV absorption system to provide unprecedented resolution of individual ions in the separation process. We showed that the multiscale model captures unexpected experimental phenomena that cannot be explained by the traditional ion electrosorption theory. The proposed multiscale framework provides a viable approach for modeling separation processes in systems where pore sizes and ion hydration effects strongly influence the sorption kinetics, which can be leveraged to explore possible strategies for improving carbon-based and, more broadly, pore-based technologies.

Confinement effects on the solvation structure of solvated alkali [correction] metal cations in a single-digit 1T-MoS2 nanochannel: A first-principles study.

Confinement plays an important role in determining ion transport in porous materials, which, in turn, may influence the performance of many energy storage and desalination devices. In this work, we combined density functional theory (DFT) with an implicit solvation model and ab initio molecular dynamics (AIMD) to investigate the effects of nanoconfinement on several solvated alkaline metal cations in a single-digit 1T-MoS2 nanochannel. Our DFT calculations with a solvation model indicated that cations with stronger hydration energy introduce a higher number of co-intercalated water molecules into the channel, consistent with early experimental observation obtained for MXene (2D transition metal carbide) channels. The predicted optimal water numbers for the cations were then used for AIMD simulations that explicitly include the effects of the solvent. When compared with the cations in bulk solution, our simulations showed that the hydration structure and coordination number (CN) of the solvated cations confined in the MoS2 channel can be significantly altered. We found that larger cations with weaker hydration energy (K+, Rb+, and Cs+) exhibited a distinctive CN decrease under confinement, while smaller cations (Li+ and Na+) retained a similar hydration shell as in the bulk solution. More specifically, the hydration shell of large cations (K+, Rb+, and Cs+) in MoS2 showed similar features of the coordination angle to the bulk, which suggests the partially broken hydration shell with no geometry change under confinement. Our simulations provided insights into the change of the hydration structure of alkaline metal cations under confinement, which may have important implications on their transport in the 1T-MoS2 channel.

Ion Solvation and Transport in Narrow Carbon Nanotubes: Effects of Polarizability, Cation-π Interaction, and Confinement.

Understanding ion solvation and transport under confinement is critical for a wide range of emerging technologies, including water desalination and energy storage. While molecular dynamics (MD) simulations have been widely used to study the behavior of confined ions, considerable deviations between simulation results depending on the specific treatment of intermolecular interactions remain. In the following, we present a systematic investigation of the structure and dynamics of two representative solutions, that is, KCl and LiCl, confined in narrow carbon nanotubes (CNTs) with a diameter of 1.1 and 1.5 nm, using a combination of first-principles and classical MD simulations. Our simulations show that the inclusion of both polarization and cation-π interactions is essential for the description of ion solvation under confinement, particularly for large ions with weak hydration energies. Beyond the variation in ion solvation, we find that cation-π interactions can significantly influence the transport properties of ions in CNTs, particularly for KCl, where our simulations point to a strong correlation between ion dehydration and diffusion. Our study highlights the complex interplay between nanoconfinement and specific intermolecular interactions that strongly control the solvation and transport properties of ions.

Fast Permeation of Small Ions in Carbon Nanotubes.

Simulations and experiments have revealed enormous transport rates through carbon nanotube (CNT) channels when a pressure gradient drives fluid flow, but comparatively little attention has been given to concentration-driven transport despite its importance in many fields. Here, membranes are fabricated with a known number of single-walled CNTs as fluid transport pathways to precisely quantify the diffusive flow through CNTs. Contrary to early experimental studies that assumed bulk or hindered diffusion, measurements in this work indicate that the permeability of small ions through single-walled CNT channels is more than an order of magnitude higher than through the bulk. This flow enhancement scales with the ion free energy of transfer from bulk solutions to a nanoconfined, lower-dielectric environment. Reported results suggest that CNT membranes can unlock dialysis processes with unprecedented efficiency.

Selectivity of nitrate and chloride ions in microporous carbons: the role of anisotropic hydration and applied potentials.

Understanding ion transport in porous carbons is critical for a wide range of technologies, including supercapacitors and capacitive deionization for water desalination, yet many details remain poorly understood. For instance, an atomistic understanding of how ion selectivity is influenced by the molecular shape of ions, morphology of the micropores and applied voltages is largely lacking. In this work, we combined molecular dynamics simulations with enhanced sampling methods to elucidate the mechanism of nitrate and chloride selectivity in subnanometer graphene slit-pores. We show that nitrate is preferentially adsorbed over chloride in the slit-like micropores. This preferential adsorption was found to stem from the weaker hydration energy and unique anisotropy of the ion solvation of nitrate. Beside the effects of ion dehydration, we found that applied potential plays an important role in determining the ion selectivity, leading to a lower selectivity of nitrate over chloride at a high applied potential. We conclude that the measured ion selectivity results from a complex interplay between voltage, confinement, and specific ion effects-including ion shape and local hydration structure.

Cation Selectivity in Capacitive Deionization: Elucidating the Role of Pore Size, Electrode Potential, and Ion Dehydration.

Capacitive deionization (CDI) is a promising water desalination technology that is applicable to the treatment of low-salinity brackish waters and the selective removal of ionic contaminants. In this work, we show that by making a small change in the synthetic procedure of hierarchical carbon aerogel monolith (HCAM) electrodes, we can adjust the pore-size distribution and tailor the selectivity, effectively switching between selective adsorption of calcium or sodium ions. Ion selectivity was measured for a mixture of 5 mM NaCl and 2.5 mM CaCl2. For the low activated flow-through CDI (fteCDI) cell, we observed extremely high sodium selectivity over calcium (SNa/Ca ≫ 10, no Ca2+ adsorbed) at all of the applied potentials, while for the highly activated fteCDI cell, we observed a selectivity value of 6.6 ± 0.8 at 0.6 V for calcium over sodium that decreased to 2.2 ± 0.03 at 1.2 V. Molecular dynamics simulations indicated that the loss in Ca2+ selectivity over Na+ at high applied voltages could be due to a competition between ion-pore electrostatic interactions and volume exclusion ("crowding") effects. Interestingly, we also find with these simulations that the relative sizes of the ions change due to changes in hydration at a higher voltage.

Water-ion permselectivity of narrow-diameter carbon nanotubes.

Carbon nanotube (CNT) pores, which mimic the structure of the aquaporin channels, support extremely high water transport rates that make them strong candidates for building artificial water channels and high-performance membranes. Here, we measure water and ion permeation through 0.8-nm-diameter CNT porins (CNTPs)-short CNT segments embedded in lipid membranes-under optimized experimental conditions. Measured activation energy of water transport through the CNTPs agrees with the barrier values typical for single-file water transport. Well-tempered metadynamics simulations of water transport in CNTPs also report similar activation energy values and provide molecular-scale details of the mechanism for water entry into these channels. CNTPs strongly reject chloride ions and show water-salt permselectivity values comparable to those of commercial desalination membranes.

Strong Differential Monovalent Anion Selectivity in Narrow Diameter Carbon Nanotube Porins.

Inner pores of carbon nanotubes combine extremely fast water transport and ion selectivity that could potentially be useful for high-performance water desalination and separation applications. We used dye-quenching halide assays and stopped-flow spectrometry to determine intrinsic permeability of three small monovalent halide anions (chloride, bromide, iodide) and one pseudohalide anion (thiocyanate) through narrow 0.8 nm diameter carbon nanotube porins (CNTPs). These measurements revealed unexpectedly strong differential ion selectivity with permeabilities of different ions varying by up to 2 orders of magnitude. Removal of the negative charge from the nanotube entrance increased anion permeability by only a relatively small factor, indicating that electrostatic repulsion was not a major determinant of CNTP selectivity. First principle molecular dynamics simulations revealed that the origin of this strong differential ion selectivity is partial dehydration of anions upon entry into the narrow CNTP channels.

Gating of Hydrophobic Nanopores with Large Anions.

Understanding ion transport in nanoporous materials is critical to a wide variety of energy and environmental technologies, ranging from ion-selective membranes, drug delivery, and biosensing, to ion batteries and supercapacitors. While nanoscale transport is often described by continuum models that rely on a point charge description for ions and a homogeneous dielectric medium for the solvent, here, we show that transport of aqueous solutions at a hydrophobic interface can be highly dependent on the size and hydration strength of the solvated ions. Specifically, measurements of ion current through single silicon nitride nanopores that contain a hydrophobic-hydrophilic junction show that transport properties are dependent not only on applied voltage but also on the type of anion. We find that in Cl--containing solutions the nanopores only conducted ionic current above a negative voltage threshold. On the other hand, introduction of large polarizable anions, such as Br- and I-, facilitated the pore wetting, making the pore conductive at all examined voltages. Molecular dynamics simulations revealed that the large anions, Br- and I-, have a weaker solvation shell compared to that of Cl- and consequently were prone to migrate from the aqueous solution to the hydrophobic surface, leading to the anion accumulation responsible for pore wetting. The results are essential for designing nanoporous systems that are selective to ions of the same charge, for realization of ion-induced wetting in hydrophobic pores, as well as for a fundamental understanding on the role of ion hydration shell on the properties of solid/liquid interfaces.