Configurational Sampling of All-Atom Solvated Membranes Using Hybrid Nonequilibrium Molecular Dynamics Monte Carlo Simulations.

All-atom simulations are a powerful approach to study the structure and dynamics of biological membranes. However, sampling the atomic configurations of inhomogeneous membranes can be challenging due to the slow lateral diffusion of the various constituents. To address this issue, a hybrid nonequilibrium molecular dynamics Monte Carlo (neMD/MC) simulation method is proposed in which randomly chosen lipid molecules are swapped to generate configurations that are subsequently accepted or rejected according to a Metropolis criterion based on the alchemical work for the attempted swap calculated via a short trajectory. A dual-topology framework constraining the common atoms of the exchanging molecules yields a good acceptance probability using switching trajectories as short as 10 ps. The performance of the hybrid neMD/MC algorithm and its ability to sample the distribution of lipids near a transmembrane helix carrying a net charge are illustrated for a binary mixture of charged and zwitterionic lipids.

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib.

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-µs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.

Dynamics of activation in the voltage-sensing domain of Ciona intestinalis phosphatase Ci-VSP.

The Ciona intestinalis voltage-sensing phosphatase (Ci-VSP) is a membrane protein containing a voltage-sensing domain (VSD) that is homologous to VSDs from voltage-gated ion channels responsible for cellular excitability. Previously published crystal structures of Ci-VSD in putative resting and active conformations suggested a helical-screw voltage sensing mechanism in which the S4 helix translocates and rotates to enable exchange of salt-bridge partners, but the microscopic details of the transition between the resting and active conformations remained unknown. Here, by combining extensive molecular dynamics simulations with a recently developed computational framework based on dynamical operators, we elucidate the microscopic mechanism of the resting-active transition at physiological membrane potential. Sparse regression reveals a small set of coordinates that distinguish intermediates that are hidden from electrophysiological measurements. The intermediates arise from a noncanonical helical-screw mechanism in which translocation, rotation, and side-chain movement of the S4 helix are only loosely coupled. These results provide insights into existing experimental and computational findings on voltage sensing and suggest ways of further probing its mechanism.

Classifying Protein-Protein Binding Affinity with Free-Energy Calculations and Machine Learning Approaches.

Understanding the intricate phenomenon of neuronal wiring in the brain is of great interest in neuroscience. In the fruit fly, Drosophila melanogaster, the Dpr-DIP interactome has been identified to play an important role in this process. However, experimental data suggest that a merely limited subset of complexes, essentially 57 out of a total of 231, exhibit strong binding affinity. In this work, we sought to identify the residue-level molecular basis underlying the difference in binding affinity using a state-of-the-art methodology consisting of standard binding free-energy calculations with a geometrical route and machine learning (ML) techniques. We determined the binding affinity for two complexes using statistical mechanics simulations, achieving an excellent reproduction of the experimental data. Moreover, we predicted the binding free energy for two additional low-affinity complexes, devoid of experimental estimation, while simultaneously identifying key residues for the binding. Furthermore, through the use of ML algorithms, linear discriminant analysis, and random forest, we achieved remarkable accuracy, as high as 0.99, in discerning between strong (cognate) and weak (noncognate) binders. The presented ML approach encompasses easily transferable input features, enabling its broad application to any interactome while facilitating the identification of pivotal residues critical for binding interactions. The predictive power of the generated model was probed on similar protein families from 13 diverse species. Our ML model exhibited commendable performance on these additional data sets, showcasing its reliability and robustness across the species barrier.

How is Membrane Permeation of Small Ionizable Molecules Affected by Protonation Kinetics?

According to the pH-partition hypothesis, the aqueous solution adjacent to a membrane is a mixture of the ionization states of the permeating molecule at fixed Henderson-Hasselbalch concentrations, such that each state passes through the membrane in parallel with its own specific permeability. An alternative view, based on the assumption that the rate of switching ionization states is instantaneous, represents the permeation of ionizable molecules via an effective Boltzmann-weighted average potential (BWAP). Such an assumption is used in constant-pH molecular dynamics simulations. The inhomogeneous solubility-diffusion framework can be used to compute the pH-dependent membrane permeability for each of these two limiting treatments. With biased WTM-eABF molecular dynamics simulations, we computed the potential of mean force and diffusivity of each ionization state of two weakly basic small molecules: nicotine, an addictive drug, and varenicline, a therapeutic for treating nicotine addiction. At pH = 7, the BWAP effective permeability is greater than that determined by pH-partitioning by a factor of 2.5 for nicotine and 5 for varenicline. To assess the importance of ionization kinetics, we present a Smoluchowski master equation that includes explicitly the protonation and deprotonation processes coupled with the diffusive motion across the membrane. At pH = 7, the increase in permeability due to the explicit ionization kinetics is negligible for both nicotine and varenicline. This finding is reaffirmed by combined Brownian dynamics and Markov state model simulations for estimating the permeability of nicotine while allowing changes in its ionization state. We conclude that for these molecules the pH-partition hypothesis correctly captures the physics of the permeation process. The small free energy barriers for the permeation of nicotine and varenicline in their deprotonated neutral forms play a crucial role in establishing the validity of the pH-partitioning mechanism. Essentially, BWAP fails because ionization kinetics are too slow on the time scale of membrane crossing to affect the permeation of small ionizable molecules such as nicotine and varenicline. For the singly protonated state of nicotine, the computational results agree well with experimental measurements (P1 = 1.29 × 10-7 cm/s), but the agreement for neutral (P0 = 6.12 cm/s) and doubly protonated nicotine (P2 = 3.70 × 10-13 cm/s) is slightly worse, likely due to factors associated with the aqueous boundary layer (neutral form) or leaks through paracellular pathways (doubly protonated form).

Simulating the Fluorescence of Di-8-ANEPPS in Solvents of Different Polarity.

Voltage-sensitive fluorescent probes, such as di-8-amino-naphthyl-ethylene-pyridinium-propyl-sulfonate (di-8-ANEPPS), are extremely useful for monitoring the membrane potential in biological and biophysical studies. However, because di-8-ANEPPS is very sensitive to its environment, it can be difficult to distinguish the degree to which a given external factor affects the observed fluorescence. Molecular dynamics simulations based on detailed atomic models make it possible to examine the particular characteristics of the system and predict the effects of the surroundings. Here, the sensitivity of the spectra of di-8-ANEPPS to solvent polarity is investigated by modeling the electronic transition between the ground and excited states using classical molecular mechanical force fields. The absorption and emission of di-8-ANEPPS were simulated in 12 solvents of increasing polarity using nonpolarizable and polarizable force fields. While the computational results and experimental data do not match perfectly, classical Lippert plots of both models show the expected increase of the Stokes shift of di-8-ANEPPS with the orientation polarizability of the surrounding solvent.

Simulating the Fluorescence of the Locally Excited State of DMABN in Solvents of Different Polarities.

4-(N,N-Dimethylamino)benzonitrile (DMABN) is a luminescent probe that can be used for tracking changes in the surrounding solvent due to the large change in polarity between its ground and excited states. An important characteristic of DMABN is that it exhibits dual fluorescence with two different emission energies that can be monitored, allowing for better characterization of the surrounding system. The first excited state is called the locally excited (LE) state and is characterized by the movement of charge over the conjugated ring structure. In nonpolar solvents and in the gas phase, the fluorescence of DMABN is entirely attributed to the transition from the near-planar LE state. In more polar environments, emission occurs from both the LE and a second excited state, corresponding to a twisted intramolecular charge-transfer (ICT) structure. For the sake of simplicity, this work considers transitions between only the ground and LE state. Molecular mechanical force field models of DMABN in its ground and LE states have been developed to investigate the sensitivity of the LE state to the polarity of the solvent. Both nonpolarizable and polarizable force fields were developed to simulate the molecule in a series of 10 different solvents of different polarities. The calculated Stokes shift of DMABN increases with increasing orientation polarizability of the surrounding solvent, which is the expected trend, as seen in experimental studies.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes.

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

A Na pump with reduced stoichiometry is up-regulated by brine shrimp in extreme salinities.

Brine shrimp (Artemia) are the only animals to thrive at sodium concentrations above 4 M. Salt excretion is powered by the Na+,K+-ATPase (NKA), a heterodimeric (αß) pump that usually exports 3Na+ in exchange for 2 K+ per hydrolyzed ATP. Artemia express several NKA catalytic α-subunit subtypes. High-salinity adaptation increases abundance of α2KK, an isoform that contains two lysines (Lys308 and Lys758 in transmembrane segments TM4 and TM5, respectively) at positions where canonical NKAs have asparagines (Xenopus α1's Asn333 and Asn785). Using de novo transcriptome assembly and qPCR, we found that Artemia express two salinity-independent canonical α subunits (α1NN and α3NN), as well as two ß variants, in addition to the salinity-controlled α2KK. These ß subunits permitted heterologous expression of the α2KK pump and determination of its CryoEM structure in a closed, ion-free conformation, showing Lys758 residing within the ion-binding cavity. We used electrophysiology to characterize the function of α2KK pumps and compared it to that of Xenopus α1 (and its α2KK-mimicking single- and double-lysine substitutions). The double substitution N333K/N785K confers α2KK-like characteristics to Xenopus α1, and mutant cycle analysis reveals energetic coupling between these two residues, illustrating how α2KK's Lys308 helps to maintain high affinity for external K+ when Lys758 occupies an ion-binding site. By measuring uptake under voltage clamp of the K+-congener 86Rb+, we prove that double-lysine-substituted pumps transport 2Na+ and 1 K+ per catalytic cycle. Our results show how the two lysines contribute to generate a pump with reduced stoichiometry allowing Artemia to maintain steeper Na+ gradients in hypersaline environments.

Biochemical and biophysical characterization of natural polyreactivity in antibodies.

To become specialized binders, antibodies undergo a process called affinity maturation to maximize their binding affinity. Despite this process, some antibodies retain low-affinity binding to diverse epitopes in a phenomenon called polyreactivity. Here we seek to understand the molecular basis of this polyreactivity in antibodies. Our results highlight that polyreactive antigen-binding fragments (Fabs) bind their targets with low affinities, comparable to T cell receptor recognition of autologous classical major histocompatibility complex. Extensive mutagenic studies find no singular amino acid residue or biochemical property responsible for polyreactive interaction, suggesting that polyreactive antibodies use multiple strategies for engagement. Finally, our crystal structures and all-atom molecular dynamics simulations of polyreactive Fabs show increased rigidity compared to their monoreactive relatives, forming a neutral and accessible platform for diverse antigens to bind. Together, these data support a cooperative strategy of rigid neutrality in establishing the polyreactive status of an antibody molecule.

Simulating the Voltage-Dependent Fluorescence of Di-8-ANEPPS in a Lipid Membrane.

Voltage-sensitive fluorescent dyes such as di-8-ANEPPS (di-8-aminonaphthylethylenepyridinium propylsulfonate) are powerful tools to study biological membranes. Its fluorescence is affected by changes in the membrane potential and other factors, requiring extensive calibration to extract meaningful quantitative results. The amphiphilic di-8-ANEPPS molecule is expected to bind at the membrane-solution interface. However, atomic-level information is sparse about its position and orientation in the membrane, especially in regards to how the latter dynamically fluctuates to affect the observed fluorescence. In the present work, molecular dynamics simulations of the ground and excited states of di-8-ANEPPS embedded in a DPPC membrane as represented by classical force fields were used to investigate how the fluorescence is affected by externally applied potential. The calculations reproduce the shifts in the wavelength of emission as a function of voltage that are observed experimentally, indicating that the approach can help better understand the various factors that can affect the fluorescence of membrane-bound dyes.

A genetically encoded sensor for visualizing leukotriene B4 gradients in vivo.

Leukotriene B4 (LTB4) is a potent lipid chemoattractant driving inflammatory responses during host defense, allergy, autoimmune and metabolic diseases. Gradients of LTB4 orchestrate leukocyte recruitment and swarming to sites of tissue damage and infection. How LTB4 gradients form and spread in live tissues to regulate these processes remains largely elusive due to the lack of suitable tools for monitoring LTB4 levels in vivo. Here, we develop GEM-LTB4, a genetically encoded green fluorescent LTB4 biosensor based on the human G-protein-coupled receptor BLT1. GEM-LTB4 shows high sensitivity, specificity and a robust fluorescence increase in response to LTB4 without affecting downstream signaling pathways. We use GEM-LTB4 to measure ex vivo LTB4 production of murine neutrophils. Transgenic expression of GEM-LTB4 in zebrafish allows the real-time visualization of both exogenously applied and endogenously produced LTB4 gradients. GEM-LTB4 thus serves as a broadly applicable tool for analyzing LTB4 dynamics in various experimental systems and model organisms.

Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.

A significant challenge faced by atomistic simulations is the difficulty, and often impossibility, to sample the transitions between metastable states of the free-energy landscape associated with slow molecular processes. Importance-sampling schemes represent an appealing option to accelerate the underlying dynamics by smoothing out the relevant free-energy barriers, but require the definition of suitable reaction-coordinate (RC) models expressed in terms of compact low-dimensional sets of collective variables (CVs). While most computational studies of slow molecular processes have traditionally relied on educated guesses based on human intuition to reduce the dimensionality of the problem at hand, a variety of machine-learning (ML) algorithms have recently emerged as powerful alternatives to discover meaningful CVs capable of capturing the dynamics of the slowest degrees of freedom. Considering a simple paradigmatic situation in which the long-time dynamics is dominated by the transition between two known metastable states, we compare two variational data-driven ML methods based on Siamese neural networks aimed at discovering a meaningful RC model─the slowest decorrelating CV of the molecular process, and the committor probability to first reach one of the two metastable states. One method is the state-free reversible variational approach for Markov processes networks (VAMPnets), or SRVs─the other, inspired by the transition path theory framework, is the variational committor-based neural networks, or VCNs. The relationship and the ability of these methodologies to discover the relevant descriptors of the slow molecular process of interest are illustrated with a series of simple model systems. We also show that both strategies are amenable to importance-sampling schemes through an appropriate reweighting algorithm that approximates the kinetic properties of the transition.

Drude Polarizable Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for Saturated and Monounsaturated Zwitterionic Lipids.

Accurate empirical force fields of lipid molecules are a critical component of molecular dynamics simulation studies aimed at investigating properties of monolayers, bilayers, micelles, vesicles, and liposomes, as well as heterogeneous systems, such as protein-membrane complexes, bacterial cell walls, and more. While the majority of lipid force field-based simulations have been performed using pairwise-additive nonpolarizable models, advances have been made in the development of the polarizable force field based on the classical Drude oscillator model. In the present study, we undertake further optimization of the Drude lipid force field, termed Drude2023, including improved treatment of the phosphate and glycerol linker region of PC and PE headgroups, additional optimization of the alkene group in monounsaturated lipids, and inclusion of long-range Lennard-Jones interactions using the particle-mesh Ewald method. Initial optimization targeted quantum mechanical (QM) data on small model compounds representative of the linker region. Subsequent optimization targeted QM data on larger model compounds, experimental data, and dihedral potentials of mean force from the CHARMM36 additive lipid force field using a parameter reweighting protocol. The use of both experimental and QM target data during the reweighting protocol is shown to produce physically reasonable parameters that reproduce a collection of experimental observables. Target data for optimization included surface area/lipid for DPPC, DSPC, DMPC, and DLPC bilayers and nuclear magnetic resonance (NMR) order parameters for DPPC bilayers. Validation data include prediction of membrane thickness, scattering form factors, electrostatic potential profiles, compressibility moduli, surface area per lipid, water permeability, NMR T1 relaxation times, diffusion constants, and monolayer surface tensions for a variety of saturated and unsaturated lipid mono- and bilayers. Overall, the agreement with experimental data is quite good, though the results are less satisfactory for the NMR T1 relaxation times for carbons near the ester groups. Notable improvements compared to the additive C36 force field were obtained for membrane dipole potentials, lipid diffusion coefficients, and water permeability with the exception of monounsaturated lipid bilayers. It is anticipated that the optimized polarizable Drude2023 force field will help generate more accurate molecular simulations of pure bilayers and heterogeneous systems containing membranes, advancing our understanding of the role of electronic polarization in these systems.

Hydrogen peroxide production by epidermal dual oxidase 1 regulates nociceptive sensory signals.

Keratinocytes of the mammalian skin provide not only mechanical protection for the tissues, but also transmit mechanical, chemical, and thermal stimuli from the external environment to the sensory nerve terminals. Sensory nerve fibers penetrate the epidermal basement membrane and function in the tight intercellular space among keratinocytes. Here we show that epidermal keratinocytes produce hydrogen peroxide upon the activation of the NADPH oxidase dual oxidase 1 (DUOX1). This enzyme can be activated by increasing cytosolic calcium levels. Using DUOX1 knockout animals as a model system we found an increased sensitivity towards certain noxious stimuli in DUOX1-deficient animals, which is not due to structural changes in the skin as evidenced by detailed immunohistochemical and electron-microscopic analysis of epidermal tissue. We show that DUOX1 is expressed in keratinocytes but not in the neural sensory pathway. The release of hydrogen peroxide by activated DUOX1 alters both the activity of neuronal TRPA1 and redox-sensitive potassium channels expressed in dorsal root ganglia primary sensory neurons. We describe hydrogen peroxide, produced by DUOX1 as a paracrine mediator of nociceptive signal transmission. Our results indicate that a novel, hitherto unknown redox mechanism modulates noxious sensory signals.

Mechanism of voltage gating in the voltage-sensing phosphatase Ci-VSP.

Conformational changes in voltage-sensing domains (VSDs) are driven by the transmembrane electric field acting on the protein charges. Yet, the overall energetics and detailed mechanism of this process are not fully understood. Here, we determined free energy and displacement charge landscapes as well as the major conformations visited during a complete functional gating cycle in the isolated VSD of the phosphatase Ci-VSP (Ci-VSD) comprising four transmembrane helices (segments S1 to S4). Molecular dynamics simulations highlight the extent of S4 movements. In addition to the crystallographically determined activated "Up" and resting "Down" states, the simulations predict two Ci-VSD conformations: a deeper resting state ("down-minus") and an extended activated ("up-plus") state. These additional conformations were experimentally probed via systematic cysteine mutagenesis with metal-ion bridges and the engineering of proton conducting mutants at hyperpolarizing voltages. The present results show that these four states are visited sequentially in a stepwise manner during voltage activation, each step translocating one arginine or the equivalent of ∼1 e0 across the membrane electric field, yielding a transfer of ∼3 e0 charges in total for the complete process.

Galvani Offset Potential and Constant-pH Simulations of Membrane Proteins.

A central problem in computational biophysics is the treatment of titratable residues in molecular dynamics simulations of large biological macromolecular systems. Conventional simulation methods ascribe a fixed ionization state to titratable residues in accordance with their pKa and the pH of the system, assuming that an effective average model will be able to capture the predominant behavior of the system. While this assumption may be justifiable in many cases, it is certainly limited, and it is important to design alternative methodologies allowing a more realistic treatment. Constant-pH simulation methods provide powerful approaches to handle titratable residues more realistically by allowing the ionization state to vary statistically during the simulation. Extending the molecular mechanical (MM) potential energy function to a family of potential functions accounting for different ionization states, constant-pH simulations are designed to sample all accessible configurations and ionization states, properly weighted according to their Boltzmann factor. Because protonation and deprotonation events correspond to a change in the total charge, difficulties arise when the long-range Coulomb interaction is treated on the basis of an idealized infinite simulation model and periodic boundary conditions with particle-mesh Ewald lattice sums. Charging free-energy calculations performed under these conditions in aqueous solution depend on the Galvani potential of the bulk water phase. This has important implications for the equilibrium and nonequilibrium constant-pH simulation methods grounded in the relative free-energy difference corresponding to the protonated and unprotonated residues. Here, the effect of the Galvani potential is clarified, and a simple practical solution is introduced to address this issue in constant-pH simulations of the acid-sensing ion channel (ASIC).

Committor-Consistent Variational String Method.

The treatment of slow and rare transitions in the simulation of complex systems poses a great computational challenge. A powerful approach to tackle this challenge is the string method, which represents the transition path as a one-dimensional curve in a multidimensional space of collective variables. Commonly used strategies for pathway optimization include aligning the tangent of the string to the local mean force or to the mean drift determined from swarms of short trajectories. Here, a novel strategy is proposed, allowing the string to be optimized based on a variational principle involving the unidirectional reactive flux expressed in terms of the time-correlation function of the committor. The method is illustrated with model systems and then probed with the alanine dipeptide and a coarse-grained model of the barstar-barnase protein complex. Successive iterations variationally refine the string toward an optimal transition pathway following the gradient of the committor between two metastable states.