The Journal of Physical Chemistry Letters

Far-Red (FR) Light Photoacclimation (FaRLiP) enables cyanobacteria to extend photosynthetic activity into the far-red region by extensively remodelling Photosystem I (PSI), including the replacement of several core subunits with paralogs that coordinate the red-shifted chlorophyll f (Chl f). The binding positions of Chls f are still a matter of debate, with the most recent structural findings supporting the location of a single Chl f molecule within the reaction centre (RC) at the so-called A-1B site. This was in turn suggested to strongly affect electron transfer (ET) directionality leading to an almost monodirectional transfer along the B branch in FR-PSI RC. Here, we directly probe ET in FR-PSI by characterising the photogenerated [P700A1-] spin-correlated radical pair using complementary pulse and Time-Resolved (TR) Electron Paramagnetic Resonance (EPR) spectroscopy at cryogenic temperature. Electron spin-echo decay kinetics are distinctly biexponential, indicating the formation of two charge-separated states. Consistently, out-of-phase ESEEM traces are quantitatively described by two modulation frequencies arising from different dipolar interactions, while TR-EPR spectra are accurately simulated by the combined contributions of [P700A1A-] and [P700A1B-] radical pairs. These results provide direct spectroscopic evidence that both the A and B branches remain photochemically active in FR-PSI. The conservation of bidirectional ET, even when considering the presence of a single Chl f molecule in the RC, further implies that the two radical pairs originate from a common primary electron donor. This finding identifies P700 as the most likely primary donor and argues against a mechanism in which the RC Chl f initiates charge separation.

Protein folding is the process by which a polypeptide chain organizes into its three-dimensional structure through a balance of stabilizing and destabilizing interactions encoded by the sequence. A central question in protein biophysics is how thermodynamic factors guide a polypeptide toward its native folded state despite the rugged energy landscape and the competing influence of nonnative interactions. In many biomolecular processes, cooperativity provides a mechanism by which multiple weak interactions act collectively to generate a robust response. In the context of protein folding, such cooperative effects may arise when the formation of one native contact enhances the stability or likelihood of nearby native contacts, thereby promoting collective organization toward the folded state. At the same time, folding is opposed by the much larger number of non-native interactions, whose heterogeneity can introduce frustration and destabilize folding even when the average native bias favors the folded phase. The interplay of these competing effects in determining foldability remains unclear in statistical-mechanical models. Here, we address this problem using a one-dimensional spin-glass model of protein folding with explicit shared-residue cooperative interactions encoded through wedge-based motifs. We show that modest cooperative bias can stabilize folding even where the noncooperative system remains unfolded, whereas non-native energetic fluctuation suppresses folding and shifts the transition to higher cooperative strengths. We further find that partial cooperative coverage is sufficient to lower the folding threshold. Therefore, the model provides a mean-field framework for incorporating cooperative interaction strength into the native one-dimensional model of protein folding and for describing how local cooperativity reshapes the folding transition.

Understanding the role of the protein backbone and side chains on cosolvent-induced stabilization is essential for a molecular picture of osmolyte action. The dominant view has been that protecting osmolytes stabilize proteins primarily through unfavorable interactions with the peptide backbone - the osmophobic effect - with side chains playing a minor or opposing role. By revisiting the decomposition of amino acid transfer free energies with proper account of the mutual shielding between backbone and side-chain groups, we derive a transfer model that is consistent with experimental denaturation m-values for urea and protecting osmolytes simultaneously - a feat neither the established nor the previously proposed universal-backbone models could achieve alone. A mechanism-dependent interpretation of backbone accessibility is proposed: geometric for excluded cosolvents, complete for binders where cosolvent-backbone interactions are specific. The model reveals that for all strong protecting osmolytes, including TMAO, sarcosine, sucrose, trehalose, and sorbitol, both backbone and side chains contribute favorably to protein stabilization, with side-chain contributions comparable to or exceeding those of the backbone. For urea, the model recovers the known balanced backbone and side-chain contributions to denaturation when the directional nature of urea-backbone hydrogen bonding is accounted for, which makes the backbone accessible to urea regardless of side-chain shielding. Weaker protectants such as proline, betaine, and glycerol are distinguished by competing backbone and side-chain effects that partially cancel. These results extend the osmophobic effect to protein side chains and establish a three-tier classification of osmolyte action: cooperative backbone and side-chain stabilization, cooperative destabilization, and competing contributions. The greater sensitivity of the model predictions to side-chain composition provides avenues for experimental validation of the underlying physical assumptions and for protein engineering.

The process of drug unbinding is of immense importance in the field of biophysics and therapeutics. The behavior of these systems is greatly influenced by their thermodynamic and kinetic properties. Therefore, it is crucial to accurately estimate the ligand binding free energies and rate of ligand dissociation, yet these processes are often governed by rare event transitions that lie beyond the reach of standard brute-force molecular dynamics simulations. While enhanced sampling simulations offer a solution, their efficacy is strictly contingent upon the selection of appropriate collective variables (CVs) which is non-trivial for complex systems like protein-ligand complexes. In this study, we present a method to derive optimized CV from transition state region (TS) via an interpretable machine learning (ML) model, Elastic Net. By employing some physically intuitive order parameters, the derived optimized CV from the TS-region greatly accelerate ligand binding-unbinding transitions and achieves rapid free energy surface (FES) convergence across diverse systems including buried and solvent exposed active sites such as Trpsin-benzamidine complex, host-guest systems and sodium epoxidase etc. Intriguingly significant contribution of the ligand hydration is found in the optimized CV which depicts crucial role of solvent in driving ligand binding-unbinding transitions. The estimated binding free energies for different protein-ligand complexes match quite well with experiments, while maintaining a low computational cost. The derived optimized CV is also used to calculate the ligand residence times across different systems and calculated residence times are within the experimental range for all systems, again with very little computational costs. Moreover, we show that the optimized CV constructed from TS region via an interpretable ML model is transferable across diverse systems, offering a robust and scalable framework for drug discovery and investigation of complex biomolecular recognition.

All-atom simulations with explicit solvent provide the most detailed and accurate description of dynamics and mechanisms in intrinsically disordered proteins and their condensates. However, interactions involving charged residues and ions remain a persistent source of systematic error. Here we introduce an osmometry-guided optimization strategy that directly targets residue-residue, residue-ion and ion-ion interactions. Osmotic pressure provides key experimental information on molecular interactions and can be calculated directly and rapidly from simulations, enabling efficient iterative force-field optimization. The resulting parameters improve agreement of all-atom simulations with a range of experimental data: single-molecule FRET measurements for 16 monomeric intrinsically disordered regions; NMR relaxation data for a complex between an IDP and a folded protein domain; and mean FRET efficiencies and chain reconfiguration times of IDPs in biomolecular condensates of highly charged proteins. For such condensates, simulations with an osmometry-calibrated force field provide the missing link for predicting condensate dynamics across length and time scales. The presented optimization strategy is broadly extensible to other interaction classes, including those governing protein-DNA and protein-RNA assemblies.

Neutron scattering is a powerful technique to investigate atomic structures and molecular dynamics of proteins at the nano-scale. When it comes to dynamics, incoherent and coherent scattering respectively provide information on the single and collective dynamics of nuclei. In proteins, hydrogen has the highest incoherent cross-section, and it is common practice to overlook the contribution of coherent terms stemming from all nuclei. However, the fast collective dynamics of heavier nuclei could also be studied if coherent scattering and incoherent scattering were experimentally separated. The recent advent of polarized neutron spectroscopy with sufficient flux and energy resolution has made it possible, and opens new perspectives to investigate the relative importance of coherent scattering and the information it provides on biological samples. The present study reports on the use of polarized quasi-elastic neutron scattering (QENS) and the application of a minimalistic model adapted to both individual and collective dynamics. Using a perdeuterated green fluorescent protein as a model globular protein, the study provides an interpretation of the dynamical parameters obtained with QENS, and a comparative study of the Elastic Coherent and Incoherent Scattering Factor. Based on both experiments and calculations, we discuss the relative importance of distinct and self components of coherent scattering, which is often wrongly assumed to be representative of collective dynamics only. The results highlight the current impediments rendering complicated a straightforward analysis of fast collective dynamics in hydrated protein samples.

Biomolecular condensates exhibit spontaneous electrochemical microenvironments characterized by asymmetric ion distributions and pH gradients that emerge from protein-sequence-dependent charge regulation. Despite their biological importance, mechanistic understanding of these microenvironments has been constrained by the absence of computationally tractable frameworks capable of treating proton exchange, counterion partitioning, and buffer equilibria on consistent thermodynamic footing. Here, we introduce the buffered Charge-Regulation Monte Carlo (b-CR-MC) framework, which couples grand-canonical exchange of ions and buffer species with explicit charge regulation of titratable residues. By extending the CR-MC ion-merging strategy to multicomponent reservoirs and employing the Restricted Primitive Model, b-CR-MC achieves computational efficiency while maintaining thermodynamic rigor, with quantitative agreement to the more expensive generalized G-RxMC approach. Applied to full-length FUS (net positive) and PGL-3 (net negative) under physiological conditions, the framework reveals sequence-dependent pH gradients: the dense phase of FUS exhibits an alkaline shift, while PGL-3 exhibits an acidic shift, in both cases driving the condensate interior toward the protein's isoelectric point. Slab-geometry simulations further resolve the Donnan potential and continuous ion profiles across the condensate interface, confirming the direction and magnitude of these electrochemical shifts. Additionally, we identify spatially resolved buffer depletion within dense phases, establishing that dynamic charge regulation is a primary determinant rather than a secondary correction to condensate electrochemistry. By establishing a sequence-resolved, thermodynamically consistent computational platform, b-CR-MC enables quantitative prediction of how mutations and post-translational modifications reprogram condensate microenvironments across biological and pathophysiological contexts.

Residual dipolar couplings (RDCs) obtained via magnetic field-induced alignment offer a powerful, media-free approach for the structural analysis of biomolecules. However, their detection in short, fast-tumbling nucleic acids remains elusive at conventional magnetic fields due to insufficient alignment and sensitivity. Here, we demonstrate the direct observation of these RDCs at 1.3 GHz in a 14-mer hairpin fragment derived from an HIV-1 Vif-targeting aptamer. The 1JNH scalar couplings of imino protons were measured at fields ranging from 600 MHz to 1.3 GHz. While the coupling constants remained invariant between 600 and 900 MHz, a clear deviation was exclusively captured at 1.3 GHz for all base-paired stem residues, demonstrating the first media-free detection of field-induced RDCs in a short RNA of this size. This breakthrough arises from a synergistic B07/2 scaling, combining enhanced alignment ({propto} B02) and sensitivity ({propto} B03/2). These RDCs showed excellent agreement with the NOE-derived structure. Additionally, the flexible loop residue G8 exhibited no detectable RDC, but displayed a field-dependent TROSY/anti-TROSY intensity inversion at 1.3 GHz, reflecting an unusual 1H chemical shift anisotropy (CSA) tensor that corroborates the local base-packing environment. Our findings highlight 1.3 GHz NMR as an indispensable tool for the structural analysis of short RNAs.

Mitochondrial complex I is a major source of reactive oxygen species (ROS), but whether radical pair spin dynamics contribute to the regulation of ROS-associated reactions remains unknown. Here we integrate cryo-electron microscopy structure-guided oxygen sampling with radical pair quantum dynamics to determine how the molecular architecture surrounding flavin mononucleotide (FMN) shapes modelled spin-dependent radical-pair reaction yields. Monte Carlo sampling revealed a broad ensemble of sterically accessible oxygen configurations, whereas spin sensitivity was concentrated within a restricted near-contact region centred at approximately 3.3-3.4 [A] from the FMN reference centre. This localisation was defined by the integration of structural accessibility with magnetic field and spin dephasing sensitivities and spatially overlapped with an exchange-hyperfine crossover regime favourable for singlet-triplet interconversion. Simulations of structural fluctuations further show that equivalent perturbations generated greater variability in singlet reaction yields within a hotspot than outside it, identifying a localized regime of enhanced structural responsiveness. These results suggest that the FMN binding pocket may act as a structure-dependent amplification layer that converts small changes in radical pair geometry into heterogeneous spin-dependent reaction outcomes. Our findings establish a framework linking experimentally resolved protein architecture to radical pair spin dynamics and identify structural constraints that may shape spin-dependent ROS chemistry in mitochondrial complex I.

[FeFe]-hydrogenases are very active biocatalysts for H2 conversion. However, their active site is vulnerable to irreversible degradation initiated by O2 binding at the catalytic iron ion (Fed) of the active center. CbA5H, the [FeFe]-hydrogenases from Clostridium beijerinckii exhibits stability towards oxygen (O2) due to its ability to reversibly enter an inactive state termed Hinact upon contact with O2. We previously proposed that the close distance of approximately 3.1 [A] between the thiol of a nearby cysteine (C367) and the Fed, based on a 2.9 [A] crystal structure of CbA5H in the Hinact state, enables their binding to each other. This binding therefore was suggested to shield the Fed from O2 damage. However, there is currently a lack of evidence to support this hypothesis. Furthermore, density functional theory (DFT) calculations based on a homologous model favored hydroxide as the binding ligand of the Fed over the thiol of C367. In this study, we present the crystal structure of CbA5H in the Hinact state at an improved resolution of 2.15 [A]. The structure reveals a direct binding between the thiol of C367 and the Fed with a distance of approximated 2.77 [A] which is well supported by our DFT calculations based on the new crystallographic data. It is noteworthy that the 2.77 [A] bond distance is strikingly long when compared with other iron-sulfur bonds. This finding may provide a crucial foundation for understanding the rapid reversibility of the Hinact state.

The fidelity of molecular dynamics (MD) simulations fundamentally depends on the quality and coverage of the ab initio data used to parameterize the underlying force field, yet the role of side-chain conformational space remains insufficiently explored. In this study, we systematically investigate how comprehensive ab initio sampling of dipeptide conformations--specifically targeting side-chain degrees of freedom--impacts force field accuracy and MD simulation predictive power. We present the Protein Unit Dataset Plus (PUD+), a 40-million-conformation quantum mechanical dataset featuring unprecedented coverage of both backbone and side-chain conformational space. Machine learning force fields trained on PUD+ and integrated into AI2BMD simulations demonstrate superior energy and force prediction accuracy, capturing high-fidelity protein folding dynamics and the conformational flexibility of long-side-chain systems. Furthermore, leveraging PUD+ to reparameterize the CMAP term of the classical ff19SB force field markedly improves the description of intrinsically disordered protein (IDP) dynamics and IDP-ligand binding. Collectively, these results demonstrate that ab initio sampling of dipeptide side-chain conformations enables high-fidelity modeling of protein dynamics across both AI-driven and classical simulation paradigms.

Photoprotection against excess energy is essential for the survival of photosynthetic organisms under adverse conditions. In plants, excess energy can be dissipated as heat through non-photochemical quenching (NPQ) of chlorophyll fluorescence, involving the trimeric light-harvesting complex II (LHCII), the major antenna of photosystem II. How NPQ affects antenna proteins remains debated, especially as most studies focus on short-lived components artificially induced in vitro. Here, we characterize the effects of qH, a long-lived NPQ component, on the fluorescence properties of natively quenched LHCII. Single-molecule fluorescence measurements, combined with biochemical and biophysical ensemble approaches, reveal a larger and more quenched subpopulation of LHCII trimers exhibiting fluorescence intermittency in samples with qH compared to those without. This behavior is linked to a small conformational change that stabilizes a quenched state, enhancing photoprotection at the antenna level. These findings provide new insights into sustained NPQ and its role in regulating energy dissipation under natural light conditions.

Proteases are an important class of proteolytic enzymes having great importance in both basic science and industrial applications. While cells tightly regulate the spatio-temporal activity of different proteases for cellular homeostasis, mis-regulation often leads to adverse effects. In this context, the protease activity of papain and its activation by L-cysteine is poorly understood in the literature. Herein, we discover that the protease activity of papain can be effectively regulated via a spontaneous liquid-liquid phase separation (LLPS) pathway. We show that papain undergoes biomolecular condensation via spontaneous LLPS under macromolecular crowding through the involvement of intermolecular hydrophobic interactions. Secondary structure analyses revealed a compact conformation of phase-separated papain with increased -helix content. Although native free papain is found to be active towards synthetic and protein substrates, the proteolytic digestion produces heterogeneous peptide aggregates. In contrast, we found that papain droplets remain dormant toward protein digestion due to the disulfide linkage of the active cysteine residue (Cys-25) in its compact conformational state. More importantly, we show that the protease activity of phase-separated papain can be reactivated in the presence of L-cysteine to produce uniform soluble peptide fragments. Our findings indicate that although disulfide linkages are not necessary for the phase separation of papain, upon phase separation, intermolecular interactions between phase-separated papain result in the formation of disulfide linkages involving active Cys-25 residues. The present discovery has tremendous technological importance to boost the efficacy of meat tenderization in the food industry.

Proteins are critical biomolecular machines that populate ensembles of interconverting conformations. Many biological processes depend on transitions between metastable states. Although molecular dynamics (MD) simulations provide a physically grounded route to characterize these motions, routine sampling of large-scale conformational transitions remains computationally demanding. Recent advances in protein structure prediction have created new opportunities for ensemble generation, but many existing approaches require noising inputs, task-specific training, supervised fitting on extensive MD data, or experimentally-informed restraints. Here, we introduce Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states. Unlike previous methods, Pi-Ensemble alternately leverages inverse-folding and structure-prediction models to propose intermediate conformations between known protein states, generating diverse ensembles without additional training. We evaluate Pi-Ensemble across diverse protein systems, including enzymes, transporters, receptors, and benchmark cases with reference MD simulations or experimental Double Electron-Electron Resonance (DEER) data. Pi-Ensemble recovers physically plausible intermediate conformations, captures transition pathways observed in large-scale MD simulations, and generates structures consistent with experimental distance distributions. Furthermore, Pi-Ensemble-generated conformations provide effective starting seeds for parallel MD simulations, improving conformational exploration and accelerating convergence relative to simulations initiated only from endpoint structures. These results establish sequence-guided structural interpolation as a practical strategy for probing protein conformational landscapes. By generating diverse and physically reasonable conformational proposals without long-timescale MD or model retraining, Pi-Ensemble provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of protein function.

Coarse-grained protein force fields enable simulations of biomolecular systems at length and time scales that are difficult to access with atomistic models, but achieving transferability across folded, intrinsically disordered, and multidomain proteins remains challenging. A central difficulty is that one-bead-per-residue models must represent chemically specific residue interactions while also absorbing solvent-mediated and many-body effects into a simplified energy function. Here, we present MOFF2, a transferable coarse-grained protein force field that combines residue-pair-specific interactions with a density-dependent many-body potential. MOFF2 is optimized using a two-stage strategy: bottom-up parameter learning from heterogeneous reference ensembles followed by refinement against experimental conformational observables. The resulting model provides balanced performance across ordered proteins, intrinsically disordered proteins, and multidomain proteins, and predicts condensate saturation-concentration trends for A1-LCD variant systems. Analysis of the learned parameters reveals chemically interpretable interaction patterns and density-dependent effects that explain the models improved transferability. These results demonstrate that combining a generalized coarse-grained energy function with data-driven optimization can produce a practical and interpretable force field for protein conformational and condensate simulations.

Biomolecular condensates are widespread cellular self-assembled structures with essential functions. There are suggestions of condensates formed by different proteins being near criticality. However, systematic investigation of the criticality of condensates is absent, and critical exponents defining their universality class have not been found. Here, using long-time simulations, we show that condensates exhibit typical critical phenomena, including scale-free spatiotemporal correlations, critical slowing down, divergence of correlation length and dynamic scaling. From these scaling behaviors, a set of critical exponents is determined. Based on dynamic critical exponent, diverse condensates can be divided into two distinct universality classes, arising from differences in their molecular components and interaction types.

Solution NMR spectroscopy provides atomistic measurements of proteins in a native-like biophysical state. Because these measurements are ensemble averages, it also has the potential to report on conformational diversity. However, conventional NMR structure determination typically converts experimental observables into restraints for molecular dynamics, which encode information on the mean structure but do not retain information on the underlying conformational distribution. Ensemble selection has long been proposed as an alternative, whereby experimental observables are compared directly with candidate conformers generated independently of the measurements. This allows population distributions to be inferred from the data. However, few such methods have incorporated NOESY - the richest source of structural information in protein NMR - data, due to challenges in the quantitative comparison of experimental and back-calculated spectra. To address this challenge, we previously introduced the CoMAND method, demonstrating that quantitative agreement is practical for NOESY spectra with bespoke heteronuclear editing schemes. Here we extend this approach into a framework for direct inference of protein ensembles within a flexible ensemble-selection architecture incorporating multiple classes of NMR observables. We introduce a quantitative scoring framework for comparing experimental and back-calculated observables and combine it with regularized ensemble selection and Monte Carlo simulated annealing. Integration with the OpenMM molecular dynamics engine allows conformational pools to be generated using established molecular simulation methods. Applied to human ubiquitin, the resulting ensemble provides simultaneous agreement with NOESY, residual dipolar coupling and scalar coupling data while retaining conformational diversity supported by experiment.

Cell membranes are recognized as regulators of protein organization, yet it remains unclear whether membrane-associated structural transitions arise from membrane-induced destabilization or from the reorganization of proteins already destabilized before membrane contact. Here, we address this question using cell-sized lipid-coated droplets. Native serum albumin and lysozyme showed little structural reorganization, whereas their thermally denatured forms underwent membrane-dependent {beta}-sheet formation. Denatured albumin exhibited progressively enhanced {beta}-sheet-rich organization with increasing protein-membrane attraction, whereas denatured lysozyme selectively formed a localized {beta}-sheet-rich shell at a complementary anionic membrane. Fluorescence recovery measurements revealed strong interfacial arrest in both systems. Together, these results show that membrane interfaces reorganize already destabilized proteins through electrostatic recruitment and that confinement amplifies this membrane effect. Our findings establish cell-sized droplet interfaces as active regulators of protein structural organization and provide a physical framework linking membrane confinement to amyloid-like structural transitions.

Excipient effects on protein stability are critical for biological formulations, yet their selection remains largely empirical. Here, we use molecular dynamics simulations to define unifying metrics of protein-excipient interactions at atomistic resolution. Enhanced sampling simulations of fast-folding miniproteins, including Trpzip, WAAAH-helix (an alanine-rich -helix), and Trp-Cage, were performed to capture folding transitions across diverse excipient conditions. We identified a general stabilization mechanism based on shape complementarity between protein networks and surrounding solvent networks. Stabilizing excipients were found to form solvent structures that preferentially complement each protein, as well as residues central to known folding pathways. This framework enables a unifying approach to mechanism-based excipient selection across diverse protein and solvent chemistries. More broadly, by treating protein and solvent as dynamically coupled partners, it provides a transferable strategy for understanding solvent-mediated effects in complex molecular systems.

Stereochemistry underlies structure-function relationships across biology and materials science, ranging from proteins to electronic and spintronic materials. In this work, we investigate the electron transport properties of different oligopeptide stereoisomers using experiments and computational modeling. Single-molecule electronic experiments show that stereochemical modifications in tyrosine-based peptides lead to significant variations in molecular conductance along the peptide backbone due to enhanced stacking interactions and electronic coupling of aromatic side chains. In addition, stereochemical variations in alanine-based peptides give rise to changes in conductivity due to secondary structure interactions arising from {beta}-turn conformations. All-atom molecular dynamics (MD) simulations and quantum mechanical calculations are used to understand the molecular origins of the effect of stereochemistry on the structural and electronic properties of peptides. Overall, this work shows that stereochemical modification of non-terminal amino acids effectively controls electron transport due to aromatic side chain interactions or secondary structure effects. These insights open new avenues for the molecular design of peptide-based electronic materials with enhanced function.