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.

The high energy-efficiency of life is hard to understand only with classical physics. Many efforts have been made to study its mechanism based on quantum mechanics; the progress is nevertheless slow due to lack of experimental evidence with living cells. Here, combining experiments on cells, tissues and mitochondria with a theoretical model, we demonstrate a quantum state of mitochondria, which can be employed to modulate ATP production in living cells. We found an anomalous 71.0-THz oscillation mode only in living cells and tissues, which is highly determined by intact structure of mitochondria, and cannot be assigned to any specific molecules. Based on experimental data, a quantum model of light-matter coupling was introduced to trace the origin of this mode. Our calculations suggest a quantum superposition state of functional mitochondrion that forms by the coupling of light and lipid CH2 bonds in functional cristae, and induces a splitting of the intrinsic CH2 vibration mode of 87 THz to two levels at 71 THz and 103 THz, respectively. The former can be observed only in living cells and tissues; whereas the latter falls in the range (90-110 THz) of biomolecular and water vibrations, thus indistinguishable. Additional experiments revealed this mitochondrial quantum state able to serve as an efficient channel to modulate ATP production. Our findings provide a quantum mechanics view for understanding living cells, and it will be interesting to further explore whether such quantum state could act as a channel for energy metabolism, and even information transmission in life.

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.

Nuclear magnetic resonance (NMR) spectroscopy provides atomic-resolution insights into biomolecular structure, dynamics and interactions under non-perturbative conditions. However, the widespread applicability of this technique continues to be limited by its intrinsically low sensitivity. This drawback is particularly severe in the case of concentration-limited samples. Low-concentration photochemically induced dynamic nuclear polarization (LC-photo-CIDNP) is an optically enhanced NMR hyperpolarization strategy capable of boosting NMR sensitivity in situ by several orders of magnitude under physiologically relevant environments. Yet, the inherent nature of the LC-photo-CIDNP phenomenon confines the achievable enhancements to only a subset of nuclei, thus limiting the extent of attainable residue-specific information. Here, we overcome this shortcoming by integrating LC-photo-CIDNP with Total Correlation Spectroscopy (TOCSY) to propagate initial hyperpolarization to other nuclei via scalar-coupled spin networks. First, we demonstrate efficient through-bond transfer of LC-photo-CIDNP enhanced signals throughout entire intramolecular atomic frameworks via 1D 1H- and 13C-detected experiments. Second, we extend the approach to a model protein and demonstrate efficient polarization transfer via 1D and 2D 1H-detected 13C-TOCSY LC-photo-CIDNP. Intrinsically photo-CIDNP-inactive nuclei can thus be visualized via 13C-1H correlations within minutes down to 10 M concentration. In all, this work establishes a general strategy to achieve through-bond propagation of LC-photo-CIDNP hyperpolarization. This technology enables gaining residue-specific structural insights on amino acids and proteins at previously unattainable low concentrations.

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.

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.

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.

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.

Designing photochemical properties in proteins is a frontier challenge in biomolecular design, which, when realised, will open new avenues in catalysis and biomolecular electronics. Here we demonstrate an integrated computational design framework, supported by ultrafast spectroscopy, combining protein design, quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) and electronic structure calculations, to control flavin excited states in de novo proteins. Iterative design yields FC-4D2, a flavocytochrome with covalently bound flavin and non-covalently bound heme B and Fl-4D2, where the heme-binding site is removed. Both proteins are highly thermostable, and access all physiologically relevant flavin redox and protonation states, while high-resolution X-ray crystallography confirms the near-[A]ngstrom-level accuracy in design. Incorporating QM/MM MD and linear-response time-dependent density functional theory (LR-TDDFT) into the design workflow enables engineering of a flavin-tryptophan charge-transfer variant, Fl-4D2CT, that modifies the flavin absorption spectrum, substantially alters its redox properties and suppresses fluorescence. Ultrafast studies reveal how protein design reshapes the excited state relaxation timescale and mechanism. Our study establishes a modular strategy for programming flavin photochemistry in designed proteins.

G protein-coupled receptors (GPCRs) represent the largest membrane protein family in the human body and the major targets of FDA-approved medications. Unlike some other physiological proteins, GPCRs are highly plastic and often exist as collections of conformational ensembles in the environment to exert their function. Therefore, understanding how a GPCR adopts different conformational ensembles in response to ligands and signaling partners is essential for drug development. 19F-NMR has emerged as one of the most promising tools in this regard; however, a well-established 19F-labeling system is still lacking, including an effective 19F probe that meets both environmental susceptibility and NMR sensitivity requirements, even though progress has been made dramatically in recent years. Thus, this manuscript reports a novel conformational probe (4-(bromomethyl)-ethoxy-2-(trifluoromethyl)benzene, BTMB) that offers paramount benefits for future GPCR conformational dynamics studies. To rationalize the mechanism behind BTMB's superiority for future probe development, we performed MD simulations, referenced to the current state-of-the-art probe, BTFMA. Our study suggests that the spatial proximity of aromatic residues significantly influences the chemical shift dispersion and linewidths of related 19F-probe-profiled conformational states, as well as the rigidity and compactness of -CF3 in the target protein.

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.

Protein dynamics is fundamental to understand mechanisms. Although atomistic Molecular Dynamics (MD) remains the gold standard for predicting protein motions, its computational cost limits applications at large scales. Here, we present a coarse-grained (CG) simulation framework that combines Elastic Network Models (ENMs) with implicit-solvent Brownian Dynamics (BD) to efficiently generate protein conformational ensembles from native states. Benchmarking the method on more than 8,500 proteins against large datasets of atomistic MD trajectories and multi-state ensembles from Nucleic Magnetic Resonance (NMR), we show that short ENM-BD simulations can reproduce the residue fluctuation profiles and the dominant protein motions with remarkable accuracy. Correlations of residue fluctuations often exceeded 80%, with many proteins showing agreements above 90%, while Principal Component Analysis (PCA) revealed strong correspondences between the essential motions in the ENM-BD simulations and those observed in MD and NMR ensembles. Following CG-to-all-atom reconstruction and short energy minimization, the ENM-BD conformers also achieve high stereochemical quality, which enables their usage in downstream atomistic applications. Finally, we show that the stochastic dynamics emerging from BD trajectories closely aligns with the normal modes encoded in the underlying elastic network, highlighting that most of the intrinsic dynamics of proteins is already embedded in their native 3D topology.

Simulations have emerged as a pillar in biophysics to understand behavior at the molecular scale, in particular for proteins such as intrinsically disordered proteins. These often show transient folding with long-lived states that are challenging to efficiently sample using conventional simulations. Here, we show that Hamiltonian replica-exchange at the level of hydrogen bonds can be used to systematically explore the conformational landscape of proteins. Investigating the IM30 protein, a partially alpha helical protein, under the PLUM coarse-grain force-field, we show that this approach straightforwardly allows characterization of local helix stability, which would be otherwise hard to obtain. For instance, we show that some regions of the conserved coiled-coil structure are less stable than others. By combining the different replicas with principal component analysis, we are able to dissect the conformational landscape of the IM30 protein. Taken together, this indicates that enhanced sampling remains an important part of simulations, allowing insights that cannot be easily obtained in traditional simulations.

Allosteric and cryptic binding sites provide unique opportunities in drug discovery for achieving selectivity, reducing toxicity and unlocking challenging therapeutical targets. Yet, cryptic pockets are particularly elusive in that they are difficult to capture with experimental methodologies, and therefore often remain undetected. Computational approaches for flexible cryptic site detection traditionally involve extensive unbiased molecular dynamics simulations, or more efficient but computationally still highly demanding biased MD alternatives that make use of enhanced sampling along low-frequency motions of the protein structure. Here we utilize a new variant of the recently introduced and orders of magnitude faster, MD-free COVIB algorithm to identify cryptic pockets efficiently in two challenging targets, the KRAS oncoprotein and the N-terminal domain of the STAT5B transcription factor. Using experimental and AI-modelled structures, respectively, we identify numerous cryptic sites, some of which have not yet been reported. The sites are prioritized and validated with our pharmacophore optimized photoaffinity-tagged fragment library via MS-based peptide mapping and NMR spectroscopy. Besides identifying new, targetable regions on the surface of these important oncotargets, we have established a protocol combining COVIB-based cryptic pocket detection and photoaffinity-based experimental peptide mapping as a robust approach for exploring unique and tractable cryptic pockets.

Molecular flexibility governs how molecules behave, reorganize, and respond to their environment. Although experiments measure molar entropy for small molecules and molecular dynamics (MD) simulations capture molecular motions, quantifying configuration entropy and the concerted internal motions such as torsion rotations, angle bending, and their couplings are central to understanding thermodynamic behavior but remains challenging. To dissect these contributions, we used MD trajectories and developed an internal coordinate PC-entropy (iPC-entropy) method to probe the origins of entropy and reveal how specific motions shape the thermodynamic landscape. The studies accurately captured molar entropy, identified key torsional motions as major contributors, and uncovered a critical angle-torsion coupling in which angle bending was strongly correlated with torsional rotation, a coupling that increases nonlinearly with molecular size. Evaluating entropic changes upon protein-ligand binding reveals that dominant entropic penalty arises from ligand dihedral rigidification rather than protein reorganization and highlights the specific dihedral rotations that become restricted. We also suggest systematic corrections for approaches considering solely rotamers to reliably reproduce the relative entropic penalty in computer-aided drug discovery. Together, our findings elucidate the molecular origins of entropy and entropy changes. In addition, we can quantify and illustrate the internal motions that strongly shape binding thermodynamics, thereby offering mechanistic insights to guide drug development.

A foundational tenet of modern biophysics dictates that protein-solvent interactions and charge-transport dynamics operate strictly within microscopic, sub-nanometer boundaries where long-range coordination is natively suppressed by rapid room-temperature thermal dephasing. Here, we report an unexpected phenomenon that fundamentally challenges this paradigm: the long-distance, quantum-coherent communication between isolated protein samples in aqueous solution across macroscopic spatial separations. By mapping the distance-dependence of a weak, protein-emitted electromagnetic field on the response dynamics of an adjacent complex, we demonstrate that this inter-protein communication establishes a stable, intermolecular vibronic coupling operating across millimeter-scale distances up to at least 40 mm. This field interference induces a robust quantum coherence that lowers the local potential energy surface, transforming the coupled protein-solvent network into an un-attenuated, low-barrier energy transfer conduit. This stabilized pathway launches a topologically protected, non-dispersive soliton that exhibits exceptional phase stability against room-temperature thermal dephasing. These findings provide definitive empirical evidence of a sophisticated, field-driven quantum infrastructure embedded within a biological macromolecule. More explicitly, they reveal that a protein can possess an inherent capacity to preserve coherent wave propagation over macroscopic scales previously deemed impossible in room-temperature aqueous environments.

Biomolecular condensates formed by intrinsically disordered proteins require molecular models that accurately describe proteins in both dilute solution and condensed phases. Explicit-solvent coarse-grained models offer an attractive balance between chemical resolution and computational efficiency. Yet, it remains unclear whether improving dilute-state properties is sufficient to obtain an accurate description of condensates. Here, we address this question by introducing minimal modifications to the Martini 3 force field that combine recent advances in bonded interactions with refined protein-water interactions and strengthened glycine self-interactions, while preserving the underlying chemical transferability of the model. The resulting model substantially improves the description of single-chain conformations across a diverse benchmark of disordered proteins. We then investigate phase separation of the well-characterized low-complexity domain of heterogeneous nuclear ribonucleoprotein A1 and its sequence variants. The model reproduces several key physicochemical properties of biomolecular condensates, including chain expansion in the dense phase, sequence-dependent intermolecular contacts, protein diffusion and its relation to single-chain dimensions, and hydration, while revealing quantitative limitations in condensate density, phase equilibria, and ion partitioning. Our results show that improving dilute-state behaviour translates into a better description of condensed-phase properties, including condensate density, but is not sufficient to quantitatively reproduce the equilibrium between the dilute and dense phases.

Interpreting single-molecule force spectroscopy (SMFS) data from optical tweezers with steered molecular dynamics (SMD) is hindered by a severe timescale mismatch: SMD trajectories span nanoseconds to microseconds, while SMFS experiments unfold over milliseconds to seconds, forcing SMD to pull six to nine orders of magnitude faster than experiment and systematically distorting rupture forces, molecular pathways, and kinetic intermediates. We introduce force-probe coupled steered molecular dynamics (fp-SMD), which explicitly propagates the force probe (the optically trapped bead) alongside the biomolecule within a single classical Hamiltonian description of the coupled molecule-linker-bead system. A Predict-Correct Trajectory Propagation (PCTP) scheme resolves the resulting computational bottleneck by decoupling the fast molecular relaxation from the slow, macroscopic bead motion, using metadynamics and transition-state theory to identify the kinetically resolved molecular intermediate reached at each macroscopic time step. On the cholesterol-{beta}-cyclodextrin ({beta}-CD) host-guest complex, fp-SMD reproduces the experimental rupture-force histogram directly at matched conditions, without extrapolation from standard SMD. fp-SMD further resolves how applied force reshapes the molecular escape pathway itself--cholesterol dissociates via a high-angle route at low force and a direct, small-angle route at high force--providing, to our knowledge, the first direct structural evidence for force-induced pathway switching within small host-guest systems believed to underlie catch-slip bond behavior. fp-SMD/PCTP thus offers a general route to simulating force-probe-coupled biomolecular mechanics at experimentally relevant timescales.