The Journal of Physical Chemistry Letters

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.

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.

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.

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.

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.

Reading both physical and chemical properties of a microenvironment from a single fluorophore remains a challenge. Here we demonstrate that two coexisting molecular equilibria within one near-infrared cyanine, CyC4, encode two mechanistically distinct ratiometric reporting channels. A meso-amino group and a pendant carboxylate form a tunable intramolecular hydrogen bond that toggles the dye between closed (700 nm) and open (780 nm) emissive conformers. Time-dependent density functional theory (TD-DFT) calculations show that the hydrogen bond raises the LUMO and blue-shifts the emission, establishing the 700/780 emission ratio as a local reporter of hydrogen bonding and polarity. Independently, the chromophore self-associates under crowding- and cosolvent-rich conditions into an aggregate with a blue-shifted, H-type absorption signature near 530-540 nm and a distinct emission near 610 nm upon 540 nm excitation. The intensity of this aggregate band relative to the monomer emission (Ra) serves as a ratiometric reporter of crowding and self-association. Because the two channels arise from distinct molecular equilibria (intramolecular hydrogen bonding vs. intermolecular self-association) they are largely decoupled: a glycerol titration series confirms that the self-association channel (Ra) can be moved while the hydrogen-bonding channel stays essentially fixed. Applied to protein-PEG biomolecular condensates, the two ratios move oppositely with increasing salt, showing that the interior's chemical (polarity, hydrogen bonding) and physical (packing, self-association) environments co-vary across the salt series; a single CyC4 measurement thereby maps this coupled microenvironment, providing a general strategy for multiparametric, ratiometric sensing of crowded microenvironments.

N-linked glycosylation is known to modulate the catalytic function of human carbonic anhydrase (HCA) IX, yet its influence on the underlying free-energy landscape remains largely unexplored. In the present work, we combine extensive all-atom molecular dynamics simulations with kinetic transition network analysis to investigate the effect of glycosylation on the conformational organization of the catalytic domain of HCA IX in both monomeric and dimeric forms. The multidimensional conformational space is discretized into distinct free energy minima using the distribution of reciprocal interatomic distances (DRID), and the effective barriers separating them are estimated using the max flow-min cut formalism. The corresponding free energy landscapes are visualized in terms of disconnectivity graphs, which provide a faithful representation of underlying kinetics. Minimum free energy paths, mean first passage times, as well as frustration metrics are computed to further quantify the effect of glycosylation on landscape topography. Unglycosylated systems are found to exhibit predominantly funnel-like landscapes, with a limited number of metastable states in the vicinity of the native protein fold. In contrast, glycosylation enhances landscape complexity, resulting in a wide array of relaxation timescales. Strikingly, the two glycan chains affect the landscape topography in distinct ways, despite having closely matching sequences. Dimerization couples the glycan chain dynamics, with transitions between key metastable states involving coordinated motions of both the chains. Our work illustrates that interpretation in terms of disconnectivity graphs and transition networks could reveal important insights into the organization of glycoprotein energy landscapes.

Chemically similar post-translational modifications can mediate distinct biological functions, but how proteins distinguish between them remains unclear. Sulfotyrosine (sTyr) and phosphotyrosine (pTyr) exemplify this problem because they have similar sizes, local geometries, and electrostatic properties but function in different biological contexts. Here, we used the monoclonal antibody PSG2, which recognizes sTyr independently of the surrounding peptide sequence, to examine how a protein distinguishes these modifications. The crystal structure of PSG2 bound to an sTyr-containing peptide revealed a deep electropositive pocket with no modeled water molecules in direct contact with the sulfate group. Gas-phase density functional theory calculations favored pTyr over sTyr, showing that direct protein-ligand interactions alone are insufficient to explain PSG2 selectivity. Explicit first-shell hydration calculations showed that pTyr has a larger desolvation penalty than sTyr, and accounting for this difference reversed the calculated energetic order. Isothermal titration calorimetry showed favorable enthalpic and entropic contributions to sTyr binding, whereas no detectable heat signal was observed for pTyr. These results show that PSG2 distinguishes sTyr from pTyr through the balance between direct protein-ligand interactions and ligand desolvation.

Solution X-ray scattering provides unique structural information on biomolecules under biological conditions, resolving conformational heterogeneity and time-resolved structural changes. The scattering profiles contain limited information, and interpretation largely relies on fitting candidate structures guided by priors. Direct reconstruction of electron density maps is desirable, but so far has been prevented by the difficulty of incorporating such prior knowledge. Here we propose XSSDense, a framework that couples a variational autoencoder trained on electron densities from predicted or simulated protein ensembles with a genetic algorithm to refine densities against scattering data. We validate XSSDense on synthetic data for crambin, recover the conformational heterogeneity of the unfolded state of Avena sativa light-oxygen-voltage sensing domain 2, resolve a de-novo density for the pre-unfolding state of the same protein, and provide a new structural description of the signalling-state ensemble of photoactive yellow protein. XSSDense enables structurally grounded electron density reconstructions that intrinsically capture conformational heterogeneity.

Photosystem II (PSII) is in eukaryotic phototrophs is generally considered to operate within a more restricted spectral range than photosystem I (PSI), in which long-wavelength chlorophylls are a well-established feature of the peripheral antenna. Whether eukaryotic PSII can acquire comparable far-red-associated properties through lineage-specific antenna diversification has remained unclear. Here we present a 3.09 [A] cryo-electron microscopy structure of the C2S2M2L2 PSII supercomplex from Euglena gracilis, a euglenophyte species harbouring a secondary plastid and unusual light-harvesting system. We show that the euglenophyte-specific antenna protein LhcE9 occupies the position corresponding to canonical Lhcb5, but in a markedly different orientation that creates a distinct interface with the PSII core, particularly with CP43. Combined structural, spectroscopic, mutagenesis and proteomic analyses support LhcE9 as the stably bound PSII antenna subunit most closely associated with the far-red state in the supercomplex. Excitation-energy-transfer calculations further indicate two fast lineage-specific antenna-to-core routes mediated by LhcE9 and PsbX. Together, these findings reveal an unexpected mode of PSII antenna diversification and provide a structural framework for far-red-associated light harvesting in PSII.

Weak, reversible interactions underpin biomolecular recognition, and single-molecule fluorescence (smF) imaging techniques can provide unprecedented insight into those biological processes. Unfortunately, such studies often require micromolar concentrations of fluorophore-labeled biomolecules, which is beyond the accessible range of conventional smF microscopies. Here, we describe a surface-functionalization method based on cloud-point polyethylene glycol (PEG) grafting that enables widefield smF microscopy measurements at micromolar concentrations without the use of nanophotonic devices. Using conventional total internal reflection fluorescence (TIRF) microscopy, we detected single-molecule fluorescence resonance energy transfer (smFRET) from surface-tethered, donor-labeled target molecules with up to 8 micromolar concentrations of freely diffusing, acceptor-labeled analyte molecules in the background--two orders of magnitude higher than typical studies in the literature. Weak, DNA-hybridization and protein-RNA binding equilibria were measured across micromolar range titrations. Together with advances in high-background data analysis, the robust method presented here enables kinetic and thermodynamic analyses of weak biomolecular interactions, especially those limited by nonspecific adsorption and high fluorescence backgrounds, using only standard smF instrumentation.

We present the solution structures of HIV-1 proteins NC(p7)1-55 corresponding to the full-length NC(p7) and mature p6. The studies were carried in water and, to mimic the membrane, in micellar DPC (Dodecylphosphocholine) conditions. Our results unravel for the first time the structure adopted by the N-terminal amino acids of the free NC(p7)1-55, with the formation of a small helix spanning residues F6 to R10. Our NMR and Fluorescence Anisotropy data disclose an interaction between NC(p7)1-55 and p6 both in water and DPC, with respective Kd of 2.5mM and 370 mM at 23{degrees}C. The interaction is thus strengthened in lipidic conditions. Protein p6 stabilizes the N-terminus of NC(p7)1-55 while increasing at the same time the dynamic of the first zinc finger. Although the entire p6 sequence is involved in the interaction, we show that its C-terminal region is particularly sensitive to the presence of NC(p7)1-55, with a propensity of forming a a helix ranging from amino acids S111 to F116. This study brings experimental evidence of a direct protein-protein interaction between p6 and the N-terminal region of NC(p7)1-55. We further show that such interaction is readily accommodated within the NC(p15) framework and hypothesize that it may facilitate the selective assembly of assembly of the viral genomic RNA (gRNA) in the cell.

Time-resolved serial crystallography (TR-SX) has emerged as a powerful method for capturing ultrafast structural dynamics in proteins. TR-SX continues to produce remarkable studies, revealing previously unobserved transient states and providing deeper insights into processes such as drug targeting, DNA repair, and photosynthesis. However, extracting weak structural signals from noisy time-resolved datasets remains a major challenge. Robust computational methods are therefore required to isolate the signals associated with the underlying transient states. Importantly, this should be performed in reciprocal space to preserve compatibility with established downstream structure refinement workflows. Here, we introduce a framework for kinetic decomposition directly in reciprocal space that enables separation of kinetically distinct structural states. The method decomposes crystallographic data according to a predefined kinetic model, improving the recovery of weak transient signals and enhancing mechanistic interpretation from limited time-resolved datasets. We validate the framework using simulated data based on a previously published time-resolved crystallography study and demonstrate its application to a new TR-SX dataset comprising 17 time points. We show that the method separates the reciprocal space signatures of four intermediates by incorporating kinetic information from a predefined reaction model. This establishes a workflow for extracting kinetic states directly from time-resolved X-ray diffraction data that can be seamlessly integrated into existing crystallographic structure-determination pipelines.

Directional anisotropy in electron density provides key information about chemical bonding that is not readily accessible from conventional electron-density maps. Here, a model-independent framework is presented for decomposing experimental structure factors into angular components using spherical harmonics. Reciprocal-space projection onto spherical harmonics followed by standard Fourier synthesis yields angularly filtered density maps. The{ell} = 0 component captures the isotropic part of the density, while the{ell} = 1 components resemble px, py and pz-like dipolar functions that highlight directional electronic structure. Applications to high-resolution datasets, including urea, the Gly-Ala dipeptide and a 0.97 [A]{beta}-lactamase structure, reveal chemically interpretable dipolar features associated with carbonyl and amide bonds, N-H interactions and aromatic{pi} systems. Quantitative analysis using bond-centred sampling demonstrates stable dipolar signatures that remain detectable under moderate resolution truncation. These results establish spherical-harmonic angular decomposition as a practical framework for extracting directional electronic information from crystallographic electron-density maps. SynopsisAngular decomposition of experimental structure factors reveals dipolar anisotropy and directional electron-density features that are directly meaningful for chemical interpretation.

Nanodiscs and bicelles are widely used as membrane mimetics for structural studies of membrane-associated systems. Studies have reported that their magnetic alignment behavior and phase stability are highly sensitive to composition and temperature. In this study, we systematically investigate the effects of cholesterol on bicelle formation and magnetic alignment in DMPC + 0.2 glycyrrhizic acid (GA) systems using a combined 31P and 14N solid-state NMR experimental and simulation-based approach. Temperature dependent 31P NMR spectra reveal a clear transition from vesicle dominant to aligned bicelles/nanodsics phase, while 1N quadrupolar splitting and lineshape analysis provides quantitative insights into heterogeneous lipid bilayer populations, distinguishing large aligned nanodiscs (B(L)), small nanodiscs (B(S)), and isotropic/random components (B(R)). A strong correlation is observed between the 31P derived bicelle fraction and the 14N B(L) population, confirming that macroscopic alignment in the presence of an external magnetic field directly reflects the growth of large, well-ordered nanodiscs. Cholesterol is found to play a critical dual role by modulating membrane order and curvature. At low cholesterol concentration (0 to 5 mole percent), nanodiscs alignment occurs gradually with increasing temperature, while at higher cholesterol concentration (15 to 25 mole percent), the alignment is delayed and accompanied by broader spectral features, indicating structural heterogeneity. Notably, 10 mole percent cholesterol consistently provides the optimal balance, enabling efficient temperature dependent conversion to aligned bicelles while maintaining high B(L) populations (about 70-80 percentage) and minimal isotropic fractions. In contrast, higher cholesterol maintains significant B(S) and B(R) populations, even at elevated temperature. The 14N quadrupolar coupling (Cq is approximately 8.5 to 9.2 kHz for aligned nanodiscs) remains nearly invariant across compositions, showing that cholesterol does not change local headgroup dynamics but instead redistributes lipid populations. These findings establish a combined 31P and 14N solid -state NMR approach provides a valuable platform for quantitatively correlating membrane structure, dynamics, and alignment, offering practical guidelines for optimizing bicelle systems for high resolution solid-state NMR studies of membrane associated biomolecules.