The Journal of Physical Chemistry Letters

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.

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.

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.

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.

The development of NIR fluorescent dyes with favorable optical properties is of great importance especially for applications within the field of optical imaging. In this study, we report the design, synthesis and photophysical characterization of novel BODIPY-Xanthene hybrid dyes obtained via Knoevenagel condensation between an asymmetrical BODIPY core and the functionalized xanthene aldehydes intermediates. The synthesized dyes ex-hibit absorption (667 nm) and emission bands (720-722 nm) within the NIR region. Detailed UV-Vis and fluo-rescence studies were performed and molar extinction coefficients and stokes shifts also calculated. Moreover, findings highlighting the influence of structural modifications on the optical behavior of the hybrid dyes and their potential utility in optical imaging applications are reported.

Integrative structural biology seeks molecular descriptions consistent with complementary experimental measurements. A particular challenge arises for molecules whose broad conformational ensembles resist description by a few representative structures, such as natively unfolded proteins and fluid-phase biomembrane lipids. Although molecular simulations can generate such ensembles, their simultaneous agreement with a large number of experimental constraints is commonly prevented by model inaccuracies. Here we introduce autofocusing as an approach that integrates experimental information by automated finetuning of the physical model generating the ensemble. We demonstrate it for a fluid bilayer of the phospholipid POPC, for which no existing force field has simultaneously reproduced the complete set of 82 NMR C-H bond order parameters. Autofocusing brings all the 82 within experimental uncertainty while preserving agreement with independent small-angle X-ray scattering measurements and retaining or improving agreement with untargeted NMR relaxation rates and effective correlation times, thus supporting its description of membrane structure and dynamics beyond the calibration targets. The resulting simulations challenge a long-standing two-state interpretation of phosphatidylcholine headgroup structure, instead supporting a broad conformational ensemble poorly represented by crystal-derived conformers. These results establish autofocusing as a practical approach to constructing experimentally grounded conformational ensembles while retaining the computational tractability of classical molecular dynamics.

In retinal proteins, light absorption leads to a double bond isomerization of the chromophore, followed by conformational relaxations that drive functional protein rearrangements. The balance between photoisomerization and radiative or non-radiative decays of the excited state is adjusted to the demands of the respective photoreceptor function. The molecular determinants controlling the excited state processes remain poorly understood and are the focus of this combined crystallographic, spectroscopic, and theoretical work on the recently discovered neorhodopsin (NeoR). NeoR exhibits a highly red-shifted and fluorescent dark state and selectively photoisomerizes from all-trans to 7-cis. We show that the local electrostatic environment reduces the negative charge density near the Schiff base and suppresses torsional motion around the canonical C13=C14 isomerization site of microbial rhodopsins. Instead, the chromophore-binding pocket permits only low-efficiency C7=C8 isomerization, accompanied by polyene inversion, {beta}-ionone ring displacement, and Schiff base deprotonation. Consequently, unlike other retinal photoreceptors, NeoR undergoes structural changes localized near the {beta}-ionone ring.

Hetero-oligomeric protein complexes play crucial roles across biology, providing combinatorial diversity and context-specific functionality in membrane receptors, molecular chaperones and beyond. Yet, existing methods are limited in their ability to resolve and quantify the assembly energetics of complex biomolecular populations. Here, we introduce a comprehensive method combining iterative 3D classification, error-rate quantification via simulated particles, and global thermodynamic modeling to extract interaction energies from cryo-EM data. Using hexameric cruciferin (the primary rapeseed storage protein) as a model, we reconstruct a 1.83 [A] structure and distinguish three highly similar 50 kDa isoforms. We resolve the complex compositional heterogeneity of the hexameric species at the single-particle level and fit a global binding model revealing pairwise interaction energies between isoforms differing by fractions of the thermal energy (kB T ). This method offers a powerful, broadly applicable tool to characterize the compositional heterogeneity and energetics of biomolecular complexes.

Fluorescent proteins (FPs) are the workhorse of modern-day cellular imaging and biosensing. However, rationally predicting and engineering the photophysical properties of FPs remain challenging, as these are governed by complex three-dimensional microenvironments and post-translational modifications. Here, we present a novel surface-mutation-guided engineering strategy combined with iterative rounds of rational mutagenesis to improve key photophysical properties of red fluorescent proteins (RFPs). This approach generated two bright, monomeric RFP variants from the mFruits lineage, mCranberry and mCranberry2, with quantum yields of 85% and 80%, respectively. Strategic incorporation of charged amino acid residues on the protein surface enhanced fluorescence quantum yield without compromising photostability. Comprehensive photophysical characterization and live-cell microscopy demonstrate enhanced performance of these variants and establish their utility as robust probes for fluorescence imaging. However, the exact mechanism of such improvements is subject to further investigation. Collectively, this work introduces a strategy for optimizing fluorescence properties and provides a framework that may be broadly applicable to other pigment-protein systems such as genetically encoded metal-ion indicators and photosynthetic light-harvesting complexes.

Deep learning co-folding and docking models accurately predict ligand binding poses at orthosteric sites yet systematically underperform at allosteric pockets. Here, we demonstrate that this accuracy gap reflects a fundamental biophysical property of local energy landscape topology specifically, the magnitude of frustration quenching upon ligand binding rather than an intrinsic algorithmic defect. By evaluating leading co-folding and docking paradigms across curated benchmarks and large-scale uncurated datasets, we show that AI prediction success strongly tracks frustration quenching which the degree to which ligand binding relieves local energetic strain to form a steep energy funnel. Topologically, frustration-weighted residue interaction networks reveal that orthosteric pockets act as high-betweenness communication hubs embedded within contiguous, minimally frustrated pathways. In contrast, allosteric sites reside in low-betweenness peripheral regions with fragmented frustration profiles, presenting flatter, entropy-dominated landscapes that challenge structure prediction algorithms. Comparing generative diffusion with single-shot regression demonstrates that multi-pose sampling capacity is required to cross energy barriers and discover the frustration funnel, whereas joint receptor-ligand coordinate co-adaptation governs local optimization within the funnel. Collectively, this study reframes the allosteric "blind spot" as a quantitative property of energy landscape architecture rather than a defect of any specific predictive framework. We demonstrate that frustration quenching magnitude and structural network centrality serve as physical descriptors that define the intrinsic limits of AI performance, reframing AI accuracy from a benchmark metric into a physics-grounded quantitative reporter of macromolecular binding landscape architecture.

Understanding how conformational dynamics regulate protein complex assembly remains a central challenge in molecular systems biology. Here, we develop a multiscale modeling framework that integrates all-atom molecular dynamics (MD) simulations with rule-based kinetic modeling to investigate the formation of the PP1-GADD34-eIF2 complex, a critical regulator of the integrated stress response. Using umbrella sampling, we compute the potential of mean force (PMF) for key interactions, revealing strong thermodynamic driving forces for eIF2 binding ({Delta}G = -89.5 kJ/mol, KD {approx} 0.07 fM) and moderate affinity for PP1-GADD34 association ({Delta}G = -40.7 kJ/mol, KD {approx} 0.138 M). These energetics inform a PySB-based model that incorporates GADD34's folding state as a continuous variable ({varphi}), linking conformational transitions to holoenzyme assembly and activity. Simulations show that catalytic efficiency is maximized when GADD34 folding free energy is 20 kJ/mol and {varphi} = 0.9, reflecting a highly ordered state stabilized by actin and PP1 binding. Comparative analysis of energy-constrained and optimized parameter regimes reveals that folding-dependent assembly enhances both eIF2 dephosphorylation and information transmission, with channel capacities increasing from <0.002 bits to >5.5 bits. These results demonstrate how conformational regulation encodes system-level function and underscore the utility of multiscale models in bridging molecular energetics with dynamic biochemical control.

Finite-bandgap charge-based FET biosensors are known to be insensitive to electronic tunneling noise in the semiconductor and interference from weakly-charged proteins in physiological samples. However, their signal is still corrupted by pH fluctuations and condensing counterions in the bulk electrolyte. Here, we demonstrate that electrolyte noise is screened by the Stern layer beyond the linear Debye-Huckel limit, when the interfacial potential drop is larger than the thermal voltage. Using a fully nonlinear Gouy-Chapman-Stern (GCS) theory, a universal calibration curve is derived and shown to collapse the low-noise FET signals for miRNA, extracellular vesicles (EV), and protein, captured either directly or through silica and magnetic nanoparticle charge reporters, over eleven decades of analyte concentration. The theory involves the dissociation constant KD and a gain parameter {chi}, which represents the competing effects of the reporter charge and buffer Debye screening that must be large to produce the low-noise GCS condition. The limit of detection (LOD) is shown to be KD/{chi}, which can be optimized by varying the probe density, charge reporter, and buffer solution. An optimized hybridization buffer then yields {chi} ~ 22 (LOD ~1 pM) for miRNA detection, and multivalent capture reduces KD by four orders of magnitude for EV detection (relative to protein detection) with {chi} ~ 34 (LOD ~10 fM).

Amyloid {beta}-protein (A{beta}), which is known for its role in Alzheimer's disease (AD), is also involved in several physiological functions. Of the two predominant isoforms, A{beta}40 and A{beta}42, the latter is more aggregation-prone and more active in health and disease. In the brain, A{beta} interacts with free lipids, which may affect its assembly pathways. Here, thioflavin T (ThT) fluorescence spectroscopy is combined with molecular dynamics (MD) to investigate A{beta}40 and A{beta}42 co-assembly with free dimyristoylphosphatidylcholine (DMPC) and DMPC/cholesterol (CHOL) lipids. ThT measurements at several protein-to-lipid (PL) ratios show isoform- and lipid composition-dependent kinetics of A{beta} fibril formation with clear isoform-dependent lipid effects at a PL ratio of 1:5. Early co-assembly stages of six A{beta}40/A{beta}42 with DMPC and DMPC/CHOL lipids at this PL ratio are then examined by MD. MD results indicate that lipids alter the secondary structure more and interact more avidly with A{beta}40 than with A{beta}42. In turn, A{beta}40 increases the planar order of lipid bicelles more than A{beta}42. Unlike A{beta}40, A{beta}42 decreases the nematic order of CHOL. In DMPC/CHOL lipids, A{beta}42 but not A{beta}40 exhibits two A{beta}-lipid interaction modes associated with mutually exclusive A{beta}42-lipid and A{beta}42-A{beta}42 interactions. Our MD findings provide a plausible explanation for an experimentally observed increase in the lag time for A{beta}40 fibril formation in DMPC lipids and even more so in DMPC/CHOL lipids that contrasts with much smaller effects of lipids on A{beta}42 fibril formation and offer additional atomistic-level insights into A{beta} co-assembly with lipids amenable to experimental testing.

Aromatic side chains carry the optical and redox chemistry of peptides, and lengthening one is widely expected to extend its conjugation and narrow its HOMO-LUMO gap. We test that expectation with plane-wave density functional theory on 22 isolated neutral peptides NH2-(X)n-COOH (X = Phe, Tyr, Trp, Cys; n = 1 to 5, and n = 10 for Cys and Trp). The gap does narrow, at the same rate for all three aromatic residues, i.e., -0.17 to -0.18 eV per residue, even though benzene, phenol and indole are very different chromophores. Residue-resolved projections reveal why: nothing delocalises. Every aromatic frontier orbital is 97 to 100 % confined to a single residue and the HOMO participation number saturates at one ring. Each residue instead keeps its own local gap: for Trp it is invariant to within 0.03 eV over n = 2 to 10, while over that same span the global gap falls by 1.19 eV. The global gap is therefore an extreme-value functional of the residue site energies, gap(n) =<g_f>-{Delta}(n), whose alignment penalty accounts for the entire effect: the polar backbone generates a monotonic internal electrostatic ramp that leaves the two gap edges at opposite ends of the chain, 32 [A] apart. We set out the resulting design rules for peptide-based bio-organic semiconductors.

Many RNAs occupy multiple inter-converting structures in order to perform key biological functions. Characterizing RNA conformational ensembles is therefore critical to illuminating the mechanisms by which RNAs fold, unfold, and undergo precise structural rearrangements in response to cellular signals. However, resolving the equilibrium populations and folding kinetics of RNA ensembles is nontrivial due to the intrinsic ruggedness of RNA conformational landscapes and wide range of timescales spanned by RNA structural dynamics. To address these challenges, we present a de novo hierarchical multiscale computational framework that integrates diverse secondary and tertiary structure generation, adaptive unbiased molecular dynamics, a generative AI model termed latent thermodynamic flows (LaTF), and Markov state modeling to efficiently map global RNA folding landscapes and associated dynamics. Applied to the GCAA tetraloop, HIV-TAR stem-loop, and microROSE RNA thermometer, this framework yields ~ 1.3 ms of cumulative sampling and resolves temperature-dependent conformational landscapes comprising numerous native-like, misfolded, partially folded, and unfolded metastable states. Across all three RNA hairpins, temperature substantially reshapes landscape ruggedness, metastable state populations, and folding mechanisms. Our predicted ensembles show overall agreement with key static and dynamic biophysical observables, including Nuclear Magnetic Resonance (NMR)-resolved structures, Nuclear Overhauser Effect (NOE)-derived distance restraints, and residual dipolar couplings (RDCs). Together, these results establish a general physics-informed AI approach for accurately modeling RNA conformational heterogeneity, equilibrium thermodynamics, and long-timescale kinetics.

Small multifunctional viral membrane proteins called viroporins are attractive drug targets but are difficult to structurally characterize in a lipid-bilayer environment. We have successfully reconstituted the full-length Envelope (E) protein from SARS-CoV-2 in a lipid-bilayer, co-assembled with a native host factor that binds to E, the PDZ domain 2 of ZO1. We have leveraged the enhancement afforded by dynamic nuclear polarization NMR to determine that the ensemble-averaged oligomer state of E is a pentamer. Complexing the protein with a host PDZ domain did not impact the oligomer state, but resulted in structuring of the last four residues of E, and immobilization of the PDZ domain near the lipid surface. Combined with secondary structure information derived from the chemical shifts, these data were used to model an E protein pentamer together with the PDZ protein dimer. Inclusion of a relevant host factor, as well as a complex lipid bilayer that mimics conditions in the ER-Golgi intermediate compartment, approaches native conditions, and addresses the ongoing controversy regarding the functional oligomer state in an infected cell.

Biomolecular condensates formed via phase separation play critical roles in regulating cellular processes, and their dysregulation is increasingly associated with neurodegeneration. A key challenge in understanding condensate pathology lies in distinguishing dynamic condensates from neurotoxic solid aggregates, the formation of which can, in some systems, occur within existing condensates. Yet current detection techniques often lack the sensitivity and resolution to directly monitor these assemblies and their transitions in dynamic and complex environments. Here, we combined fluorescent probes, based on molecular rotors, with fluorescence lifetime imaging microscopy to quantify changes in protein dynamics during phase separation-associated aggregate formation. Molecular rotors respond to local viscosity changes, which in our study allowed discrimination between fluid condensates and rigid amyloids based on rotors' fluorescence lifetime. Using -synuclein, a hallmark protein in Parkinson's disease, as a model system, we demonstrate real-time visualization of phase separation and condensate maturation within a unified experimental framework. Our method preserves protein integrity and provides quantitative, spatially resolved insights into structural transitions. This technology bridges a critical gap in the study of pathological protein aggregation and offers a powerful platform for mechanistic studies, biomarker discovery, and drug discovery in neurodegenerative disease research.