The Journal of Physical Chemistry B

Molecular dynamics simulations of nucleic acids are performed using a solvent-buffer distance of 10 [A] between the solute surface and the simulation box boundary. Although this cell size has been extensively explored in protein simulations, its implications for nucleic acid dynamics are not well understood. Nucleic acids are elongated, highly charged, and flexible structures with hydration and dynamical properties distinct from those of proteins and therefore, they may require different solvent-layer considerations in simulations. In this study, we investigated the effect of simulation cell size on nucleic acid dynamics by simulating a 30-base-pair double-helical nucleic acid structure and its two single-stranded forms using solvent-buffer distances of 3, 5, 10, 15, and 20 [A]. Smaller cells may impose restricted hydration, molecular crowding, and periodic image interactions. However, larger cells provide solvent space for conformational relaxation. A total of 45 s of molecular dynamics simulations were performed (3 structures x 5 cell sizes x 3 replicates x 1 s). Our results show that while the commonly used 10 [A] buffer may be sufficient to maintain the stability of the double-stranded nucleic acid, larger cells are required to capture the conformational dynamics of single-stranded structures. In both, increasing the cell size to 15 or 20 [A] enables broader conformational sampling. The first hydration shell exhibits reduced crowding in the 20 [A] cell, consistent with more relaxed conformations. At larger cell sizes, single-stranded nucleic acids adopt compact, self-associated conformations for stability. Together, this study presents physical insight into how simulation cell size and solvent environment influence nucleic acid dynamics.

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

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.

In a recent study of a mice model it was suggested that after myocardial infarction Serum Amyloid A (SAA) aggregates are formed that contribute to the long-term complications of the infarct, and that a similar mechanism may exist for humans. Motivated by this hypothesis we have designed four peptide candidates that may interfere with formation of SAA3 fibrils, and using all-atom molecular dynamics have evaluated their ability to destabilize SAA fibrils. As the lifetime of peptide drugs can be increased by replacing L-amino acids with their mirror D-amino acids, we have built the peptides from D-amino acids. We identify two of these peptides, DRI-R5S and DRI-H6A, as promising drug candidates.

Live cell plasma membranes show spatially heterogeneous liquid-ordered (Lo)-like and liquid-disordered (Ld)-like regions similar to the co-existing Lo/Ld phases observed in lipid vesicles. The Lo-like regions are relatively less hydrated and less polar due to tight packing of the membrane components compared to the Ld-like regions. The steady-state fluorescence spectra of Di-4-ANEPPDHQ (Di-4), a widely used polarity-sensitive probe, is blue or red shifted when solvated in less polar (Ld- like) or more polar (Lo-like) regions respectively. However, quantification of Di-4 fluorescence in blue and red channels for the evaluation of membrane phase state suffers from the lack of specific wavelength choice for these two channels and Di-4s relatively higher concentration in Ld phase (red channel) due to its partitioning preference. To address these issues, we employed fluorescence lifetime of Di-4, a concentration independent photophysical parameter, to understand membrane biophysical properties. The fluorescence lifetime of Di-4 in lipid vesicles exhibits Arrhenius-like temperature dependence. Centred around this energetic feature of Di-4 photophysics, we developed a novel analytical module, namely excited state relaxation activation energy (ESRAct), that serves as an intrinsic descriptor of the membrane nano-environment sensed by this probe. We show that the ESRAact value scales with increasing disorder in nanoscale phase separation (i.e., ESRAct of pure Ld > mixed Ld/Lo > pure Lo phase). We then extended its applications to giant plasma membrane vesicles (GPMVs) isolated from MCF-7 cells and found that these vesicles exhibit nanoscale Lo/Ld co-existing phase within 16-37{degrees}C. We envisage wide applications of ESRAct to delineate plasma membrane phase behavior as well as general photophysical studies on other newly designed polarity-sensitive probes.

Heterochromatin protein 1 (HP1 is an intrinsic component of heterochromatin domains where it is involved in a diverse set of functions including heterochromatin spreading and organization, chromatin compaction and transcriptional silencing. It has been suggested that HP1 functions through a phase separation mechanism, a process that has been observed in vitro in the presence of N-terminal phosphorylation, nucleic acids and nucleosome arrays. HP1 can also interact with numerous binding partners that contain a specific motif called an HP1 access code (HAC). HACs recognize and bind to an interface formed by the chromoshadow (CSD) domains in the HP1 homodimer, the functional form of the protein. It has been shown that some HP1 binding partners can enhance its phase separation ability while others disrupt the process. Here, we focus on the interactions between HP1 and three binding partners, namely the p150 subunit of the chromatin assembly factor 1 (CAF-1), the N-terminal domain of the lamin B receptor (LBR), and the mitotic protein Shugoshin 1 (Sgo1). Using phase separation assays, we show that CAF-1 prevents HP1 phase separation while LBR and Sgo1 enhance it. Binding assays, mutational studies, NMR spectroscopy and computational analysis allow us to dissect the contributions of the HAC motifs, the charge patterns of the binding partner sequences and the role of N-terminal phosphorylation on HP1 in condensate formation. Our results demonstrate that each binding partner uniquely balances these contributions to modulate the properties of HP1, while electrostatic interactions dominate the regulation of phosphorylated HP1. These results suggest that HP1 binding partners play an important role in the modulation of its properties and the regulation of its functions in distinct biological contexts.

Phosphorylation of the CLOCK/BMAL1 complex is a reversible post-translational modification that plays a central role in regulating circadian oscillations; however, its underlying mechanistic basis remains poorly understood. Although biochemical studies have shown that phosphorylation modulates CLOCK/BMAL1 binding to DNA, yet it remains unclear whether these effects are confined to local perturbation or also propagate through allosteric effects. Moreover, the influence of phosphorylation on histone dynamics and transcription factor-nucleosome interactions has not been systematically investigated. Here, we address these questions using atomistic trajectories obtained through backmapping of coarse-grained molecular dynamics simulations based on the recently resolved cryoEM structure of the CLOCK/BMAL1 and nucleosome complex. We investigated three experimentally identified phosphorylation states: CLOCK bHLHS38/42, BMAL1 bHLHS78, and simultaneous phosphorylation of both proteins. Our results demonstrate that phosphorylation regulates CLOCK and BMAL1 asymmetrically. Whereas phosphorylation weakens the interaction of the modified bHLH domain with the E-box, BMAL1 phosphorylation simultaneously enhances DNA engagement by the CLOCK bHLH domain, an effect that persists in the doubly phosphorylated complex and identifies BMAL1 phosphorylation as the dominant regulatory event. Steered pulling simulations further demonstrate that phosphorylation equalizes the mechanical stability of CLOCK and BMAL1 interactions with DNA. Beyond modulating DNA binding, phosphorylation remodels protein histone interactions by altering contacts between the CLOCK PASB domain and histone H3 and between the BMAL1 PASA domain and the H2A/H2B acidic patch, while simultaneously rewiring residue-correlation and allosteric communication networks throughout the heterodimer. Importantly, phosphorylation increases the separation between the CLOCK HI loop and the H31 L1 elbow, supporting a structural model in which phosphorylation acts as a priming event that provides a more permissive environment for CRY1 recruitment to the chromatin-bound CLOCK/BMAL1 complex, thereby facilitating transcriptional repression. Collectively, our findings reveal that phosphorylation regulates the CLOCK/BMAL1 complex through coordinated remodeling of DNA binding, nucleosome interactions, and long-range allosteric communication, providing a mechanistic framework for circadian transcriptional repression and a foundation for the rational design of therapeutics targeting the molecular circadian clock.

Electrostatic interactions often play a role in determining the thermodynamic and kinetic properties of protein-protein interactions. However, the role of long-range electrostatic interactions in intrinsically disordered protein (IDP) binding is less clear, as they often bind in multiple steps including initial formation of a disordered encounter complex, followed by rearrangement into the bound state. We varied the salt concentration to probe the role of long-range electrostatic interactions in the binding of the highly charged AbpSH3 domain and the oppositely charged IDP ArkA. Using isothermal titration calorimetry, we observe that salt enthalpically destabilizes the bound complex. Molecular dynamics and NMR experiments reveal that salt has little effect on the bound state structure. However, simulations show that salt destabilizes the encounter complex intermediate, which primarily affects the association rate as measured by NMR. Consistent with these results, salt has the largest stabilizing effect on the apo SH3 domain, as cations substitute for the transient and long-range electrostatic interactions that can form with ArkA in the complex. We reveal a detailed picture of how a highly charged domain uses long-range, fuzzy, electrostatic interactions to help reach the bound state, a mechanism that is likely common among other highly charged domains that bind IDPs. TOC Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/741257v1_ufig1.gif" ALT="Figure 1000"> View larger version (21K): org.highwire.dtl.DTLVardef@dfc7acorg.highwire.dtl.DTLVardef@1ae0438org.highwire.dtl.DTLVardef@19704adorg.highwire.dtl.DTLVardef@1b40b15_HPS_FORMAT_FIGEXP M_FIG C_FIG

We present a comparative study of the inner membrane of three Gram-positive bacterial strains, namely S. aureus, S. epidermidis and N. lacusekhoensis. A lipidomics study is used to obtain the lipid architecture and composition for S. epidermidis found in the skin microbiome and N. lacusekhoensis, an extremophile present in halophilic and alkophilic environments. Differences between the strains arise from both the lipid architecture and the cardiolipin content varying from 5% in S. aureus to 85% in N. lacusekhoensis. We develop coarse grained (CG) Martini-3 membrane models which reproduce structural properties such as membrane area, thickness, density distributions as well as ion-correlations with all-atom models. Inter-lipid correlations reveal a homogeneous distribution of lipids in the membranes despite the wide variation in lipid types and composition. Mechanical properties such as the area stretch modulus increased with cardiolipin content, however the bending modulus has a more complex dependence on membrane charge and lipid type. Using the CG models we evaluate the insertion free energies for four widely used antimicrobial molecules. Entry barriers for thymol and methylparaben arise from the charge density modulation at the membrane headgroups due to counterion condensation. The entry mechanisms of the antimicrobial peptide cecropin-melittin-15 (CM15) and the preservative molecule ethyl-lauroyl-arginate (ELAR) are found to be similar across all three strains. We also illustrate the manner in which the extremophilic strain, N. lacusekhoensis with its high cardiolipin content, modulates the partitioning kinetics of the antimicrobial molecule thymol with pH and salt. Our study reveals that membrane properties are largely conserved across the three model membranes. The molecular models and insights emerging from the present work should aid in the development of novel antimicrobials against Gram-positive strains.

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.

Amylose contributes to starch crystallinity, but the stability of packed amylose double helices in water at elevated temperature remains insufficiently characterized. Here, we used molecular dynamics simulations to test whether chain length affects the short-timescale stability of A-type amylose oligomers in water. Six systems differing in chain length (6, 12, or 24 glucose units per chain) and oligomer size (isolated double strand or dodecamer of six double strands) were simulated, and five independent 1-s production runs were analyzed for each simulated condition. Oligomers with six glucose units showed structural collapse accompanied by increased water penetration. By contrast, dodecamers with 12 or 24 glucose units largely retained packed double-helical organization over the simulated timescale, although fraying was observed at their ends. These results indicate that chain length and lateral packing strongly affect the early structural response of amylose-like crystalline segments in hot water. The present simulations do not establish the ultimate fate of longer oligomers at longer timescales, but they identify a relative stability difference that is relevant to molecular interpretations of hydration-driven disordering in starch.

Plexin-B1 is a transmembrane receptor that integrates signals from Rho-family and Ras-family (Rap1b) GTPases to regulate cellular processes. While ligand simulated activation of the receptor is largely understood, the role of membrane composition and GTPase allosteric effects on plexin structure, internal protein dynamics, and function is still to be elucidated. Here, we performed multi-replica, 1 s all-atom simulations of Plexin-B1-GTPase complexes on PIP2- and PIP3-containing membranes to investigate the effects of these two signaling lipids, as well as on the GTPases. We found that both Rap1b and Rnd1 stably associate with the membrane, with PIP2 promoting broader lipid engagement and stronger Rap1b-Plexin-B1 interactions, whereas PIP3 enhances Rnd1-Plexin contacts and induces a membrane proximal orientation of Plexins juxtamembrane helix and makes contacts with a previously discovered activation switch loop. Contact map and network analyses revealed lipid-dependent shifts in allosteric communication, with PIP2 favoring Rap1b-centric hotspots and PIP3 favoring Rnd1-centric pathways. These predictions allow us to suggest a model for plexin intracellular region activation where both the identity of phosphoinositides and GTPase context synergistically stabilize Plexin-B1 membrane engagement, alter structural dynamics, and allosteric networks. Thus, we propose that the membrane is an active modulator of plexin receptor signaling.

Small molecules regulate biomolecular condensates in a biphasic manner, promoting liquid-liquid phase separation (LLPS) at low concentrations while suppressing it at higher concentrations. Despite increasing experimental evidence for such re-entrant behavior, a unified physical description remains lacking. Here, we identify a minimal thermodynamic mechanism for re-entrant LLPS by coupling Cahn-Hilliard dynamics to a concentration-dependent Flory interaction parameter containing competing LLPS-promoting and inhibitory contributions. The resulting model reproduces experimentally observed nonmonotonic condensate formation in Tau-tannic acid and TDP-43-bis-ANS systems, including the concentration-dependent emergence and dissolution of protein-rich domains. Spinodal analysis reveals finite concentration windows for phase instability and demonstrates that re-entrant mixing is encoded directly in the free-energy landscape. The framework further captures morphology transitions and diffusive coarsening within the phase-separated regime. These results establish a general mesoscale description of chemically regulated condensates and provide design principles for controlling phase separation through small-molecule modulators.

This present work shows that E-hook fragments possess functional structure differences governed by electrostatic interactions and sequence composition. The acidic C-terminal tails of tubulin, known as E-hooks, play a central role in regulating interactions between microtubules and motor proteins, microtubule-associated proteins, and enzymatic modifiers. Despite their functional importance, the intrinsic structural properties of these peptide segments remain poorly characterized due to their intrinsically disordered nature. In this work, we present quantum-mechanically optimized structures of hexamer peptides derived from {beta}-tubulin E-hook sequences. Density functional theory calculations were used to optimize peptide geometries using progressively larger basis sets. From the optimized geometries we calculated theoretical Raman spectra, Ramachandran backbone dihedral distributions, and measured radii of gyration to resolve composition dependent structural tendencies. The combined Raman and conformational analyses provide a systematic computational approach for comparing simulated and experimental Raman spectra of tubulin E-hooks and other intrinsically disordered proteins and offer insight into how E-hooks contribute to the recognition mechanisms underlying the tubulin code.

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

The binding affinity of a ligand to a protein is influenced by the protonation and tautomeric states of both partners. However, this relationship remains under-investigated due to the limited availability of computational tools capable of considering all charge and tautomer states in a scalable manner to study clinically relevant systems. Here, we use Multi-Conformation Continuum Electrostatics (MCCE) to calculate the protonation and tautomer distributions of nine kinase domains bound to 18 FDA-approved inhibitors while considering their Boltzmann-ensemble. Our simulations show that protein net charge and proton distribution remain largely stable even upon binding charged inhibitors. Our results find that individual inhibitor charges are dynamic, frequently increasing, or decreasing upon binding a specific protein target. Kinase-inhibitor binding significantly shifts the relative probabilities of low-energy states ({Delta}G < 2.5 kcal/mol), though it does not recruit higher-energy conformers into the bound population. Our consideration of all possible charge states and tautomers enable us to identify when tautomer have significant significant free binding energy differentials (3-6kcal/mol). In turn, we find that minority species can become the dominant component in the bound state, emphasizing the necessity of considering ensemble-wide protonation and tautomer states to accurately predict protein-ligand binding energetics.

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

Protein behavior inside cells is dominated by the crowded nature of the intracellular environment. Progress in structure determination of proteins and protein complexes, based on advances in Artificial Intelligence, provides an opportunity for structure-based modeling of cellular phenomena. Such modeling at the atomic resolution has been advanced by the traditional simulation techniques, e.g. molecular dynamics. A recently developed docking-based approach implements Markov Chain Monte Carlo sampling of intermolecular energy landscapes, offering several orders of magnitude faster simulation protocols. The approach allows addressing much longer trajectories of macromolecular systems in the crowded intracellular environment at atomic resolution. The sampling by design avoids low-probability (high-energy) states, which greatly accelerates the simulation process. A notable feature of this docking-based approach is the rigid body approximation of protein structures. The rigid-body approximation had been the primary direction in the protein docking field up until recent developments in deep learning. The rigid-body approach should be quite robust for the higher energy transient interactions that dominate the highly crowded cellular environment, as they likely involve relatively small conformational change. However, it is less applicable to the low-energy protein-protein complexes, especially those involving flexible regions. We addressed this problem by incorporating AlphaFold3 top models of the protein complexes in the mapping of the intermolecular energy landscape, as representative of the low-energy configurations of the protein assembly. By the nature of the AlphaFold predictions, these models involve appropriate conformational change between unbound and bound structures. These low-energy docking poses are combined with the rigid-body docking predictions that cover the multiplicity of the transient interactions. Such combination directly addresses the conformational flexibility of proteins upon binding along with the multiplicity of the transient protein encounters in the crowded cellular environment. SIGNIFICANCEProtein behavior inside cells is dominated by the crowded nature of intracellular environment. A recently developed approach allowed addressing long simulation trajectories of macromolecular systems in such environment at atomic resolution. A notable feature of this approach is the rigid body approximation in representation of the protein structures, which had been popular in the field up until the recent developments in artificial intelligence. However, such approximation is less applicable to stable protein-protein complexes, especially those involving flexible regions. We addressed this problem head-on by incorporating top deep learning-generated models of protein complexes. The new approach directly accounts for the flexibility of protein structures upon binding, along with the multiplicity of the transient protein encounters in the crowded cellular environment.

Integral membrane proteins remodel the surrounding lipid bilayer, but quantifying the resulting deformations and linking them to protein density in the membrane has remained challenging. Here, we introduce an integrative methodology that combines all-atom molecular dynamics (MD) simulations with multiscale small-angle X-ray scattering (SAXS) analysis to connect membrane strain to the protein/lipid ratio in proteoliposomes. Using outer membrane phospholipase A (OmpLA) reconstituted into lipid bilayers with both increased and decreased hydrophobic thickness, we systematically probe the effects of positive and negative hydrophobic mismatch.MD simulations demonstrate that OmpLA causes anisotropic, oscillatory thickness deformations extending up to eight times the radius of the first lipid shell surrounding the protein, yet the net change in average membrane thickness remains below 1%. Through our multiscale SAXS analysis, we quantitatively extract structural parameters, ranging from proteoliposome size to internal membrane architecture, using constrained Bayesian inference, with priors derived from MD findings. Specifically, we determine the protein/lipid molar ratio and average membrane strain, revealing excellent agreement between experiment and simulation. In thinner bilayers, substantial protein loss limits the analysis, highlighting the role of bilayer stability in sample preparation. Moreover, the predominance of OmpLA monomers in the thicker membranes is consistent with weak, membrane-mediated repulsive interactions between protein inclusions. Collectively, this integrative approach establishes a framework for quantifying protein-lipid interactions across molecular and mesoscale dimensions.

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