The oxide surface structure plays a vital role in controlling and utilizing the emergent phenomena occurring at the interface of nanoarchitecture. A complete understanding of ternary oxide surfaces remains challenging due to complex surface reconstructions in various chemical and physical environments. Here a thermodynamic framework is developed to treat the stability of ternary oxide surfaces with finite temperature and chemical environments. Strontium titanate, as a representative ternary oxide, is used to establish the complete energy landscape of SrTiO3 (001) surface. The complete mapping yields a comprehensive understanding of various stable SrTiO3 surfaces with finite temperature and chemical potential or vapor pressure of the constituents, i.e., Sr (or Ti) metal and oxygen. This treatment also reveals a stable surface unknown yet with SrTi2O3 stoichiometry, which unveils the missing link between numerous previous experimental observations and the current understanding of SrTiO3 surface. Interestingly, the new surface shows an anisotropic surface-localized metallic state originating from the characteristic surface structure. The findings would provide a viable way to understand ternary oxide surfaces and further utilize SrTiO3 surfaces for oxide nanoarchitectures.

The Hofmeister series categorizes ions based on their effects on protein stability, yet the microscopic mechanism remains a mystery. In this series, NaCl is neutral, Na2SO4 and Na2HPO4 are kosmotropic, while GdmCl and NaSCN are chaotropic. This study employs CD and NMR to investigate the effects of NaCl, Na2SO4, and Na2HPO4 on the conformation, stability, binding, and backbone dynamics (ps-ns and µs-ms time scales) of the WW4 domain with a high stability and accessible side chains at concentrations ≤ 200 mM. The results indicated that none of the three salts altered the conformation of WW4 or showed significant binding to the four aliphatic hydrophobic side chains. NaCl had no effect on its thermal stability, while Na2SO4 and Na2HPO4 enhanced the stability by ~5 °C. Interestingly, NaCl only weakly interacted with the Arg27 amide proton, whereas Na2SO4 bound to Arg27 and Phe31 amide protons with Kd of 32.7 and 41.6 mM, respectively. Na2HPO4, however, bound in a non-saturable manner to Trp9, His24, and Asn36 amide protons. While the three salts had negligible effects on ps-ns backbone dynamics, NaCl and Na2SO4 displayed no effect while Na2HPO4 significantly increased the µs-ms backbone dynamics. These findings, combined with our recent results with GdmCl and NaSCN, suggest a microscopic mechanism for the Hofmeister series. Additionally, the data revealed a lack of simple correlation between thermodynamic stability and backbone dynamics, most likely due to enthalpy-entropy compensation. Our study rationalizes the selection of chloride and phosphate as the primary anions in extracellular and intracellular spaces, as well as polyphosphate as a primitive chaperone in certain single-cell organisms.

BACKGROUND: Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.
RESULTS: We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.
METHODS: Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.
BACKGROUND: Supplementary Information is available at Bioinformatics online.

Excessive fructose consumption is a primary contributor to the global surges in obesity, cancer, and metabolic syndrome. Fructolysis is not robustly regulated and is initiated by ketohexokinase (KHK). In this study, we determined the crystal structure of KHK-A, one of two human isozymes of KHK, in the apo-state at 1.85 Å resolution, and we investigated the roles of residues in the fructose-binding pocket by mutational analysis. Introducing alanine at D15, N42, or N45 inactivated KHK-A, whereas mutating R141 or K174 reduced activity and thermodynamic stability. Kinetic studies revealed that the R141A and K174A mutations reduced fructose affinity by 2- to 4-fold compared to WT KHK-A, without affecting ATP affinity. Molecular dynamics simulations provided mechanistic insights into the potential roles of the mutated residues in ligand coordination and the maintenance of an open state in one monomer and a closed state in the other. Protein-protein interactome analysis indicated distinct expression patterns and downregulation of partner proteins in different tumor tissues, warranting a reevaluation of KHK\'s role in cancer development and progression. The connections between different cancer genes and the KHK signaling pathway suggest that KHK is a potential target for preventing cancer metastasis. This study enhances our understanding of KHK-A\'s structure and function and offers valuable insights into potential targets for developing treatments for obesity, cancer, and metabolic syndrome.

Three approaches for determining the thermodynamic stability of irreversible processes are described in generalized formulations. The simplest is the Gibbs-Duhem theory, specialized to irreversible trajectories, which uses the concept of virtual displacement in the reverse direction. Its only drawback is that even a trajectory leading to an explosion is identified as a thermodynamically stable motion. In the second approach, we use a thermodynamic Lyapunov function and its time rate from the Lyapunov thermodynamic stability theory (LTS, previously known as CTTSIP). In doing so, we demonstrate that the second differential of entropy, a frequently used Lyapunov function, is useful only for investigating the stability of equilibrium states. Nonequilibrium steady states do not qualify. Without using explicit perturbation coordinates, we further identify asymptotic thermodynamic stability and thermodynamic stability under constantly acting disturbances of unperturbed trajectories as well as of nonequilibrium steady states. The third approach is also based on the Lyapunov function from LTS, but here we additionally use the rates of perturbation coordinates, based on the Gibbs relations and without using their explicit expressions, to identify not only asymptotic thermodynamic stability but also thermodynamic stability under constantly acting disturbances. Only those trajectories leading to an infinite rate of entropy production (unstable states) are excluded from this conclusion. Finally, we use these findings to formulate the Fourth Law of thermodynamics based on the thermodynamic stability. It is a comprehensive statement covering all nonequilibrium trajectories, close to as well as far from equilibrium. Unlike previous suggested \"fourth laws\", this one meets the same level of generality that is associated with the original zeroth to third laws. The above is illustrated using the Schlögl reaction with its multiple steady states in certain regions of operation.

Therapeutic deep eutectic solvents (THEDES) have been attracting increasing attention in the pharmaceutical literature as a promising enabling technology capable of improving physicochemical and biopharmaceutical properties for difficult-to-deliver drug compounds. The current literature has explored amide local anaesthetics and carboxylic acid nonsteroidal anti-inflammatories (NSAIDs) as commonly used THEDES formers for their active hydrogen-bonding functionality. However, little is known about what happens within the \"deep eutectic\" region where a range of binary compositions present simply as a liquid with no melting events detectable across experimentally achievable conditions. There is also very limited understanding of how parent compounds\' physicochemical properties could impact upon the formation, interaction mechanism, and stability of the formed liquid systems, despite the significance of these information in dose adjustment, industrial handling, and scaling-up of these liquids. In the current work, we probed the \"deep eutectic\" phenomenon by investigating the formation and physicochemical behaviours of some chosen lidocaine-NSAID systems across a wide range of composition ratios. Our data revealed that successfully formed THEDES exhibited composition dependent Tg variations with strong positive deviations from predicted Tg values using the Gordon-Taylor theory, suggesting substantial interactions within the formed supramolecular structure. Interestingly, it was found that the parent compound\'s glass forming ability had a noticeable impact upon such profound interaction and hence could dictate the success of THEDES formation. It has also been confirmed that all successful systems were formed based on charge-assisted hydrogen bonding within their THEDES network, affirming the significant role of partial protonisation on achieving a profound melting point depression. More importantly, the work found that within the \"deep eutectic\" region there was still an ideal, or thermodynamically preferrable \"THEDES point\", which would exhibit excellent stability upon exposure to stress storage conditions. The discoveries of this study bring the literature one step closer to fully understanding the \"therapeutic deep eutectic\" phenomenon. Through correlation between parent reagents\' physicochemical properties and the synthesised products\' characteristics, we establish a more educated process for the prediction and engineering of THEDES.

Membrane protein folding is distinct from folding of soluble proteins. Conformational acquisition in major membrane protein subclasses can be delineated into insertion and folding processes. An exception to the \"two stage\" folding, later developed to \"three stage\" folding, is observed within the last two helices in bacteriorhodopsin (BR), a system that serves as a model membrane protein. We employ a reductionist approach to understand interplay of molecular factors underlying the apparent defiance. Leveraging available solution NMR structures, we construct, sample in silico, and analyze partially (PIn) and fully inserted (FIn) BR membrane states. The membrane lateral C-terminal helix (CH) in PIn is markedly prone to transient structural distortions over microsecond timescales; a disorder prone region (DPR) is thereby identified. While clear transmembrane propensities are not acquired, the distortions induce alterations in local membrane curvature and area per lipid. Importantly, energetic decompositions reveal that overall, the N-terminal helix (NH) is thermodynamically more stable in the PIn. Higher overall stability of the FIn arises from favorable interactions between the NH and the CH. Our results establish lack of spontaneous transition of the PIn to the FIn, and attributes their partitioning to barriers that exceed those accessible with thermal fluctuations. This work paves the way for further detailed studies aimed at determining the thermo-kinetic roles of the initial five helices, or complementary external factors, in complete helical folding and insertion in BR. We comment that complementing such efforts with the growing field of machine learning assisted energy landscape searches may offer unprecedented insights.

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

Whey protein isolates (WPI) are known to have mineral-binding capacity to promote iron absorption. The aim of this study was to investigate the effect of iron ratio on the conformational structure of iron-bound whey protein isolate (WPI-Fe) and its thermodynamic stability. It was shown that the iron to protein ratio affects both the iron binding capacity of WPI and the iron valence state on the surface of WPI-Fe complexes. As the iron content increases, aggregation between protein molecules occurs. In addition, WPI-Fe nanoparticles have thermodynamic stability and Fe2+ has a high affinity with WPI for spontaneous exothermic reactions. This study demonstrates that WPI-Fe complexes can be used to efficiently deliver high-quality iron source (Fe2+) for future iron supplements.

Due to the limitation of the high-value-added products obtained from electrocatalytic CO2 reduction within an acid environment, introducing additional elements can expand the diversity of the products obtained during the CO2 reduction reaction (CO2RR) and nitrogen reduction reaction (NRR). Thus, coelectroreduction of CO2 and N2 is a new strategy for producing acetamide (CH3CONH2) via both C-C and C-N bond coupling using Cu-based nitrogen-carbon nanosheets. CO2 can reduce to CO, and a key ketene (*C═C═O) can be generated from *CO*CO dimerization; this ketene is postulated as an intermediate in the formation of acetamide. However, most studies focus on promoting the C-C bond formation. Here, we propose that C-N bond coupling can form acetamide through the interaction of *C═C═O with NH3. The acetamide is formed via a nucleophilic attack between *NH3 and the *C═C═O intermediate. The C-N coupling mechanism was successfully applied to expand the variety of nitrogen-containing products obtained from CO2 and N2 coreduction. Thus, we successfully screened Cu2-based graphite and Cu-based C3N4 as catalysts that can produce C2+ compounds by integrating CO dimerization with acetamide synthesis. In addition, we observed that Cu2-based C2N and Cu-based C3N4 catalysts are suitable for the NRR. Cu-based C3N4 showed high CO2RR and NRR activities with small negative limiting potential (UL) values of -0.83 and -0.58 V compared to those of other candidates, respectively. The formation of *COHCOH from *COHCO was considered the rate-determining step (RDS) during acetamide electrosynthesis. The limiting potential value of Cu2-based C2N was only -0.46 V for NH3 synthesis, and the formation of *NNH was via the RDS via an alternating path. The adsorption energy difference analysis both CO2 and N2 compare with the hydrogen evolution reaction (HER), suggesting that Cu2-based C2N exhibited the highest CO2RR and NRR selectivity among the 13 analyzed catalysts. The results of this study provide innovative insights into the design principle of Cu-based nitrogen-carbon electrocatalysts for generating highly efficient C-N coupling products.