Molecule-protein Interactions Research Articles

Hydrogen/Deuterium Exchange Reaction Rate of Cytochrome c Determined by Droplet Collision Atmospheric Pressure Infrared Laser Ablation Mass Spectrometry.

The hydrogen/deuterium (H/D) exchange rate is an optimal measure for studying the structures and dynamics of hydrogen bonding systems, as it reflects the molecular contact environment and the strength of the hydrogen bonds. A method for rapid measurement of the H/D exchange reaction rates is required to examine the intermolecular environments of molecules in solutions. We developed a droplet collision atmospheric pressure infrared laser ablation mass spectrometry technique for this purpose. We obtained the H/D exchange reaction rate of cytochrome c in a methanol/H2O·D2O solution. We revealed that the first hydration shell of the cytochrome c molecule hinders the penetration of D2O to the surface of the molecule from the rates, which provides a novel method to investigate solution structures by a mass-spectrometric method. The droplet-collision mass spectrometry method developed in the present study can be extended to research on the molecular interactions in solutions, such as the mutual interactions of protein molecules, which are of importance in living cells.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles.

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL: https://proj.cse.cuhk.edu.hk/aihlab/pronet/.

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins.

Protein-molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein-molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein-molecule interaction prediction, setting a new precedent for future research in this field.

Revisiting the conformational transition model for the pH dependence of BSA structure using photoluminescence, circular dichroism, and ellipsometric Raman spectroscopy

Changes in pH affect metabolic pathways, primarily by modulating enzyme conformations, which is why a detailed analysis of pH-driven conformational transitions is required to understand the underlying biochemistry of diseases and biological organisms. In this work, we examined the pH-driven conformational dynamics of Bovine Serum Albumin (BSA), within the framework of the Foster Model. Circular Dichroism and Raman Optical Activity showed the conversion of helical into β-rich structures in the acid and basic regions, while an opening of BSA tertiary structure was shown by the upsurging of accessibility of ANS-BSA binding sites and the increasing of random contributions at regions F and B. We could then revisit the Foster Model by introducing two additional intermediate conformational states and structural reorganization at extreme pH values. This expanded model opens up new possibilities concerning protein-molecule interactions, promising far-reaching implications for fields such as drug design and biomaterials.

PH-Dependent HEWL-AuNPs Interactions: Optical Study.

Optical methods (spectroscopy, spectrofluorometry, dynamic light scattering, and refractometry) were used to study the change in the state of hen egg-white lysozyme (HEWL), protein molecules, and gold nanoparticles (AuNPs) in aqueous colloids with changes in pH, and the interaction of protein molecules with nanoparticles was also studied. It was shown that changing pH may be the easiest way to control the protein corona on gold nanoparticles. In a colloid of nanoparticles, both in the presence and absence of protein, aggregation-deaggregation, and in a protein colloid, monomerization-dimerization-aggregation are the main processes when pH is changed. A specific point at pH 7.5, where a transition of the colloidal system from one state to another is observed, has been found using all the optical methods mentioned. It has been shown that gold nanoparticles can stabilize HEWL protein molecules at alkaline pH while maintaining enzymatic activity, which can be used in practice. The data obtained in this manuscript allow for the state of HEWL colloids and gold nanoparticles to be monitored using one or two simple and accessible optical methods.

Albumin Protein Impact on Early-Stage In Vitro Biodegradation of Magnesium Alloy (WE43).

Mg and its alloys are promising biodegradable materials for orthopedic implants and cardiovascular stents. The first interactions of protein molecules with Mg alloy surfaces have a substantial impact on their biocompatibility and biodegradation. We investigate the early-stage electrochemical, chemical, morphological, and electrical surface potential changes of alloy WE43 in either 154 mM NaCl or Hanks' simulated physiological solutions in the absence or presence of bovine serum albumin (BSA) protein. WE43 had the lowest electrochemical current noise (ECN) fluctuations, the highest noise resistance (Zn = 1774 Ω·cm2), and the highest total impedance (|Z| = 332 Ω·cm2) when immersed for 30 min in Hanks' solution. The highest ECN, lowest Zn (1430 Ω·cm2), and |Z| (49 Ω·cm2) were observed in the NaCl solution. In the solutions containing BSA, a unique dual-mode biodegradation was observed. Adding BSA to a NaCl solution increased |Z| from 49 to 97 Ω·cm2 and decreased the ECN signal of the alloy, i.e., the BSA inhibited corrosion. On the other hand, the presence of BSA in Hanks' solution increased the rate of biodegradation by decreasing both Zn and |Z| while increasing ECN. Finally, using scanning Kelvin probe force microscopy (SKPFM), we observed an adsorbed nanolayer of BSA with aggregated and fibrillar morphology only in Hanks' solution, where the electrical surface potential was 52 mV lower than that of the Mg oxide layer.

Improvement of gelation properties of Penaeus vannamei surimi by magnetic field-assisted freezing in combination with curdlan

Traditional methods of freezing and thawing may harm the quality of meat products. In order to reduce the negative impact of freezing on surimi products, the magnetic field-assisted freezing method is combined with various curdlan ratios to enhance the gelation characteristics of Penaeus vannamei surimi in this study. The results showed that the magnetic field-assisted freezing technique significantly improved the quality of thawed surimi compared with soaking freezing (SF), whereas the addition of curdlan further improved the gelation properties, and the gel strength, water-holding capacity, textural properties, whiteness, and G′ value were significantly improved when its content was increased to 0.6 %. However, excessive amounts of curdlan interfered with protein covalent cross-linking, leading to a decrease in gel quality. Additionally, the addition of magnetic field and curdlan encouraged the shift of the α-helix to the random coil and β-sheet transition, which stimulated the growth of myofibril molecules, exposed the hydrophobic groups and thiols, improved protein-molecule interactions, and promoted systematic gathering of proteins, leading to the formation of the microstructure of dense and small pores. It also resulted in a drop in water release, an increase in the proton density and a shift in the water condition from free water to more immobile water, which had higher sensory qualities. These effects together resulted in a reduction in thawing and cooking loss to 11.41 % and 13.83 %, respectively. These results also help to clarify the gelation process of shrimp surimi and help to regulate the gelation characteristics of shrimp surimi products.

Interaction studies of recombinant laccase with co-solutes: Using various spectroscopic, calorimetric, and in silico approaches

The co-solute engineering is a rational approach to elucidate protein-molecule interactions which involves an exhaustive understanding of the structural characteristics of active sites of enzymes and their involvement in enzyme function. Laccase has attracted huge interest among scientists due to its potential uses in diverse fields. In this study, we have exploited a co-solute engineering approach to elucidate the protein interactions with polyethylene glycol (PEG of molecular weight of 400 Da, PEG 400) and amino acid (arginine). This study employed a comprehensive range of spectroscopic techniques (absorbance, fluorescence, FTIR, and circular dichroism), calorimetry, and in silico method (molecular docking) to investigate the protein conformations and determine the thermodynamic binding parameters and affinities in the presence or absence of co-solutes. Furthermore, the presence of co-solutes induced notable conformational changes in the secondary structure of the protein, transitioning it from a β -sheet configuration to an α -helical structure. The objective was to gain insights into the effect of co-solutes on the protein behavior and explore the potential of “Protein-co-solute engineering” for identifying target residues that could be modulated to alter the protein's properties. By employing these multidisciplinary approaches, the study aimed to shed light on the interactions between ligands and proteins and their potential for engineering protein function. A comprehensive understanding of the mechanisms governing protein–ligand interactions is vital for progress in drug discovery. By exploring the physical principles that regulate these interactions, such as binding kinetics, thermodynamics, and driving forces, we can uncover fundamental insights into protein folding and stability, offering a solid foundation for the development of innovative therapeutic strategies.

Kaempferol has potential anti-coronavirus disease 2019 (COVID-19) targets based on bioinformatics analyses and pharmacological effects on endotoxin-induced cytokine storm.

COVID-19 has infected 272 million patients and caused 5.33 million deaths around the world, and it remains the main global threat. Previous studies revealed that Chinese traditional medicine is an effective treatment for COVID-19 infection. This study aims to reveal the pharmacological effects of kaempferol, which is the active component of Radix Bupleuri and Tripterygii Radix, and potential mechanisms for the treatment of COVID-19. Here, we employed the bioinformatics methods to filter the anti-COVID-19 candidate genes of kaempferol, which mainly enriched in inflammation (TNF, JUN, etc.) and virus infection (AKT1, JNK, etc.). The Transcription levels of AKT1, JNK and JUN were significantly reduced by kaempferol treatment in the LPS-activated macrophages. In addition, kaempferol reduced the secretion of inflammatory factors by LPS-stimulated macrophages, inhibited MAPK/NF-κB signaling and regulated macrophage polarization to M2 type in vitro, and suppressed endotoxin-induced cytokine storm and improved survival in mice. Molecular docking analysis demonstrated that kaempferol was probable to bind the COVID-19 protein 5R84 and formatted hydrogen bond with the residues, the free binding energy of which was lower than the original ligand. In summary, our current work indicates that kaempferol has anti-COVID-19 potential through the reduction of COVID-19-induced body dysfunction and molecule-protein interaction, and bioinformatics results clarify that some of these key target genes might serve as potential molecular markers for detecting COVID-19.

An Approach to Evaluate the Effective Cytoplasmic Concentration of Bioactive Agents Interacting with a Selected Intracellular Target Protein.

To compare the effectiveness of various bioactive agents reversibly acting within a cell on a target intracellular macromolecule, it is necessary to assess effective cytoplasmic concentrations of the delivered bioactive agents. In this work, based on a simple equilibrium model and the cellular thermal shift assay (CETSA), an approach is proposed to assess effective concentrations of both a delivered bioactive agent and a target protein. This approach was tested by evaluating the average concentrations of nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated-protein 1 (Keap1) proteins in the cytoplasm for five different cell lines (Hepa1, MEF, RAW264.7, 3LL, and AML12) and comparing the results with known literature data. The proposed approach makes it possible to analyze both binary interactions and ternary competition systems; thus, it can have a wide application for the analysis of protein-protein or molecule-protein interactions in the cell. The concentrations of Nrf2 and Keap1 in the cell can be useful not only in analyzing the conditions for the activation of the Nrf2 system, but also for comparing the effectiveness of various drug delivery systems, where the delivered molecule is able to interact with Keap1.

Localization of Fusobacterium nucleatum in oral squamous cell carcinoma and its possible directly interacting protein molecules: A case series.

While 15 to 20% of cancers are associated with microbial infection, the relationship between oral microorganisms and oral squamous cell carcinoma (OSCC) remains unclear. The location of bacteria in a tumor is closely related to its carcinogenic mechanism. The aim of this study was to analyse bacterial diversity in clinical OSCC tissue samples and tumor distant normal tissues, locate target bacteria, and search for proteins that may interact with target bacteria. The 16S rDNA method was used to analyse bacterial diversity in clinical OSCC tissue samples and tumor distant normal tissues. Correlations between Fusobacterium abundance and clinicopathological characteristics were analysed using the χ2 test. The position of target bacteria was analysed by fluorescence in situ hybridization (FISH), and the expression of CK, CD31, CD45, CD68, cyclin D1, β-catenin, E-cadherin, NF-κB, and HIF-1α was analysed by immunohistochemistry (IHC) in OSCC tumor tissues and tumor distant normal tissues. The 16S rDNA results showed that the detected amount of Fusobacterium in OSCC tumor tissues was significantly larger than that in tumor distant normal tissues. High expression of Fusobacterium was significantly correlated with the lifestyle-related oral risk habits, including smoking (p=0.036) and alcohol consumption (p=0.022), but did not correlate with patient sex, age, tumor laterality, tumor size, grade or TNM stage. Fusobacterium nucleatum was enriched in tumor stroma, where CD31+ blood vessels and inflammatory cells (including CD45+ leukocytes and CD68+ macrophages) were densely distributed. Cyclin D1 was mainly expressed in the nucleus of tumor cells. β-catenin was expressed in the tumor cell membrane and was positively expressed in tumor interstitial vascular endothelial cells. E-cadherin was mainly expressed in tumor cell membranes. NF-κB was positively expressed in the cytoplasm of tumor cells, tumor interstitial cells and myo-fibrocytes. HIF-1α was mainly expressed in the cytoplasm of tumor interstitial cells. HIF-1α was highly expressed where Fusobacterium nucleatum was densely distributed. According to our study, the detected amount of Fusobacterium in OSCC tumor tissues was significantly larger than that in tumor distant normal tissues, and Fusobacterium nucleatum might aggravate inflammation and hypoxia by interacting with NF-κB and HIF-1α in OSCC.

Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction.

Simple SummaryWith the development of artificial intelligence, researchers can roughly predict the crystal structure of a protein by computer without the need for biological experiments, which provides new ideas and solutions to problems, such as protein-protein interaction and drug-target predictions. In this study, we proposed strategies to combine predicted protein structures with deep learning networks and evaluated them on different protein binding site prediction tasks. Our computational experiment results showed that all proposed strategies could effectively encode structural information for deep learning models.Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations.

The relationship between acylation degree and gelling property of NaOH-induced egg white gel: Efficient is better?

In this study, succinic anhydride (SA) and octenyl succinic anhydride (OSA) were used to modify the egg white protein (EWP), and acylated EWP was further prepared as a gel by adding NaOH and heat modification. The results showed that the acylation degree for the SA- and OSA-added groups reached 57.68% and 26.95%, respectively, and the average size, the absolute value of ζ-potential, increased significantly with SA and OSA addition, indicating that acylation improved the stability of EWP solution. Furthermore, the surface hydrophobicity increased for the SA-added group while decreasing for the OSA-added group, and the acylation process exposed the flexibility part of the protein molecule. Acylated EWP prepared gel (EWG) showed a translucent and slightly yellow appearance (except for the sol state of the OSA-added 1:50 group), the gel strength, water-holding capacity and rheological properties for SA-added EWG were remarkably reduced while OSA-added EWG improved apparently. Intermolecular forces analysis indicated that the addition of SA promoted the formation of disulfide bonds and strengthened proteins interactions while adding OSA weakened the interactions. Microstructural observations revealed a rougher gel network structure and weaker protein cross-linking in the gel prepared after the acylation of proteins. However, the high efficiency of SA and promotion of protein-molecule interactions were unable to counteract the negative effect of SA on the extension of the gel network structure, and the interaction between OSA expanded the formation of the gel network structure.

Interaction of human erythrocyte catalase with air-water interface in cryoEM.

One of the key goals in single-particle cryo-microscopy is to obtain a uniform distribution of particle orientations, so that the three-dimensional structure has isotropic resolution in Fourier space. A common problem arises from the interaction of protein molecules with the air–water interface that exists on both surfaces of the thin film of liquid that is formed prior to plunge-freezing into liquid ethane. Some proteins and other macromolecular complexes are disrupted by interaction with the air–water interface. Other proteins or macromolecules either become concentrated through their interaction with the interface or are excluded because they bind strongly to some other part of the grid or the filter paper used in blotting. In this paper, the interaction of human erythrocyte catalase with the air–water interface is investigated and minimized by the addition of certain detergents. Detergents can form an amphipathic monolayer at the air–water interface that creates a barrier and leaves the molecules free to adopt a variety of orientations, thus facilitating the 3D structure determination. These results suggest that further characterization and development of detergents for cryo-microscopy plunge-freezing would be useful.

Antimicrobial and Antibiofilm Activity of Biosynthesized Silver Nanoparticles Against Beta-lactamase-Resistant Enterococcus faecalis.

Due to the presence of antibiotic-resistant genes, treatment options of clinical isolates are exceedingly limited. This study was aimed to fabricate, optimize, characterize, and evaluate the action of silver nanoparticles (AgNPs) against a clinical isolate of Enterococcus faecalis. A combination of cell-free supernatant (C-FS) of the filamentous fungus Fusarium solani and Gram-negative Comamonas aquatica for AgNP formation was proposed; the antigrowth and antibiofilm of AgNPs against E. faecalis harboring blaTEM and blaCTX-M genes were assessed. The ratio of 1:2 v/v (C-FS:AgNO3) at pH 9.0 for 72h in 1mM AgNO3 were the optimal conditions for AgNP formation. UV-vis absorption peak appeared at 425nm and the crystalline nature of synthesized particles was verified by X-ray diffraction (XRD). Fourier transform infrared spectroscopy (FTIR) analysis confirmed the interaction of protein molecules with the AgNPs. Transmission electron microscopy (TEM) analysis demonstrated that fabricated AgNPs were relatively monodispersed, approximately spherical, and of size 2-7.5nm. blaTEM and blaCTX-M were detected in E. faecalis; the growth and biofilm of E. faecalis were significantly decreased by the action of 12.5μg/mL AgNPs. This is the first study proposing alternative sources to form AgNPs via synergistic metabolites of F. solani and C. aquatica. The results here offer a foundation for developing an effective therapy using AgNPs against clinical pathogens.

The Role of Cations of the Precipitant in the Interaction of Protein Molecules in the Lysozyme Oligomers in Crystallization Solutions

At the moment, the main opinion is that protein crystallization depends mainly on the the precipitant anions, therefore, there have been only few works devoted to the problem of the influence of its cations. Using the molecular dynamics method, we investigated the stability, changes in the compactness and structural transformations of lysozyme dimers and octamers in solutions with different precipitants (LiCl, NaCl, KCl and CuCl2) in order to study the contribution of cations during crystal formation in more detail. As a result, we found that cations have a rather noticeable effect on the behavior of oligomers: the higher the atomic mass of the cation, the greater the changes in the dimers structures during its dynamics and, according to the data of SAXS experiments, the lower the concentration of dimers. However, for octamers, this dependence is more complicated.

Construction of sRNA Regulatory Network for Magnaporthe oryzae Infecting Rice Based on Multi-Omics Data.

Studies have shown that fungi cause plant diseases through cross-species RNA interference mechanism (RNAi) and secreted protein infection mechanism. The small RNAs (sRNAs) of Magnaporthe oryzae use the RNAi mechanism of rice to realize the infection process, and different effector proteins can increase the autotoxicity by inhibiting pathogen-associated molecular patterns triggered immunity (PTI) to achieve the purpose of infection. However, the coordination of sRNAs and proteins in the process of M. oryzae infecting rice is still poorly understood. Therefore, the combination of transcriptomics and proteomics to study the mechanism of M. oryzae infecting rice has important theoretical significance and practical value for controlling rice diseases and improving rice yields. In this paper, we used the high-throughput data of various omics before and after the M. oryzae infecting rice to screen differentially expressed genes and sRNAs and predict protein interaction pairs based on the interolog and the domain-domain methods. We were then used to construct a prediction model of the M. oryzae-rice interaction proteins according to the obtained proteins in the proteomic network. Finally, for the differentially expressed genes, differentially expressed sRNAs, the corresponding mRNAs of rice and M. oryzae, and the interacting protein molecules, the M. oryzae-rice sRNA regulatory network was built and analyzed, the core nodes were selected. The functional enrichment analysis was conducted to explore the potential effect pathways and the critical infection factors of M. oryzae sRNAs and proteins were mined and analyzed. The results showed that 22 sRNAs of M. oryzae, 77 secretory proteins of M. oryzae were used as effect factors to participate in the infection process of M. oryzae. And many significantly enriched GO modules were discovered, which were related to the infection mechanism of M. oryzae.

Changes in egg yolk gelation behaviour and mechanisms during freezing

The aim of this study was to determine the changes in freezing-induced egg yolk gelation behaviour and mechanism. The rheological properties, particle size, moisture distribution, and structural characteristics of egg yolk during freezing were studied. The results showed that freezing increased the viscosity of the egg yolks. The k value, G′ and G″ all increased significantly, while the n value and tanδ decreased significantly, indicating that the egg yolk changed from liquid to solid form during freezing, and gelation occurred. Secondly, with the freezing time increased to 12 h, the average particle size of egg yolk increased from 29.42 μm to 88.16 μm, the immobile water increased from 62% to 67%, and the free water decreased from 37% to 32%. The increase of λmax in the fluorescence spectrum and the ultraviolet spectrum revealed that the exposure of tyrosine residues on the protein surface increased and the conformation of the protein changed. As observed with confocal laser scanning microscopy (CLSM), freezing destroyed the protein structure, released lipids, and promoted egg yolk protein aggregation. Freezing-induced protein molecule interactions are a key factor in frozen-thawed egg yolk gel formation.

Protein conformational entropy is not slaved to water

Conformational entropy can be an important element of the thermodynamics of protein functions such as the binding of ligands. The observed role for conformational entropy in modulating molecular recognition by proteins is in opposition to an often-invoked theory for the interaction of protein molecules with solvent water. The “solvent slaving” model predicts that protein motion is strongly coupled to various aspects of water such as bulk solvent viscosity and local hydration shell dynamics. Changes in conformational entropy are manifested in alterations of fast internal side chain motion that is detectable by NMR relaxation. We show here that the fast-internal side chain dynamics of several proteins are unaffected by changes to the hydration layer and bulk water. These observations indicate that the participation of conformational entropy in protein function is not dictated by the interaction of protein molecules and solvent water under the range of conditions normally encountered.