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To manipulate the protein concentration at a certain functional state through chemical stabilizers is crucial for protein-related studies. It not only plays a key role in protein structure analysis and protein folding kinetics, but also affects protein functionality to a large extent and thus has wide applications in medicine, food industry, etc. However, due to concerns about side effects or financial costs of stabilizers, identifying optimal strategies for enhancing protein stability with a minimal amount of stabilizers is of great importance. Here, we prove that either for the fixed terminal time (including both finite and infinite cases) or for the free one, the optimal control strategy for stabilizing the folding intermediates with a linear strategy for stabilizer addition belongs to the class of bang–bang controls. The corresponding optimal switching time is derived analytically, whose phase diagram with respect to several key parameters is explored in detail. The bang–bang control will be broken when nonlinear strategies for stabilizer addition are adopted. Moreover, the above theory is applied to the stabilization of erythropoietin by ten different kinds of chemicals, providing theoretical guidance for the selection and rational usage of stabilizers. Our current study on optimal strategies for protein stabilizers not only offers deep insights into the general picture of protein folding kinetics but also provides valuable theoretical guidance on treatments for protein-related diseases in medicine. [ABSTRACT FROM AUTHOR]

Vibrational spectroscopy of protein structure often utilizes 13C18O-labeling of backbone carbonyls to further increase structural resolution. However, sidechains such as arginine, aspartate, and glutamate absorb within the same spectral region, complicating the analysis of isotope-labeled peaks. In this study, we report that the waiting time between pump and probe pulses in two-dimensional infrared spectroscopy can be used to suppress sidechain modes in favor of backbone amide I′ modes based on differences in vibrational lifetimes. Furthermore, differences in the lifetimes of 13C18O-amide I′ modes can aid in the assignment of secondary structure for labeled residues. Using model disordered and β-sheet peptides, it was determined that while β-sheets exhibit a longer lifetime than disordered structures, amide I′ modes in both secondary structures exhibit longer lifetimes than sidechain modes. Overall, this work demonstrates that collecting 2D IR data at delayed waiting times, based on differences in vibrational lifetime between modes, can be used to effectively suppress interfering sidechain modes and further identify secondary structures. [ABSTRACT FROM AUTHOR]

Contactin 2 (CNTN2) is a cell adhesion molecule involved in axon guidance, neuronal migration, and fasciculation. The ectodomains of CNTN1-CNTN6 are composed of six Ig domains (Ig1-Ig6) and four FN domains. Here, we show that CNTN2 forms transient homophilic interactions (KD ∼200 nM). Cryo-EM structures of full-length CNTN2 and CNTN2_Ig1-Ig6 reveal a T-shaped homodimer formed by intertwined, parallel monomers. Unexpectedly, the horseshoe-shaped Ig1-Ig4 headpieces extend their Ig2-Ig3 tips outwards on either side of the homodimer, while Ig4, Ig5, Ig6, and the FN domains form a central stalk. Cross-linking mass spectrometry and cell-based binding assays confirm the 3D assembly of the CNTN2 homodimer. The interface mediating homodimer formation differs between CNTNs, as do the homophilic versus heterophilic interaction mechanisms. The CNTN family thus encodes a versatile molecular platform that supports a very diverse portfolio of protein interactions and that can be leveraged to strategically guide neural circuit development.

We studied the origin of the vibrational signatures in the sum-frequency generation (SFG) spectrum of fibrillar collagen type I in the carbon–hydrogen stretching regime. For this purpose, we developed an all-reflective, laser-scanning SFG microscope with minimum chromatic aberrations and excellent retention of the polarization state of the incident beams. We performed detailed SFG measurements of aligned collagen fibers obtained from rat tail tendon, enabling the characterization of the magnitude and polarization-orientation dependence of individual tensor elements X ijk 2 of collagen's nonlinear susceptibility. Using the three-dimensional atomic positions derived from published crystallographic data of collagen type I, we simulated its X ijk 2 elements for the methylene stretching vibration and compared the predicted response with the experimental results. Our analysis revealed that the carbon–hydrogen stretching range of the SFG spectrum is dominated by symmetric stretching modes of methylene bridge groups on the pyrrolidine rings of the proline and hydroxyproline residues, giving rise to a dominant peak near 2942 cm−1 and a shoulder at 2917 cm−1. Weak asymmetric stretches of the methylene bridge group of glycine are observed in the region near 2870 cm−1, whereas asymmetric CH2-stretching modes on the pyrrolidine rings are found in the 2980 to 3030 cm−1 range. These findings help predict the protein's nonlinear optical properties from its crystal structure, thus establishing a connection between the protein structure and SFG spectroscopic measurements. [ABSTRACT FROM AUTHOR]

Electron spin resonance pulsed dipolar spectroscopy (PDS) has become popular in protein 3D structure analysis. PDS studies yield distance distributions between a pair or multiple pairs of spin probes attached to protein molecules, which can be used directly in structural studies or as constraints in theoretical predictions. Double-quantum coherence (DQC) is a highly sensitive and accurate PDS technique to study protein structures in the solid state and under physiologically relevant conditions. In this work, we have derived analytical expressions for the DQC signal for a system with N-dipolar coupled spin-1/2 particles in the solid state. The expressions are integrated over the relevant spatial parameters to obtain closed form DQC signal expressions. These expressions contain the concentration-dependent "instantaneous diffusion" and the background signal. For micromolar and lower concentrations, these effects are negligible. An approximate analysis is provided for cases of finite pulses. The expressions obtained in this work should improve the analysis of DQC experimental data significantly, and the analytical approach could be extended easily to a wide range of magnetic resonance phenomena. [ABSTRACT FROM AUTHOR]

Cells employ many large macromolecular machines for the execution and regulation of processes that are vital for cell and organismal viability. Interestingly, cells cannot synthesize these machines as functioning units. Instead, cells synthesize the molecular parts that must then assemble into the functional complex. Many important machines, including chaperones such as GroEL and proteases such as the proteasome, comprise protein rings that are stacked on top of one another. While there is some experimental data regarding how stacked-ring complexes such as the proteasome self-assemble, a comprehensive understanding of the dynamics of stacked-ring assembly is currently lacking. Here, we developed a mathematical model of stacked-trimer assembly and performed an analysis of the assembly of the stacked homomeric trimer, which is the simplest stacked-ring architecture. We found that stacked rings are particularly susceptible to a form of kinetic trapping that we term deadlock, in which the system gets stuck in a state where there are many large intermediates that are not the fully assembled structure but that cannot productively react. When interaction affinities are uniformly strong, deadlock severely limits assembly yield. We thus predicted that stacked rings would avoid situations where all interfaces in the structure have high affinity. Analysis of available crystal structures indicated that indeed the majority-if not all-of stacked trimers do not contain uniformly strong interactions. Finally, to better understand the origins of deadlock, we developed a formal pathway analysis and showed that, when all the binding affinities are strong, many of the possible pathways are utilized. In contrast, optimal assembly strategies utilize only a small number of pathways. Our work suggests that deadlock is a critical factor influencing the evolution of macromolecular machines and provides general principles for understanding the self-assembly efficiency of existing machines.

Thermostable clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas9) enzymes could improve genome-editing efficiency and delivery due to extended protein lifetimes. However, initial experimentation demonstrated Geobacillus stearothermophilus Cas9 (GeoCas9) to be virtually inactive when used in cultured human cells. Laboratory-evolved variants of GeoCas9 overcome this natural limitation by acquiring mutations in the wedge (WED) domain that produce >100-fold-higher genome-editing levels. Cryoelectron microscopy (cryo-EM) structures of the wild-type and improved GeoCas9 (iGeoCas9) enzymes reveal extended contacts between the WED domain of iGeoCas9 and DNA substrates. Biochemical analysis shows that iGeoCas9 accelerates DNA unwinding to capture substrates under the magnesium-restricted conditions typical of mammalian but not bacterial cells. These findings enabled rational engineering of other Cas9 orthologs to enhance genome-editing levels, pointing to a general strategy for editing enzyme improvement. Together, these results uncover a new role for the Cas9 WED domain in DNA unwinding and demonstrate how accelerated target unwinding dramatically improves Cas9-induced genome-editing activity.

The application of terahertz radiation has been shown to affect both protein structure and cellular function. As the key to such structural changes lies in the dynamic response of a protein, we focus on the susceptibility of the protein's internal dynamics to mechanical stress induced by acoustic pressure waves. We use the open-boundary molecular dynamics method, which allows us to simulate the protein exposed to acoustic waves. By analyzing the dynamic fluctuations of the protein subunits, we demonstrate that the protein is highly susceptible to acoustic waves with frequencies corresponding to those of the internal protein vibrations. This is confirmed by changes in the compactness of the protein structure. As the amplitude of the pressure wave increases, even larger deviations from average positions and larger changes in protein compactness are observed. Furthermore, performing the mode-projection analysis, we show that the breathing-like character of collective modes is enhanced at frequencies corresponding to those used to excite the protein. [ABSTRACT FROM AUTHOR]

The transcription factor and cell cycle regulator p53 is marked for degradation by the ubiquitin ligase MDM2. The interaction between these 2 proteins is mediated by a conserved binding motif in the disordered p53 transactivation domain (p53TAD) and the folded SWIB domain in MDM2. The conserved motif in p53TAD from zebrafish displays a 20-fold weaker interaction with MDM2, compared to the interaction in human and chicken. To investigate this apparent difference, we tracked the molecular evolution of the p53TAD/MDM2 interaction among ray-finned fishes (Actinopterygii), the largest vertebrate clade. Intriguingly, phylogenetic analyses, ancestral sequence reconstructions, and binding experiments showed that different loss-of-affinity changes in the canonical binding motif within p53TAD have occurred repeatedly and convergently in different fish lineages, resulting in relatively low extant affinities (KD = 0.5 to 5 μM). However, for 11 different fish p53TAD/MDM2 interactions, nonconserved regions flanking the canonical motif increased the affinity 4- to 73-fold to be on par with the human interaction. Our findings suggest that compensating changes at conserved and nonconserved positions within the motif, as well as in flanking regions of low conservation, underlie a stabilizing selection of functional affinity in the p53TAD/MDM2 interaction. Such interplay complicates bioinformatic prediction of binding and calls for experimental validation. Motif-mediated protein-protein interactions involving short binding motifs and folded interaction domains are very common across multicellular life. It is likely that the evolution of affinity in motif-mediated interactions often involves an interplay between specific interactions made by conserved motif residues and nonspecific interactions by nonconserved disordered regions.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Complex II (CII) activity controls phenomena that require crosstalk between metabolism and signaling, including neurodegeneration, cancer metabolism, immune activation, and ischemia-reperfusion injury. CII activity can be regulated at the level of assembly, a process that leverages metastable assembly intermediates. The nature of these intermediates and how CII subunits transfer between metastable complexes remains unclear. In this work, we identify metastable species containing the SDHA subunit and its assembly factors, and we assign a preferred temporal sequence of appearance of these species during CII assembly. Structures of two species show that the assembly factors undergo disordered-to-ordered transitions without the appearance of significant secondary structure. The findings identify that intrinsically disordered regions are critical in regulating CII assembly, an observation that has implications for the control of assembly in other biomolecular complexes.

Studying specific subpopulations of cancer-derived extracellular vesicles (EVs) could help reveal their role in cancer progression. In cancer, an increase in reactive oxygen species (ROS) happens which results in lipid peroxidation with a major product of 4-hydroxynonenal (HNE). Adduction by HNE causes alteration to the structure of proteins, leading to loss of function. Blebbing of EVs carrying these HNE-adducted proteins as a cargo or carrying HNE-adducted on EV membrane are methods for clearing these molecules by the cells. We have referred to these EVs as Redox EVs. Here, we utilize a surface tension-mediated extraction process, termed exclusion-based sample preparation (ESP), for the rapid and efficient isolation of intact Redox EVs, from a mixed population of EVs derived from human glioblastoma cell line LN18. After optimizing different parameters, two populations of EVs were analyzed, those isolated from the sample (Redox EVs) and those remaining in the original sample (Remaining EVs). Electron microscopic imaging was used to confirm the presence of HNE adducts on the outer leaflet of Redox EVs. Moreover, the population of HNE-adducted Redox EVs shows significantly different characteristics to those of Remaining EVs including smaller size EVs and a more negative zeta potential EVs. We further treated glioblastoma cells (LN18), radiation-resistant glioblastoma cells (RR-LN18), and normal human astrocytes (NHA) with both Remaining and Redox EV populations. Our results indicate that Redox EVs promote the growth of glioblastoma cells, likely through the production of H2O2, and cause injury to normal astrocytes. In contrast, Remaining EVs have minimal impact on the viability of both glioblastoma cells and NHA cells. Thus, isolating a subpopulation of EVs employing ESP-based immunoaffinity could pave the way for a deeper mechanistic understanding of how subtypes of EVs, such as those containing HNE-adducted proteins, induce biological changes in the cells that take up these EVs. [ABSTRACT FROM AUTHOR]

The molecular interactivities between proteins and polysaccharides exhibit a profound role in characteristics of binary hydrogels. The binary gels were characterized from the major aspects; molecular behavior, microstructure, texture, rheology, water-holding capacity (WHC), and swelling ratio (SR). Despite previous articles claiming the electrostatic interactions to be the main driving forces of gelation, it was shown that gelation of animal-derived proteins and polysaccharides are totally in line with hydrogen and hydrophobic forces. Polysaccharides unfold proteins, evidenced by increased surface hydrophobicity and fluorescence quenching. Polysaccharides modify both secondary and tertiary structures of proteins by facilitating the transition of α-helix to β-sheet along with all-gauche conformation to trans-gauche-gauche. The π-π stacking causes enhanced adhesiveness, elasticity, gel strength, springiness, resistance to fracture, WHC, and SR. Increased specific surface area and surface active energy enhance the gelation properties. Both excessive total polymer concentrations and hydrophobic interactions weaken the structure. Polysaccharide addition enhanced the gel tendency toward water, so WHC and SR increased. However, excess protein reduction abruptly reduced them. Therefore, a threshold was known for protein/polysaccharide mass ratio. In common, polysaccharide made the structure harder than single protein gel. [ABSTRACT FROM AUTHOR]

Pea (Pisum sativum) protein, as a pulse protein second only to soy protein, has shown increasing popularity in the plant-based food market. Numerous studies have shown the desirable techno-functionalities of pea protein for emulsifying, gelling, and foaming. Nevertheless, poor digestibility and unpleasant beany flavors of pea protein lower consumer acceptance. The current state of understanding on digestibility and beany attributes of pea proteins could help pave the way for pea protein-based food formulation with improved quality. This paper covers recently reported studies to modulate the digestibility and beany notes of pea protein by many processing techniques, and highlights the influence of non-protein components and food matrix structures while formulating pea protein-based food systems. Several modification techniques could alter the digestibility and improve the flavor of pea proteins by reorienting the protein structures or forming protein-based complexes. Precise tailoring the digestibility/flavor of pea proteins is promising based on current research, though further investigation is still required to reduce adverse effects or side products. In addition, more studies on simulated pea protein-based food systems are required to mimic the actual food comprising multi-components and specific structures to narrow the gap between lab-scale research and food industry applications. [ABSTRACT FROM AUTHOR]

Target-first drug discovery relies heavily on protein structure information, which severely limits its application. In recent years, fragment-based drug Design (FBDD) has been identified as an alternative solution, where screening of smaller molecules for lower affinity allowed the use of focused libraries with a higher hit rate. It is shown that coupling an sp2-rich heteroaromatic group with a monofunctional sp3-rich core gives fragments (186 examples) with advantageous physical-chemical properties, covering a chemical space often neglected in traditional libraries. [ABSTRACT FROM AUTHOR]

The goal of food allergy (FA) prevention and treatment is to induce oral tolerance (OT). Appropriate nutritional interventions are essential to induce OT to food allergens. This review introduces the mechanism of OT and the importance of early nutritional interventions, and then firstly summarizes specific nutritional factors to induce the development of OT of FA, including proteins, vitamins, fatty acids, saccharides and probiotics. The regulatory mechanism mainly induces the development of tolerance by increasing local or systemic protective regulatory T cells (Tregs) to suppress FA, while the gut microbiota may also be changed to maintain intestinal homeostasis. For allergens-specific OT, the disruption to the structure of proteins and epitopes is critical for the induction of tolerance by hydrolyzed and heated proteins. Vitamins (vitamin A, D), fatty acids, saccharides and probiotics as allergens nonspecific OT also induce the development of OT through immunomodulatory effects. This review contributes to our understanding of OT in FA through nutritional interventions. Nutritional interventions play an important role in the induction of OT, and offer promising approaches to reduce allergy risk and alleviate FA. Moreover, due to the importance and diversity of nutrition, it must be the future trend of induction of OT in FA. [ABSTRACT FROM AUTHOR]

AbstractMercury (Hg) pollution poses significant threats to human health and ecosystems worldwide due to its persistence, bioaccumulation, and toxic effects. This study focuses on mercuric reductase from Pseudomonas fluorescens (UniProt ID Q51772), a key enzyme involved in detoxifying mercury through reduction to its less toxic elemental form, Hg(0). This study aims to explore the potential of this enzyme for bioremediation applications, focusing on structural insights, functional mechanisms, and biotechnological enhancements to facilitate mercury detoxification. The protein sequence of Q51772 was analyzed using bioinformatics tools to determine its structural and functional attributes. Physiochemical properties, including molecular weight, isoelectric point, and secondary structure predictions, were assessed. Virulence prediction tools confirmed the protein’s nontoxic and nonpathogenic nature. Homology modeling and docking studies provided insights into its three-dimensional structure and binding interactions with mercury substrates. Q51772 consists of 548 amino acids and belongs to the Pyridine nucleotide-disulphide oxidoreductase family, class I. It features specific domains crucial for mercury reduction, identified through Pfam and InterPro analyses. Physiochemical analysis indicated a stable protein with hydrophilic tendencies conducive to enzymatic function. Structural modeling validated by ProQ and PROCHECK confirmed the reliability of the predicted structure. Molecular docking of organic mercury compound with wild-type and mutant mercuric reductase revealed strong binding affinities, with key hydrogen bonds involving residues Gly95, Thr122, and Asp390. Mercuric reductase Q51772 from Pseudomonas fluorescens emerges as a promising candidate for bioremediation of mercury-contaminated environments. Future research should focus on optimizing its performance under diverse environmental conditions to advance sustainable solutions for mercury pollution control. [ABSTRACT FROM AUTHOR]

Structural diversity of diterpenes is mediated by the enigmatic family of diterpene synthases. The overall enzymatic contribution hereby lies in a carefully concerted chemistry of highly reactive carbocation intermediates mainly guided by aromatic and polar amino acid side chains and the pyrophosphate cofactor. To date several studies aimed to shed light on the mechanism underlining terpene synthases chemistry. Specifically, the diterpene synthase CotB2 serves as model enzyme for detailed mutagenesis studies. Here we investigate the catalytic mechanism of CotB2 variant V80L in a holistic, biochemical, structural, and computational biology approach. We were able to identify an altered product profile compared to CotB2WT for the substrates geranylgeranyl diphosphate and farnesyl diphosphate. Moreover, we solved the crystal structure, and shed further light on terpene synthase chemistry by modelling of the substrate and intermediate binding. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Conclusions Wilson's disease (WD) results from pathogenic ATP7B gene variations, causing copper accumulation mainly in the liver, brain, and kidneys.In India, despite studies on ATP7B variants, WD often goes undiagnosed, with the prevalence, carrier rate, and mutation spectrum remaining unknown.A multicenter study examined genetic variations in WD among individuals of Indian origin via whole exome sequencing. The study used the InDelible structural variants calling pipeline and conducted molecular dynamic simulations on variants of uncertain significance (VUS) in ATP7B AlphaFold protein structures. Additionally, a high‐throughput gene screening panel for WD was developed.This study examined 128 clinically diagnosed cases of WD, revealing 74 genetically confirmed cases, 22 with ATP7B variants, and 32 without. Twenty‐two novel ATP7B gene variants were identified, including a 322 bp deletion classified as a structural variant. Molecular dynamics simulations highlighted the potential deleterious effects of 11 ATP7B VUS. Gene burden analysis suggested associations with ANO8, LGR4, and CDC7. ATP7B gene hotspots for pathogenic variants were identified. Prevalence and carrier rates were determined as one in 18,678 and one in 67, respectively. A multiplex sequencing panel showed promise for accurate WD diagnosis.This study offers crucial insights into WD's genetic variations and prevalence in India, addressing its underdiagnosis. It highlights the novel genetic variants in the ATP7B gene, the involvement of other genes, a scalable, cost‐effective multiplex sequencing panel for WD diagnosis and management and promising advancements in WD care. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusions The hair shaft is often exposed to shampoo and haircare products that have unknown or varying pH levels. These products contain a combination of surfactants and other active ingredients to treat the hair or the scalp. As amphoteric proteins, hair keratins have limited buffering capacity, so variations in pH can have multifaceted impacts on them. However, there is limited knowledge about how pH affects keratins and keratin‐associated proteins (KAPs). Therefore, this study aims to examine the effects of varying pH levels (pH 3–pH 12) on hair structure and analyse consequent alterations in the hair proteome using mass spectrometry‐based proteomics.A scanning electron microscope was used to examine changes in hair‐shaft morphology due to exposure to various pH levels, while mass spectrometry was employed to analyse protein alterations.We found that exposing the hair shaft to varying pH levels led to specific effects on the cuticle, including cuticle lifting at certain pH levels, while proteomics analysis identified alterations in the hair proteome along with significant deamidation of keratins types I and II and KAPs. More pronounced effects were observed at extreme acidic conditions (pH 3) and alkaline conditions (above pH 8) on both hair morphology and hair proteins. pH levels between pH 5 and pH 7 had minimal impact on hair structure and proteins, suggesting that haircare products with pH in this range are ideal for hair‐shaft health. In contrast, alkaline pH levels were found to negatively affect hair.The structure evaluation and proteomics data emphasize the critical role of pH in hair health. The extreme acidic or alkaline pH impacts the hair structure and hair proteins. The study highlights the optimal pH range for maintaining healthy hair. [ABSTRACT FROM AUTHOR]

Background: The PKD2, encoding polycystin-2 (PC2) protein, is second major genetic determinant of autosomal dominant polycystic kidney disease (ADPKD) after PKD1. However, the structural and functional consequences of genetic variants in PKD2 remain poorly understood. Given the complexity and heterogeneous nature of ADPKD, understanding its pathogenesis at cellular and molecular levels is vital for deciphering genotype–phenotype correlations and disease severity, thus informing patient-centered treatments. We analyzed missense variants of PKD2 to assess their impact on RNA structure using computational tools and explored associated protein structure dynamics through MD simulation. Results: Our findings reveal distinct structural alterations and dynamic behaviors associated with specific missense variants. The variants such as c.1789C > A (p.L597M), c.1109G > A (p.S370N), c.1849C > A (p.L617I), and c.646 T > C (p.Y216H) induced major changes not only in RNA structure and accessibility profile but also in protein structure dynamics. In contrast, variants such as c.915C > A (p.N305K), c.1354A > G (p.I452V), and c.568G > A (p.A190T) resulted in minor alterations in RNA structure but exhibited noticeable effects on certain parameters of protein structure dynamics. Conclusion: This study suggests the multifaceted impact of these missense variants on both RNA and protein levels. It lays the groundwork in identifying high-impact variants in terms of pathogenicity and prioritizing these for further implications in understanding disease heterogeneity and eventually contributing to the development of targeted therapeutic interventions for ADPKD. Study Highlights: PKD2 missense variants analyzed for impact on RNA and protein Structures Alterations in RNA Structures and protein dynamics observed Computational integration of analyses aids in prioritizing variants for further study Provide insights into disease heterogeneity and potential therapeutic targets [ABSTRACT FROM AUTHOR]

Progressive familial intrahepatic cholestasis type 3 (PFIC3) is a severe hepatic disorder characterized by cholestasis. Elucidating the genotype-phenotype correlations and expanding the mutational spectrum of the ABCB4 gene are crucial for enhancing diagnostic accuracy and therapeutic strategies.Clinical and genetic data from 2 original PFIC3 patients from our institution, along with 118 additional cases identified through a comprehensive literature review, were integrated for a comprehensive analysis. The study included statistical analysis of clinical information, genetic analysis, multi-species sequence alignment, protein structure modeling, and pathogenicity assessment. Machine learning techniques were applied to identify genotype-phenotype relationships. We identified three novel ABCB4 mutations: two missense mutations (c.904G > T and c.2493G > C) and one splicing mutation (c.1230 + 1G > A). Homozygous mutations were associated with significantly earlier disease onset compared to compound heterozygous mutations (p < 0.0001). Missense mutations were predominant (76.9%), with Exon 7 being the most frequently affected region. A random forest model indicated that Exon 10 had the highest feature importance score (9.9%). Liver transplantation remains the most effective treatment modality for PFIC3. This investigation broadens the known mutation spectrum of the ABCB4 gene and identifies key variant sites associated with clinical manifestations. These insights lay a foundation for early diagnosis, optimal treatment selection, and further research into PFIC3. [ABSTRACT FROM AUTHOR]

Lipid peroxidation, occurring through enzymatic or non‐enzymatic processes, generates lipid‐derived electrophiles (LDEs), which can covalently modify nucleophilic amino acid residues in proteins, a process known as protein lipoxidation. This modification can alter protein structure and function, either causing damage or regulating signalling pathways. Identifying the protein targets and specific lipoxidation sites provide important clues for unveiling the oxidative stress‐related protein interaction network and molecular mechanisms of related diseases. In this review, we present a detailed overview of recent advances in protein LDE modification profiling, with a focus on mass spectrometry (MS)‐based chemoproteomic platforms for global protein lipoxidation profiling. [ABSTRACT FROM AUTHOR]

β-carotene (BCR) is the most abundant carotenoid, a colorant, antioxidant, and provitamin A. The extreme hydrophobicity of this hydrocarbon requires special mechanisms for distribution in aqueous media, including water-soluble carotenoproteins. However, all known carotenoproteins prefer oxygenated carotenoids and bind BCR inefficiently. Here, we present the crystal structure of the BCR-binding protein (BBP) from gregarious male locusts, which is responsible for their vivid yellow body coloration, in complex with its natural ligand, BCR. BBP forms an antiparallel tubular homodimer with α/β-wrap folded monomers, each forming a hydrophobic 47 Å long, coaxial tunnel that opens outward and is occupied by one s-cisC6-C7, all-trans BCR molecule. In the BCR absence, BBP accepts a range of xanthophylls, with reduced efficiency depending on the position and number of oxygen atoms, but rejects lycopene. The structure captures a pigment complex with a Takeout 1 protein and inspires potential applications of BBP as a BCR solubilizer. [Display omitted] • Crystal structure of a soluble protein in complex with β-carotene (BBP) determined • Extended BBP dimer has tubular subunits each binding a single β-carotene molecule • BBP is selective for β-carotene, rejects lycopene, and tolerates some xanthophylls • BBP apoform shows efficient ligand-assisted refolding when dissolved in 8 M urea Among soluble carotenoproteins, those binding carotenes are rare. Egorkin et al. determined the crystal structure of genuine β-carotene-binding protein (BBP) from gregarious locusts responsible for their yellow body coloration. Reconstitution studies revealed a selectivity for β-carotene, inability to bind lycopene, and unusual blue spectral shifts for carotenoids upon binding to BBP. [ABSTRACT FROM AUTHOR]

AlphaFold can accurately predict static protein structures but does not account for solvent conditions. Human leucine zipper EF-hand transmembrane protein-1 (LETM1) has one sequence-identifiable EF-hand but how calcium (Ca2+) affects structure and function remains enigmatic. Here, we used highly confident AlphaFold Cα predictions to guide nuclear Overhauser effect (NOE) assignments and structure calculation of the LETM1 EF-hand in the presence of Ca2+. The resultant NMR structure exposes pairing between a partial loop-helix and full helix-loop-helix, forming an unprecedented F-EF-hand with non-canonical Ca2+ coordination but enhanced hydrophobicity for protein interactions compared to calmodulin. The structure also reveals the basis for pH sensing at the link between canonical and partial EF-hands. Functionally, mutations that augmented or weakened Ca2+ binding increased or decreased matrix Ca2+, respectively, establishing F-EF as a two-way mitochondrial Ca2+ regulator. Thus, we show how to synergize AI prediction with NMR data, elucidating a solution-specific and extraordinary LETM1 F-EF-hand. [Display omitted] • An AI-informed NMR structure elucidation method reveals a unique LETM1 F-EF-hand • The Ca2+ bound LETM1 F-EF-hand adopts an open conformation with high hydrophobicity • High affinity LETM1 F-EF-hand Ca2+ binding increases mitochondrial matrix Ca2+ • Abrogated LETM1 F-EF-hand Ca2+ binding decreases mitochondrial matrix Ca2+ AI has revolutionized structural biology but must be applied responsibly. Lin et al. present a method for synergizing AlphaFold predictions with solution NMR data, using the approach to expose an unprecedented F-EF-hand domain in LETM1. This LETM1 F-EF-hand functions as a weak Ca2+ binding sensor that modulates mitochondrial matrix Ca2+. [ABSTRACT FROM AUTHOR]

Aquatic products are a high‐quality source of protein for humans, and the changes in protein during aquatic product processing are crucial for nutritional value, product performance, and consumer health. With the advancement of science and technology, aquatic product processing methods have become increasingly diverse. In addition to traditional methods such as thermal processing (steaming, roasting, and frying) and pickling, emerging non‐thermal processing technologies, such as high pressure, ultrasound, and irradiation, are also being applied. During (ultra‐)processing, aquatic products undergo complex biochemical reactions, among which protein oxidation significantly affects the quality of aquatic products. Protein oxidation can alter the molecular structure of proteins, thereby changing their functional properties and ultimately impacting product quality. This paper primarily explored the effects of protein changes under different processing methods on aquatic product quality and human health, as well as techniques for controlling protein oxidation. It aims to provide a theoretical basis for selecting appropriate processing methods, improving aquatic product quality, and controlling protein oxidation in aquatic products, and to offer scientific guidance for practical production. [ABSTRACT FROM AUTHOR]

The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either protein sequences or protein structures. Here, we present a method for integrating information from sequence and structure in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments. We show that by integrating both types of information we obtain a variant effect prediction model that is robust when sequence information is scarce. We also show that SSEmb learns embeddings of the sequence and structure that are useful for other downstream tasks such as to predict protein-protein binding sites. We envisage that SSEmb may be useful both for variant effect predictions and as a representation for learning to predict protein properties that depend on sequence and structure. SSEmb is a multi-modal machine learning model that predicts how changes in a protein's amino acid sequence affect its function by combining information from a multiple sequence alignment and the three-dimensional structure. [ABSTRACT FROM AUTHOR]

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions. Here, the authors present MCGLPPI, a mesoscopic-scale machine learning framework for modeling protein structures. Using coarse-grained models, MCGLPPI achieves accurate, low-cost PPI predictions compared to its atom- and residue-scale counterparts. [ABSTRACT FROM AUTHOR]

Posttranslational modifications (PTMs) and metal ions are integral players in the complex regulatory orchestra governing protein function. Several metal ions, including zinc, iron, copper, and others, intricately influence protein structure and activity as cofactors. Concurrently, PTMs, such as phosphorylation, acetylation, and ubiquitination, add an additional layer of complexity to the proteome, dynamically shaping cellular responses. Understanding this bidirectional relationship is crucial for unraveling the complex web of cellular regulation. This review explores the reciprocal impact of metal ions on PTMs and vice versa and provides examples and insights into how metal ions modulate PTMs and, conversely, how PTMs influence metal‐binding proteins, shedding light on the complex crosstalk between these two crucial facets of cellular biology. This interplay is not only central to cellular homeostasis but also holds implications for diseases associated with dysregulated metal‐ion and PTM processes. [ABSTRACT FROM AUTHOR]

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains. Author summary: In immune receptors across various organisms, repeating protein structures play a crucial role in recognizing and responding to pathogen threats. These structures resemble the coils of a slinky toy, allowing these receptors to adapt and change over time. One particularly vital but challenging structure to study is the Leucine Rich Repeat (LRR). Traditional methods that rely just on analyzing the sequence of these proteins can miss subtle changes due to rapid evolution. With the introduction of protein structure prediction tools like AlphaFold 2, annotation methods can study the coarser geometric properties of the structure. In this study, we visualize LRR proteins in three dimensions and use a mathematical approach to 'flatten' them into two dimensions, so that the coils form circles. We then used a mathematical concept called winding number to determine the number of repeats and where they are in a protein sequence. This process helps reveal their repeating patterns with enhanced clarity. When we applied this method to immune receptors from a model plant organism, we found that our approach could accurately identify coiling patterns. Furthermore, we detected errors made by previous methods and highlighted unique structural variations. Our research offers a fresh perspective on understanding immune receptors, potentially influencing studies on their evolution and function. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a metabolic syndrome characterized by hyperglycaemia, polydipsia, polyuria, and weight loss, among others. The pathophysiology for the disorders is complex and results in pancreatic abnormal function. Viruses have also been implicated in the metabolic syndrome. This study charted peptides to investigate and predict the autoimmune potential of shared sequences between 8 viral species proteins (which we termed 'diabetes-viruses') and the human pancreatic proteins. The structure and immunological relevance of shared sequences between viruses reported in DM onset and human pancreatic proteins were analysed. At nonapeptide mapping between human pancreatic protein and 'diabetic-viruses', reveal 1064 shared sequences distributed among 454 humans and 4288 viral protein sequences. The viral results showed herpesviruses, enterovirus (EV), human endogenous retrovirus, influenza A viruses, rotavirus, and rubivirus sequences are hosted by the human pancreatic protein. The most common shared nonapeptide was AAAAAAAAA, present in 30 human nonredundant sequences. Among the viral species, the shared sequence NSLEVLFQG occurred in 18 nonredundant EVs protein, while occurring merely in 1 human protein, whereas LGLDIEIAT occurred in 8 influenza A viruses overlapping to 1 human protein and KDELSEARE occurred in 2 rotaviruses. The prediction of the location of the shared sequences in the protein structures, showed most of the shared sequences are exposed and located either on the surface or cleft relative to the entire protein structure. Besides, the peptides in the viral protein shareome were predicted computationally for binding to MHC molecules. Here analyses showed that the entire 1064 shared sequences predicted 203 to be either HLA-A or B supertype-restricted epitopes. Fifty-one of the putative epitopes matched reported HLA ligands/T-cell epitopes majorly coming from EV B supertype representative allele restrictions. These data, shared sequences, and epitope charts provide important insight into the role of viruses on the onset of DM and its implications. [ABSTRACT FROM AUTHOR]

Protein language models (pLMs) have emerged as potent tools for predicting and designing protein structure and function, and the degree to which these models fundamentally understand the inherent biophysics of protein structure stands as an open question. Motivated by a finding that pLM-based structure predictors erroneously predict nonphysical structures for protein isoforms, we investigated the nature of sequence context needed for contact predictions in the pLM Evolutionary Scale Modeling (ESM-2). We demonstrate by use of a "categorical Jacobian" calculation that ESM-2 stores statistics of coevolving residues, analogously to simpler modeling approaches like Markov Random Fields and Multivariate Gaussian models. We further investigated how ESM-2 "stores" information needed to predict contacts by comparing sequence masking strategies, and found that providing local windows of sequence information allowed ESM-2 to best recover predicted contacts. This suggests that pLMs predict contacts by storing motifs of pairwise contacts. Our investigation highlights the limitations of current pLMs and underscores the importance of understanding the underlying mechanisms of these models. [ABSTRACT FROM AUTHOR]

Background: The prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy. Results: Addressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein's primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction. Conclusion: Compared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model. [ABSTRACT FROM AUTHOR]

Nanobiotechnology is a field of science with tremendous promising applications in cancer chemotherapy, targeted drug delivery, molecular diagnosis, and molecular imaging. Selenium, one of the critical trace elements, participates in the structure of selenocysteine protein, which plays an important role in maintaining redox balance due to its oxidoreductase activity. Elemental selenium (Se0) has significantly lower toxicity values than its oxyanions, selenite, and selenate forms. For this reason, it is important to convert elemental selenium into atomic form in biological studies. In particular, their role in regulating the immune system makes selenium nanoparticles (SeNPs) superior to others. SeNPs have been discussed effectively in the literature in anticancer activity applications. SeNPs green synthesis is focused on a more economical, biocompatible, and environmentally friendly application by using, reducing, and stabilizing agents in plants. The aim of this review is to highlight the anticancer activity applications of SeNPs synthesized with plant extracts in the last 6 years (i.e., between 2019 and 2024). In addition, it aims to shed light on the green synthesis mechanism of NPs by using plant extracts and to highlight the factors affecting green synthesis and its advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Edible mushroom proteins hold great potential for food applications, but those extracted using the alkaline extraction–acid precipitation method typically exhibit poor solubility in neutral water, with the structural changes during acid precipitation remaining unclear. In this study, Pleurotus geesteranus protein isolate (PGPI) with high water solubility was prepared with alkaline extraction, followed by dialysis and freeze‐drying, and the effects of pH on the structural and functional properties of PGPI were systematically investigated. PGPI was enriched in essential and aromatic amino acids, and the molecular weight of bands in the sodium dodecyl sulfate–polyacrylamide gel electrophoresis profile was mainly distributed below 45 kDa. The zeta potential of PGPI changed from +16.84 to 17.58 mV when the pH increased from 2 to 9, with a pI of 4.3. At pH 7, PGPI showed a size of 232.7 nm. Away from pH 7, the particle size of PGPI increased. When the pH decreased from 7 to 2, PGPI exhibited a lower α‐helix structure content and a higher β‐sheet content and a gradual decrease in fluorescence intensity. In addition, as the pH approached 4, H0 and the content of SS group increased to a peak. These results indicated that lowering the pH induced the development of more ordered protein structure, which could be the primary reason for the poor water solubility of P. geesteranus protein obtained through alkaline extraction and acid precipitation. Additionally, these structural changes result in alterations to its functional properties, including water‐holding capacity, oil‐holding capacity, foaming capacity, foaming stability, emulsion activity index, and emulsion stability index. [ABSTRACT FROM AUTHOR]

In living systems, protein assemblies have essential functions, serving as structural supports, transport highways for molecular cargo, and containers of genetic material. The construction of protein assemblies, which involves control over space and time, remains a significant challenge in biotechnology. Here, we show that anionic boron clusters, 3,3′‐commo‐bis[closo‐1,2‐dicarba‐3‐cobaltadodecaborane] (COSAN−), and halogenated closo‐dodecarboranes (B12X122−, X=H, Cl, or I), described as super‐chaotropic nano‐ions, induce the formation of 2D assemblies of model proteins, myoglobin, carbonic anhydrase, and trypsin inhibitor. We found that the nano‐ion concentration reversibly controls the size of the protein assemblies. Furthermore, the secondary structures of the proteins are only slightly affected by assembly formation. For myoglobin, the formation of these assemblies even prevents temperature denaturation, highlighting a preservation effect of nano‐ions. Our study reveals that inorganic boron‐based nano‐ions act as a reversible molecular glue for proteins, providing a potential starting point for the further development of controlled protein assemblies. [ABSTRACT FROM AUTHOR]

Avian leukosis is a significant viral disease affecting chicken populations globally, including Bangladesh, resulting in high mortality and morbidity rates and causing substantial economic losses in the commercial poultry industry. This study aimed to detect avian leukosis virus (ALV) during recent outbreaks in Bangladesh, utilising a molecular‐based approach. A total of 14 liver samples were collected from the suspected layer flocks in Bangladesh. The diagnosis of ALV infection in chickens was confirmed through necropsy, histopathological examinations, reverse transcription‐polymerase chain reaction (RT‐PCR), and sequence analysis. Gross observations revealed severe liver enlargement with scattered white nodules on the surface in the infected chickens. Histopathological observations showed the infiltration of huge mononuclear inflammatory cells in the periportal area of liver and microvesicular fatty degeneration and necrosis of some hepatocytes. RT‐PCR results identified three samples positive for the env gene of ALV. Sequence analysis of the env genes demonstrated high homology among the identified strains (97%–98%) and with reference strains (92%–96%) at the nucleotide level. The phylogenetic tree revealed close relatedness of the three identified strains to reference strains from India, USA, and China. Mutational analysis indicated several mutations throughout the envelope glycoprotein of the identified strains. Protein structure analysis showed minor changes in the hydrophobic region of the envelope protein of the identified strains. In conclusion, this study, the first detailed investigation in Bangladesh, contributes to understanding ALV epidemiology, highlights genetic diversity, and emphasises the necessity for further investigations and the implementation of effective control measures in the affected regions. [ABSTRACT FROM AUTHOR]

Hydrogen deuterium‐exchange mass spectrometry (HDX‐MS) is commonly used in the study of protein dynamics and protein interactions. By measuring the isotopic exchange of backbone amide hydrogens in solution, HDX‐MS offers valuable structural insights into challenging biological systems. Traditional HDX‐MS approaches utilize bottom–up (BU) proteomics, in which deuterated proteins are digested before MS analysis. BU‐HDX enables the characterization of proteins with various sizes in simple protein mixtures or complex biological samples such as cell lysates. However, BU methods are inherently limited by the inability to resolve protein sub‐populations arising from different protein conformations, such as those arising from post‐translational modifications (PTMs). Alternatively, top–down (TD) HDX‐MS detects the global deuterium uptake at the intact proteoform level, allowing direct probing of structural changes due to protein–protein interactions, PTMs, or conformational changes. Combining TD‐HDX‐MS with electron‐based fragmentation techniques, such as electron capture dissociation (ECD) and electron transfer dissociation (ETD), has demonstrated the feasibility of studying intact protein interactions with amino acid‐level resolution. Here, we present a brief overview of methodologies, limitations, and applications of TD‐HDX‐MS using direct infusion techniques and LC‐based approaches. Furthermore, we conclude with a perspective on the future directions for TD‐HDX‐MS. [ABSTRACT FROM AUTHOR]

Protein structure, including various post‐translational modifications and higher‐order structures, regulates diverse biological functions. Native mass spectrometry (native MS) is a powerful analytical technique used to determine the masses of biomolecules, such as proteins and their complexes, while preserving their native folding in solution. This method provides structural information on the composition of monomers or complexes and the stoichiometry of subunits within each complex, significantly contributing to protein structural analysis. Native MS has evolved to incorporate top–down approaches, enabling the characterization of proteoforms and non‐covalent interactions between metabolites or proteins and specific targets. This perspective highlights the advancements in native MS for intracellular proteins and protein complexes, and discusses future research directions toward cellular biology. [ABSTRACT FROM AUTHOR]

Background and Objectives: Missense variants in exon 7 of the ABO gene can lead to the formation of cisAB alleles. These alleles encode glycosyltransferases (GTs) capable of synthesizing both A and B antigens. In this study, we report the discovery of a novel cisAB allele and characterize it at molecular, protein and serological levels. Materials and Methods: Blood and DNA samples from the proband and seven relatives were examined using standard and modified ABO phenotyping, polymerase chain reaction‐restriction fragment length polymorphism and ABO gene sequencing. We assessed the impact of the p.Leu324Ser variant on the protein structure of the mutant GT using bioinformatics tools. Results: Molecular tests revealed a c.971T>C (p.Leu324Ser) variant in the ABO gene in five of the eight individuals. This variant results in a GT that produces more A antigens and fewer B antigens. Bioinformatics analysis suggests that the amino acid substitution (p.Leu324Ser) could potentially affect enzymatic activity and specificity of the GT. Conclusion: We identified a novel cisAB allele resulting from a c.971T>C variant in the ABO gene. This variant led to the expression of an ABweak phenotype. [ABSTRACT FROM AUTHOR]

Hydrogen bonds (H-bonds) are the most essential non-covalent interactions in nature, playing a crucial role in stabilizing the secondary structures of proteins. Taking inspiration from nature, researchers have developed several multiple H-bonds crosslinked supramolecular polymer materials through the incorporation of H-bond side-chain units into the polymer chains. N-acryloyl glycinamide (NAGA) is a monomer with dual amides in the side group, which facilitates the formation of multiple dense intermolecular H-bonds within poly(N-acryloyl glycinamide) (PNAGA), thereby exhibiting diverse properties dependent on concentration and meeting various requirements across different applications. Moreover, numerous attempts have been undertaken to synthesize diverse NAGA-derived units through meticulous chemical structure regulation and fabricate corresponding H-bonding crosslinked supramolecular polymer materials. Despite this, the systematic clarification of the impact of chemical structures of side moieties on intermolecular associations and material performances remains lacking. The present review will focus on the design principle for synthesizing NAGA-derived H-bond side-chain units and provide an overview of the recent advancements in multiple H-bonds crosslinked PNAGA-derived supramolecular polymer materials, which can be categorized into three groups based on the chemical structure of H-bonds units: (1) monomers with solely cooperative H-bonds; (2) monomers with synergistic H-bonds and other physical interactions; and (3) diol chain extenders with cooperative H-bonds. The significance of subtle structural variations in these NAGA-derived units, enabling the fabrication of hydrogen-bonded supramolecular polymer materials with significantly diverse performances, will be emphasized. Moreover, the extensive applications of multiple H-bonds crosslinked supramolecular polymer materials will be elucidated. [ABSTRACT FROM AUTHOR]

Lectin extracted from Amaranthus hypochondriacus was purified using an affinity column with an agarose-fetuin matrix specific to the lectin of interest. Purification was confirmed by SDS-PAGE, revealing a single protein band with a molecular mass of 34.4 kDa. A hemagglutination assay showed that the lectin had a higher affinity for human type A erythrocytes, and its hemagglutinating activity was inhibited only by fetuin, not by mono-, di-, or trisaccharides. This demonstrated the lectin's selectivity for the N-acetylgalactosamine present on the surface of type A erythrocytes and fetuin. Amaranth lectin exhibited antioxidant activity, which was attributed to the phenolic compounds, amino acids, and specific peptides within the protein structure that are known for their antioxidant properties. Infrared (IR) spectroscopy provided a structural analysis and confirmed lectin glycosylation, a crucial factor in its stability and its ability to bind specific glycans on cell surfaces. Cu2+, Mn2+, and Zn2+ ions were found in the lectin, and these ions were strongly bound to the protein, as dialysis against ethylenediaminetetraacetic acid (EDTA) did not remove them. pH and temperature influenced lectin stability, with higher hemagglutinating activity observed at pH 7, and it remained thermostable at 25 °C. [ABSTRACT FROM AUTHOR]

For protein crystals in which more than two thirds of the volume is occupied by solvent, the featureless nature of the solvent region often generates a constraint that is powerful enough to allow direct phasing of X‐ray diffraction data. Practical implementation relies on the use of iterative projection algorithms with good global convergence properties to solve the difficult nonconvex phase‐retrieval problem. In this paper, some aspects of phase retrieval using iterative projection algorithms are systematically explored, where the diffraction data and density‐value distributions in the protein and solvent regions provide the sole constraints. The analysis is based on the addition of random error to the phases of previously determined protein crystal structures, followed by evaluation of the ability to recover the correct phase set as the distance from the solution increases. The properties of the difference‐map (DM), relaxed–reflect–reflect (RRR) and relaxed averaged alternating reflectors (RAAR) algorithms are compared. All of these algorithms prove to be effective for crystallographic phase retrieval, and the useful ranges of the adjustable parameter which controls their behavior are established. When these algorithms converge to the solution, the algorithm trajectory becomes stationary; however, the density function continues to fluctuate significantly around its mean position. It is shown that averaging over the algorithm trajectory in the stationary region, following convergence, improves the density estimate, with this procedure outperforming previous approaches for phase or density refinement. [ABSTRACT FROM AUTHOR]

Ustilago crameri is a pathogenic basidiomycete fungus that causes foxtail millet kernel smut (FMKS), a devastating grain disease in most foxtail millet growing regions of the world. Carbohydrate-Binding Modules (CBMs) are one of the important families of carbohydrate-active enzymes (CAZymes) in fungi and play a crucial role in fungal growth and development, as well as in pathogen infection. However, there is little information about the CBM family in U. crameri. Here, 11 CBM members were identified based on complete sequence analysis and functional annotation of the genome of U. crameri. According to phylogenetic analysis, they were divided into six groups. Gene structure and sequence composition analysis showed that these 11 UcCBM genes exhibit differences in gene structure and protein motifs. Furthermore, several cis-regulatory elements involved in plant hormones were detected in the promoter regions of these UcCBM genes. Gene ontology (GO) enrichment and protein–protein interaction (PPI) analysis showed that UcCBM proteins were involved in carbohydrate metabolism, and multiple partner protein interactions with UcCBM were also detected. The expression of UcCBM genes during U. crameri infection is further clarified, and the results indicate that several UcCBM genes were induced by U. crameri infection. These results provide valuable information for elucidating the features of U. crameri CBMs' family proteins and lay a crucial foundation for further research into their roles in interactions between U. crameri and foxtail millet. [ABSTRACT FROM AUTHOR]

Laccase is an enzyme that plays an important role in fungi, including lignin degradation, stress defense, and formation of fruiting bodies. Auricularia auricula-judae is a white-rot fungus in the Basidiomycota phylum, capable of delignifying wood. In this study, seven genes belonging to the laccase family were identified through de novo sequencing, containing Cu-Oxidase, Cu-Oxidase_2, and Cu-Oxidase_3 domains. Subsequently, the physical characteristics, phylogenetic relationships, protein secondary structure, and tertiary structure of the laccase family (AaLac1–AaLac7) were analyzed. Prediction of N-glycosylation sites identified 2 to 10 sites in the laccase family, with AaLac7 having the highest number of sites at 10. Sequence alignment and analysis of the laccase family showed high consistency in signature sequences. Phylogenetic analysis confirmed the relationship among laccases within the family, with AaLac3–AaLac4 and AaLac5–AaLac6 being closely positioned on the tree, exhibiting high similarity in tertiary structure predictions. This study identified and analyzed laccase family genes in Auricularia auricula-judae using de novo sequencing, offering a simple method for identifying and analyzing the laccase family in organisms with unknown genetic information. [ABSTRACT FROM AUTHOR]

Proteins are important factors affecting the properties of natural rubber. Therefore, investigating the effect of free and bonded proteins on the structure and mechanical properties of the vulcanized crosslinking network of natural rubber would provide a theoretical basis for the production of high mechanical resistance natural rubber. Herein, natural rubbers with different protein contents and types were prepared by high-speed centrifugation. And, the effects on their network structure, vulcanization, tensile strength, tearing strength and dynamic mechanical properties were investigated. The results showed that the reduction in protein content led to the decrement in the entanglement networks, crosslinking density and tensile and tear strengths of the vulcanized natural rubber. Moreover, the bonded proteins had an obvious influence on the vulcanization process, while free proteins played an important role in the crosslink densities. These results reveal that both bonded and free proteins are involved in the vulcanization process and the construction of the vulcanized crosslinking network structure of natural rubber, which enhances the mechanical properties such as the modulus and tensile strength of vulcanized natural rubber. [ABSTRACT FROM AUTHOR]

Experimental protein energy maps in the (φ, ψ) space for the dipeptides of 20 naturally occurring amino acids using the current collections of high-resolution entries in the protein data bank (PDB) are presented here. Data sets were generated for hydrogen bond distance cut-off values of 2.7 Å and 3.1 Å. Neighborhood effects of proline residues on the (φ, ψ) maps have been examined. The impact of disulphide bridges on these maps has been critically examined. The comparisons of experimental maps with those obtained using various molecular mechanics and molecular dynamics methods were published earlier. The comparison metrics are: (i) locations of global and secondary minima, (ii) percentage areas enclosed by isoenergy contours, (iii) energybased RMSD and (iv) barriers to conformational transitions. The experimental maps for individual amino acid dipeptide motifs show a higher degree of qualitative consistency with theoretical maps derived using molecular mechanics when compared to those from molecular dynamics methods. We also demonstrate that a majority of backbone conformations observed in the structures of small peptides in the Cambridge Crystallographic Database are within the allowed regions of the experimental Ramachandran maps. A few protein models obtained from NMR spectroscopy were evaluated in terms of the amino acid outlier energies using the experimental maps. [ABSTRACT FROM AUTHOR]

This study investigates the physicochemical and functional properties of goat milk whey with heat and non‐heat treatments (pasteurisation, high‐temperature heating, ultrasonic treatment and microwave heating) and lactose hydrolysis. The optimal lactose hydrolysis conditions were obtained through orthogonal test. The results revealed the lactose and glucose in low‐lactose goat milk whey remained largely unchanged through all four treatments, while high‐temperature and microwave treatments induced protein structure alteration. The foaming and emulsifying properties of low‐lactose whey powder were enhanced by heating and ultrasonic treatments, although foam stability decreased. Our findings provide valuable insights into exploiting more nutritional goat milk products. [ABSTRACT FROM AUTHOR]

The paper presents in silico mutational research on the energetics of the most resolved protein structure of HIV-1 NNRTIs, the 4G1Q HIV-1 reverse transcriptase protein. Twenty nearby residues that are less than 6 Å from the center of the embedded ligand are subjected to in silico alterations. For the current study, 380 unique proteins are generated in silico using a set of 380 surrounding residues that have been altered. Analyses and comparison with the parent protein have been done to determine the impact of mutation on the change in folding-unfolding free energy (G), protein stability, and solvation energy. To evaluate the impact of mutation (i) by and (ii) on a particular amino acid residue, a two-fold investigation is conducted. The findings imply that in 12 designed proteins (G − 3.0), folding-unfolding is significantly favored, resulting in the creation of a highly stable conformation. The 11 designed proteins are unstable because the positive values of G > 0.5 in these proteins point to unfavorable mutations. However, the G value in 171 designed proteins is − 1.0, which suggests that mutations cause designed proteins to adopt a stable conformation. According to the findings, out of the 380 created proteins, 11 had extremely unfavorable, 69 less-favorable, and 270 favorable folding-unfolding changes. [ABSTRACT FROM AUTHOR]

Being a core tool, graph theory, as a mathematical formalism in mathematical chemistry, has become an essential approach for studying the complex behavior and interactions in protein systems. Here, we review recent advances in the field, particularly in our group, including the methods developed to access protein functions and their applications in disease biology. First, we provide the necessary background and definitions of graph-based structures and network centralities, and methodologies developed at the node-, subgraph- and pathway-levels. We then review the applications of how to use these algorithms to gain new biological insights, ranging from protein structures to protein dynamics, and interactions for linking genotypes and phenotypes. Furthermore, we discuss immediate challenges in the multilayer network, which is more realistic in the biological world, and hope to draw increasing attention from mathematicians, especially graph theorists, to reveal the basic principles of "networks of networks". [ABSTRACT FROM AUTHOR]