Cysteines in ß-lactoglobulin affects its interfacial adsorption and protein film stabilization.

HYPOTHESIS: Disulfide bonds in proteins are strong chemical bonds forming the secondary and tertiary structure like in the dairy protein ß-lactoglobulin. We hypothesize that the partial or complete removal of disulfide bonds affects the structural rearrangement of proteins caused by intra- and intermolecular interactions that in turn define the interfacial activity of proteins at oil/water interfaces. The experimental and numerical investigations contribute to the mechanistic understanding of the structure-function relationship, especially for the interfacial adsorption behavior of proteins. EXPERIMENTAL AND NUMERICAL: Systematically, the 5 cysteines of ß-lactoglobulin were recombinantly exchanged by alanine. First, the protein structure of the variants in bulk was analyzed with Fourier-transform-infrared-spectroscopy and molecular dynamic simulations. Second, the structural changes after adsorption to the interface have been also analyzed by molecular dynamic simulations. The adsorption behavior was investigated by pendant drop analysis and the interfacial film properties by dilatational rheology. FINDINGS: The structural flexibility of ß-lactoglobulin with no cysteines encourages its unfolding at the interface, and accelerates the interfacial protein film formation that results in more visco-elastic films in comparison to the reference.

The structural biology and dynamics of malate dehydrogenases.

Malate dehydrogenase (MDH) enzymes catalyze the reversible oxidoreduction of malate to oxaloacetate using NAD(P) as a cofactor. This reaction is vital for metabolism and the exchange of reducing equivalents between cellular compartments. There are more than 100 structures of MDH in the Protein Data Bank, representing species from archaea, bacteria, and eukaryotes. This conserved family of enzymes shares a common nucleotide-binding domain, substrate-binding domain, and subunits associate to form a dimeric or a tetrameric enzyme. Despite the variety of crystallization conditions and ligands in the experimental structures, the conformation and configuration of MDH are similar. The quaternary structure and active site dynamics account for most conformational differences in the experimental MDH structures. Oligomerization appears essential for activity despite each subunit having a structurally independent active site. There are two dynamic regions within the active site that influence substrate binding and possibly catalysis, with one of these regions adjoining the subunit interface. In this review, we introduce the reader to the general structural framework of MDH highlighting the conservation of certain features and pointing out unique differences that regulate MDH enzyme activity.

Analysis of Amyloid Fibrillation of Two Family 1 Glycoside Hydrolases.

The formation and analysis of amyloid fibers by two ß-glucosidases, BglA and BglB, belonging to the GH1 enzyme family, are reported. Both proteins have the (ß/α)8 TIM-barrel fold, which is characteristic of this family and is also the most common protein structure. BglA is an octamer, whereas BglB is a monomer. Amyloid fibrillation using pH and temperature as perturbing agents was investigated using fluorescence spectroscopy as a preliminary approach and corroborated using wide-field optical microscopy, confocal microscopy, and field-emission scanning electron microscopy. These analyses showed that both enzymes fibrillate at a wide range of acidic and alkaline conditions and at several temperature conditions, particularly at acidic pH (3-4) and at temperatures between 45 and 65 °C. Circular dichroism spectroscopy corroborated the transition from an α-helix to a ß-sheet secondary structure of both proteins in conditions where fibrillation was observed. Overall, our results suggest that fibrillation is a rather common phenomenon caused by protein misfolding, driven by a transition from an α-helix to a ß-sheet secondary structure, that many proteins can undergo if subjected to conditions that disturb their native conformation.

AlphaFold two years on: Validation and impact.

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.

Identification and expression analysis of Phosphate Transporter 1 (PHT1) genes in the highly phosphorus-use-efficient Hakea prostrata (Proteaceae).

Heavy and costly use of phosphorus (P) fertiliser is often needed to achieve high crop yields, but only a small amount of applied P fertiliser is available to most crop plants. Hakea prostrata (Proteaceae) is endemic to the P-impoverished landscape of southwest Australia and has several P-saving traits. We identified 16 members of the Phosphate Transporter 1 (PHT1) gene family (HpPHT1;1-HpPHT1;12d) in a long-read genome assembly of H. prostrata. Based on phylogenetics, sequence structure and expression patterns, we classified HpPHT1;1 as potentially involved in Pi uptake from soil and HpPHT1;8 and HpPHT1;9 as potentially involved in Pi uptake and root-to-shoot translocation. Three genes, HpPHT1;4, HpPHT1;6 and HpPHT1;8, lacked regulatory PHR1-binding sites (P1BS) in the promoter regions. Available expression data for HpPHT1;6 and HpPHT1;8 indicated they are not responsive to changes in P supply, potentially contributing to the high P sensitivity of H. prostrata. We also discovered a Proteaceae-specific clade of closely-spaced PHT1 genes that lacked conserved genetic architecture among genera, indicating an evolutionary hot spot within the genome. Overall, the genome assembly of H. prostrata provides a much-needed foundation for understanding the genetic mechanisms of novel adaptations to low P soils in southwest Australian plants.

Structure of POU2AF1 recombinant protein and it affects the progression and treatment of liver cancer based on WGCNA and molecular docking analysis.

Hepatocellular carcinoma, also referred to as HCC, is the most frequent form of primary liver cancer. It is anticipated that the discovery of the molecular pathways related with HCC would open up new possibilities for the treatment of HCC.WGCNA (Weighted gene co-expression network analysis) and molecular docking analysis were used to study the structural characteristics of POU2AF1 recombinant protein and its interaction with related proteins. Normal samples were placed in one group, and tumor samples were placed in another group inside the GEO database. We continued our investigation of the DEGs by performing an enrichment analysis using GO and KEGG. The GSCA platform is utilized in the process of doing an analysis of the connection between gene expression and medication sensitivity. In the end, the core target and the active molecule were both given the green light for a molecular docking investigation. POU2AF1 is being considered as a possible therapeutic target for HCC, and the results of our work have presented novel concepts for the treatment of HCC.

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics.

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

Progress in the application of artificial intelligence in molecular generation models based on protein structure.

The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyzing a series of novel molecular generation models predicated on protein structures. Initially, we categorize the molecular generation models based on protein structures and highlight the architectural frameworks utilized in these models. Subsequently, we detail the design and implementation of protein structure-based molecular generation models by introducing different specific examples. Lastly, we outline the current opportunities and challenges encountered in this field, intending to offer guidance and a referential framework for developing and studying new models in related fields in the future.

S. aureus Eap is a polyvalent inhibitor of neutrophil serine proteases.

Staphylococcus aureus expresses three high-affinity neutrophil serine protease (NSP) inhibitors known as the extracellular adherence protein domain (EAPs) proteins. Whereas EapH1 and EapH2 are comprised of a single EAP domain, the modular extracellular adherence protein (Eap) from S. aureus strain Mu50 consists of four EAP domains. We recently reported that EapH2 can simultaneously bind and inhibit cathepsin-G (CG) and neutrophil elastase (NE), which are the two most abundant NSPs. This unusual property of EapH2 arises from independent CG and NE-binding sites that lie on opposing faces of its EAP domain. Here we used X-ray crystallography and enzyme assays to show that all four individual domains of Eap (i.e. Eap1, Eap2, Eap3, and Eap4) exhibit an EapH2-like ability to form ternary complexes with CG and NE that inhibit both enzymes simultaneously. We found that Eap1, Eap2, and Eap3 have similar functional profiles insofar as NSP inhibition is concerned, but that Eap4 displays an unexpected ability to inhibit two NE enzymes simultaneously. Using X-ray crystallography, we determined that this second NE-binding site in Eap4 arises through the same region of its EAP domain that also comprises its CG-binding site. Interestingly, small angle X-ray scattering data showed that stable tail-to-tail dimers of the NE/Eap4/NE ternary complex exist in solution. This arrangement is compatible with NSP-binding at all available sites in a two-domain fragment of Eap. Together, our work implies that Eap is a polyvalent inhibitor of NSPs. It also raises the possibility that higher-order structures of NSP-bound Eap may have unique functional properties.

Exploring domain architectures of human glycosyltransferases: Highlighting the functional diversity of non-catalytic add-on domains.

Human glycosyltransferases (GTs) play crucial roles in glycan biosynthesis, exhibiting diverse domain architectures. This study explores the functional diversity of "add-on" domains within human GTs, using data from the AlphaFold Protein Structure Database. Among 215 annotated human GTs, 74 contain one or more add-on domains in addition to their catalytic domain. These domains include lectin folds, fibronectin type III, and thioredoxin-like domains and contribute to substrate specificity, oligomerization, and consequent enzymatic activity. Notably, certain GTs possess dual enzymatic functions due to catalytic add-on domains. The analysis highlights the importance of add-on domains in enzyme functionality and disease implications, such as congenital disorders of glycosylation. This comprehensive overview enhances our understanding of GT domain organization, providing insights into glycosylation mechanisms and potential therapeutic targets.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease.

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

Toward Diverse Plant Proteins for Food Innovation.

This review highlights the development of plant proteins from a wide variety of sources, as most of the research and development efforts to date have been limited to a few sources including soy, chickpea, wheat, and pea. The native structure of plant proteins during production and their impact on food colloids including emulsions, foams, and gels are considered in relation to their fundamental properties, while highlighting the recent developments in the production and processing technologies with regard to their impacts on the molecular properties and aggregation of the proteins. The ability to quantify structural, morphological, and rheological properties can provide a better understanding of the roles of plant proteins in food systems. The applications of plant proteins as dairy and meat alternatives are discussed from the perspective of food structure formation. Future directions on the processing of plant proteins and potential applications are outlined to encourage the generation of more diverse plant-based products.

A new type of Class C ß-lactamases defined by PIB-1. A metal-dependent carbapenem-hydrolyzing ß-lactamase, from Pseudomonas aeruginosa: Structural and functional analysis.

Antibiotic resistance is one of most important health concerns nowadays, and ß-lactamases are the most important resistance determinants. These enzymes, based on their structural and functional characteristics, are grouped in four categories (A, B, C and D). We have solved the structure of PIB-1, a Pseudomonas aeruginosa chromosomally-encoded ß-lactamase, in its apo form and in complex with meropenem and zinc. These crystal structures show that it belongs to the Class C ß-lactamase group, although it shows notable differences, especially in the Ω- and P2-loops, which are important for the enzymatic activity. Functional analysis showed that PIB-1 is able to degrade carbapenems but not cephalosporins, the typical substrate of Class C ß-lactamases, and that its catalytic activity increases in the presence of metal ions, especially zinc. They do not bind to the active-site but they induce the formation of trimers that show an increased capacity for the degradation of the antibiotics, suggesting that this oligomer is more active than the other oligomeric species. While PIB-1 is structurally a Class C ß-lactamase, the low sequence conservation, substrate profile and its metal-dependence, prompts us to position this enzyme as the founder of a new group inside the Class C ß-lactamases. Consequently, its diversity might be wider than expected.

The conformational modification of myofibrillar protein by magnetic field improves its emulsification properties.

This study investigated the effect of different magnetic field treatments (0, 3, 6, 9, 12 mT) on the structure and emulsification properties of myofibrillar protein (MP). The results showed that the emulsion stabilized by MP with 3, 6, 9 mT magnetic field treatments possessed higher emulsifying ability, storage stability and apparent viscosity, since magnetic field induced the structural unfolding of MP and exposed the hydrophobic groups (the surface hydrophobic increased from 30.10 to 43.73 µg). Meanwhile, the magnetic field treatments decreased the MP particle size from 1752.00 to 1278.67 nm, which was favorable for the diffusion and adsorption of proteins at the oil-water interface, thus improving the MP emulsification ability and stability. Furthermore, the 9 mT magnetic field-treated MP had the best ability to emulsify oil droplets with a more uniform and smaller emulsion size from 28.593 to 23.443 µm. However, high-intensity magnetic field treatment (12 mT) caused MP particles to aggregate and the hydrophobic binding sites to be buried, which was not conducive to encapsulating oil droplets.

Are Viruses Taxonomic Units? A Protein Domain and Loop-Centric Phylogenomic Assessment.

Virus taxonomy uses a Linnaean-like subsumption hierarchy to classify viruses into taxonomic units at species and higher rank levels. Virus species are considered monophyletic groups of mobile genetic elements (MGEs) often delimited by the phylogenetic analysis of aligned genomic or metagenomic sequences. Taxonomic units are assumed to be independent organizational, functional and evolutionary units that follow a 'natural history' rationale. Here, I use phylogenomic and other arguments to show that viruses are not self-standing genetically-driven systems acting as evolutionary units. Instead, they are crucial components of holobionts, which are units of biological organization that dynamically integrate the genetics, epigenetic, physiological and functional properties of their co-evolving members. Remarkably, phylogenomic analyses show that viruses share protein domains and loops with cells throughout history via massive processes of reticulate evolution, helping spread evolutionary innovations across a wider taxonomic spectrum. Thus, viruses are not merely MGEs or microbes. Instead, their genomes and proteomes conduct cellularly integrated processes akin to those cataloged by the GO Consortium. This prompts the generation of compositional hierarchies that replace the 'is-a-kind-of' by a 'is-a-part-of' logic to better describe the mereology of integrated cellular and viral makeup. My analysis demands a new paradigm that integrates virus taxonomy into a modern evolutionarily centered taxonomy of organisms.

Effects of sodium tripolyphosphate on the quality and digestion properties of PSE pork.

This study aimed to examine the impact of sodium tripolyphosphate (STPP) on the quality and digestive characteristics of PSE pork. The results showed a notable decrease in cooking loss of PSE pork from 29.11% to 25.67% with increasing STPP concentration (P < 0.05). Additionally, the gastric digestibility of PSE pork decreased significantly from 52.01% to 45.81% (P < 0.05). The particle size of digesta decreased significantly after gastrointestinal digestion (P < 0.05). These changes were primarily due to the enhanced cross-linking of proteins through ionic interactions, hydrogen bonds and hydrophobic interactions, and resulted in the embedding of hydrophobic groups and endogenous fluorophores. Furthermore, denser network was formed. These findings give a new insight into considering the impact of STPP on meat nutrition when used to enhance texture and water holding capacity.

Effect of supercritical carbon dioxide on protein structure modification and antimicrobial peptides production of Mongolian cheese and its in vitro digestion.

The aim of this research was to investigate the effects of supercritical carbon dioxide (SC-CO2) treatment on protein structure in Mongolian cheese. The peptides during the digestive process of the SC-CO2 treated cheese were also studied. SC-CO2 technology was utilized to treat Mongolian cheese at three temperatures (45, 55 and 65 °C) and three pressures (7.5, 12.5 and 17.5 MPa). The results of fluorescence, ultraviolet-visible, Fourier transform infrared spectroscopy and free sulfhydryl groups showed that SC-CO2, particularly at 65 °C and 17.5 MPa, modified the protein structure in Mongolian cheese effectively. The data of LC-MS/MS-based peptidomics showed that the content of antimicrobial peptides found in the SC-CO2 treated Mongolian cheese was 1.55 times that of the untreated Mongolian cheese; the content of unique antimicrobial peptides in the digested SC-CO2 treated Mongolian cheese was 1.46 times that of the digested untreated Mongolian cheese, which proved that SC-CO2 could help produce antimicrobial peptides in cheese not only during the process of SC-CO2 treatment but during subsequent simulated gastrointestinal digestion as well. In conclusion, SC-CO2 could be considered a promising method to develop cheese products with potential health benefits.

Sulfotransferase 4A1 Coding Sequence and Protein Structure Are Highly Conserved in Vertebrates.

Cytosolic sulfotransferases (SULTs) are Phase 2 drug-metabolizing enzymes that catalyze the conjugation of sulfonate to endogenous and xenobiotic compounds, increasing their hydrophilicity and excretion from cells. To date, 13 human SULTs have been identified and classified into five families. SULT4A1 mRNA encodes two variants: (1) the wild type, encoding a 284 amino acid, ~33 kDa protein, and (2) an alternative spliced variant resulting from a 126 bp insert between exon 6 and 7, which introduces a premature stop codon that enhances nonsense-mediated decay. SULT4A1 is classified as an SULT based on sequence and structural similarities, including PAPS-domains, active-site His, and the dimerization domain; however, the catalytic pocket lid 'Loop 3' size is not conserved. SULT4A1 is uniquely expressed in the brain and localized in the cytosol and mitochondria. SULT4A1 is highly conserved, with rare intronic polymorphisms that have no outward manifestations. However, the SULT4A1 haplotype is correlated with Phelan-McDermid syndrome and schizophrenia. SULT4A1 knockdown revealed potential SULT4A1 functions in photoreceptor signaling and knockout mice display hampered neuronal development and behavior. Mouse and yeast models revealed that SULT4A1 protects the mitochondria from endogenously and exogenously induced oxidative stress and stimulates cell division, promoting dendritic spines' formation and synaptic transmission. To date, no physiological enzymatic activity has been associated with SULT4A1.