Specific Amino Acid Research Articles

Engineering GID4 for use as an N-terminal proline binder via directed evolution.

Nucleic acid sequencing technologies have gone through extraordinary advancements in the past several decades, significantly increasing throughput while reducing cost. To create similar advancement in proteomics, numerous approaches are being investigated to advance protein sequencing. One of the promising approaches uses N-terminal amino acid binders (NAABs), also referred to as recognizers, that selectively can identify amino acids at the N-terminus of a peptide. However, there are only a few engineered NAABs currently available that bind to specific amino acids and meet the requirements of a biotechnology reagent. Therefore, additional NAABs need to be identified and engineered to enable confident identification and, ultimately, de novo protein sequencing. To fill this gap, a human protein GID4 was engineered to create a NAAB for N-terminal proline (Nt-Pro). While native GID4 binds Nt-Pro, its binding is weak (µmol/L) and greatly influenced by the identity of residues following the Nt-Pro. Through directed evolution, yeast-surface display, and fluorescence-activated cell sorting, we identified sequence variants of GID4 with increased binding response to Nt-Pro. Moreover, variants with an A252V mutation showed a reduced influence from residues in the second and third positions of the target peptide when binding to Nt-Pro. The workflow outlined here is shown to be a viable strategy for engineering NAABs, even when starting from native Nt-binding proteins whose binding is strongly impacted by the identity of residues following Nt-amino acid.

Sequence diversity in the monooxygenases involved in oxime production in plant defense and signaling: a conservative revision in the nomenclature of the highly complex CYP79 family.

Cytochrome P450 monooxygenases of the CYP79 family catalyze conversion of specific amino acids into oximes feeding into a variety of metabolic plant pathways. Here we present an extensive phylogenetic tree of the CYP79 family built on carefully curated sequences collected across the entire plant kingdom. Based on a monophyletic origin of the P450s, a set of evolutionarily distinct branches was identified. Founded on the functionally characterized CYP79 sequences, sequence features of the individual substrate recognition sites (SRSs) were analyzed. Co-evolving amino acid residues were identified using co-evolutionary sequence analysis. SRS4 possesses a specific sequence pattern when tyrosine is a substrate. Except for the CYP79Cs and CYP79Fs, substrate preferences toward specific amino acids could not be assigned to specific subfamilies. The highly diversified CYP79 tree, reflecting recurrent independent evolution of CYP79s, may relate to the different roles of oximes in different plant species. The sequence differences across individual CYP79 subfamilies may facilitate the in vivo orchestration of channeled metabolic pathways based on altered surface charge domains of the CYP79 protein. Alternatively, they may serve to optimize dynamic interactions with oxime metabolizing enzymes to enable optimal ecological interactions. The outlined detailed curation of the CYP79 sequences used for building the phylogenetic tree made it appropriate to make a conservative phylogenetic tree-based revision of the naming of the sequences within this highly complex cytochrome P450 family. The same approach may be used in other complex P450 subfamilies. The detailed phylogeny of the CYP79 family will enable further exploration of the evolution of function in these enzymes.

Research Progress on Antioxidant Peptides from Fish By-Products: Purification, Identification, and Structure-Activity Relationship.

Background/Objectives: Excessive reactive oxygen species (ROS) can lead to oxidative stress, which has become an urgent problem requiring effective solutions. Due to the drawbacks of chemically synthesized antioxidants, there is a growing interest in natural antioxidants, particularly antioxidant peptides. Methods: By reviewing recent literature on antioxidant peptides, particularly those extracted from various parts of fish, summarize which fish by-products are more conducive to the extraction of antioxidant peptides and elaborate on their characteristics. Results: This article summarizes recent advancements in extracting antioxidant peptides from fish processing by-products, Briefly introduced the purification and identification process of antioxidant peptides, specifically focusing on the extraction of antioxidant peptides from various fish by-products. Additionally, this article comprehensively reviews the relationship between amino acid residues that compose antioxidant peptides and their potential mechanisms of action. It explores the impact of amino acid types, molecular weight, and structure-activity relationships on antioxidant efficacy. Conclusions: Different amino acid residues can contribute to the antioxidant activity of peptides by scavenging free radicals, chelating metal ions, and modulating enzyme activities. The smaller the molecular weight of the antioxidant peptide, the stronger its antioxidant activity. Additionally, the antioxidant activity of peptides is influenced by specific amino acids located at the C-terminus and N-terminus positions. Simultaneously, this review provides a more systematic analysis and a broader perspective based on existing research, concluded that fish viscera are more favorable for the extraction of antioxidant peptides, providing new insights for the practical application of fish by-products. This could increase the utilization of fish viscera and reduce the environmental pollution caused by their waste, offering valuable references for the study and application of antioxidant peptides from fish by-products.

Isotope Reverse-Labeled Infrared Spectroscopy as a Probe of In-Cell Protein Structure.

While recent years have seen great progress in determining the three-dimensional structure of isolated proteins, monitoring protein structure inside live cells remains extremely difficult. Here, we examine the utility of Fourier transform infrared (FTIR) spectroscopy as a probe of protein structure in live bacterial cells. Selective isotope enrichment is used both to distinguish recombinantly expressed NuG2b protein from the cellular background and to examine the conformation of specific residues in the protein. To maximize labeling flexibility and to improve spectral resolution between label and main-band peaks, we carry out isotope-labeling experiments in "reverse-labeling" mode: cells are initially grown in 13C-enriched media, with specific 12C-labeled amino acids added when protein expression is induced. 1 Because FTIR measurements require only around 20 μL of sample and each measurement takes only a few minutes to complete, isotope-labeling costs are minimal, allowing us to label multiple different residues in parallel in simultaneously grown cultures. For the stable NuG2b protein, isotope difference spectra from live bacterial cultures are nearly identical to spectra from isolated proteins, confirming that the structure of the protein is unperturbed by the cellular environment. By combining such measurements with site-directed mutagenesis, we further demonstrate that the local conformation of individual amino acids can be monitored, allowing us to determine, for example, whether a specific site in the protein contributes to α-helix or β-sheet structures.

Chromosome-level genome of diamondback terrapin provides insight into the genetic basis of salinity adaptation.

Diamondback terrapins (Malaclemys terrapin centrata) exhibit strong environmental adaptability and live in both freshwater and saltwater. However, the genetic basis of this adaptability has not been the focus of research. In this study, we successfully constructed a ∼2.21-Gb chromosome-level genome assembly for M. t. centrata using high-coverage and high-depth genomic sequencing data generated on multiple platforms. The M. t. centrata genome contains 25 chromosomes and the scaffold N50 of ∼143.75 Mb, demonstrating high continuity and accuracy. In total, 53.82% of the genome assembly was composed of repetitive sequences, and 22435 protein-coding genes were predicted. Our phylogenetic analysis indicated that M. t. centrata was closely related to the red-eared slider turtle (Trachemys scripta elegans), with divergence approximately ∼23.6 million years ago (Mya) during the early Neogene period of the Cenozoic era. The population size of M. t. centrata decreased significantly over the past ∼14 Mya during the Cenozoic era. Comparative genomic analysis indicated that 36 gene families related to ion transport were expanded and several genes (AQP3, solute carrier subfamily, and potassium channel genes) underwent specific amino acid site mutations in the M. t. centrata genome. Changes to these ion transport-related genes may have contributed to the remarkable salinity adaptability of diamondback terrapin. The results of this study not only provide a high-quality reference genome for M. t. centrata but also elucidate the possible genetic basis for salinity adaptation in this species.

Quercetin: Potential antidiabetic effects through enzyme inhibition and starch digestibility

AbstractDiabetes mellitus involves high blood sugar levels due to insufficient insulin action. Furthermore, enzymes such as α‐amylase and α‐glucosidase break down carbohydrates into glucose, leading to postprandial hyperglycemia. Flavonoids, particularly quercetin, inhibit these enzymes, slowing carbohydrate digestion and reducing glucose absorption. Quercetin has significant hypoglycemic effects with inhibitory concentration (IC50) values comparable to acarbose, a standard inhibitor, suggesting its potential as a natural alternative for diabetes management. In silico models, including molecular docking, molecular dynamics (MD) simulations, and quantitative structure‐activity relationship (QSAR) approaches, help researchers understand the molecular interactions of therapeutic agents. These techniques identify potential inhibitors, determine enzyme‐inhibitor structures, and calculate binding energies, correlating findings with in vitro or in vivo data. Molecular docking predicts molecular orientations, MD simulations offer insights into enzyme–inhibitor dynamics, and QSAR models predict inhibitory potential based on structural properties. Studies have shown that quercetin effectively inhibits α‐glucosidase and α‐amylase by forming hydrogen bonds with specific amino acid residues. Quercetin interacts with starches and reduces their digestibility, increases the formation of resistant starch, lowers the glycemic index, and inhibits digestive enzymes. Studies show that the effects of quercetin on starch digestion vary with concentration and type of starch, and its incorporation into foods such as bakery products, pasta, etc. can significantly decrease starch hydrolysis. The incorporation of quercetin into starch matrices may aid in the development of functional foods aimed at improving glycemic control.

Evidence for a hydrogen sulfide-sensing E3 ligase in yeast.

In yeast, control of sulfur amino acid metabolism relies upon Met4, a transcription factor that activates the expression of a network of enzymes responsible for the biosynthesis of cysteine and methionine. In times of sulfur abundance, the activity of Met4 is repressed via ubiquitination by the SCFMet30 E3 ubiquitin ligase, but the mechanism by which the F-box protein Met30 senses sulfur status to tune its E3 ligase activity remains unresolved. Herein, we show that Met30 responds to flux through the trans-sulfuration pathway to regulate the MET gene transcriptional program. In particular, Met30 is responsive to the biological gas hydrogen sulfide, which is sufficient to induce ubiquitination of Met4 in vivo. Additionally, we identify important cysteine residues in Met30's WD-40 repeat region that sense the availability of sulfur in the cell. Our findings reveal how SCFMet30 dynamically senses the flow of sulfur metabolites through the trans-sulfuration pathway to regulate the synthesis of these special amino acids.

Bitter peptide prediction using graph neural networks

Bitter taste is an unpleasant taste modality that affects food consumption. Bitter peptides are generated during enzymatic processes that produce functional, bioactive protein hydrolysates or during the aging process of fermented products such as cheese, soybean protein, and wine. Understanding the underlying peptide sequences responsible for bitter taste can pave the way for more efficient identification of these peptides. This paper presents BitterPep-GCN, a feature-agnostic graph convolution network for bitter peptide prediction. The graph-based model learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. BitterPep-GCN was benchmarked using BTP640, a publicly available bitter peptide dataset. The latent peptide embeddings generated by the trained model were used to analyze the activity of sequence motifs responsible for the bitter taste of the peptides. Particularly, we calculated the activity for individual amino acids and dipeptide, tripeptide, and tetrapeptide sequence motifs present in the peptides. Our analyses pinpoint specific amino acids, such as F, G, P, and R, as well as sequence motifs, notably tripeptide and tetrapeptide motifs containing FF, as key bitter signatures in peptides. This work not only provides a new predictor of bitter taste for a more efficient identification of bitter peptides in various food products but also gives a hint into the molecular basis of bitterness.Scientific ContributionOur work provides the first application of Graph Neural Networks for the prediction of peptide bitter taste. The best-developed model, BitterPep-GCN, learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. The embeddings were used to analyze the sequence motifs responsible for the bitter taste.

Statistical analysis of the unique characteristics of secondary structures in proteins

Protein folding is a complex process influenced by the primary sequence of amino acids. Early studies focused on understanding whether the specificity or the conservation of properties of amino acids was crucial for folding into secondary structures such as α-helices, β-sheets, turns, and coils. However, with the advent of artificial intelligence (AI) and machine learning (ML), the emphasis has shifted towards the precise nature and occurrence of specific amino acids. In our study, we analyzed a large set of proteins from diverse organisms to identify unique features of secondary structures, particularly in terms of the distribution of polar, non-polar, and charged amino acid residues. We found that α-helices tend to have a higher proportion of charged and non-polar groups compared to other secondary structures and that the presence of oppositely charged amino acid residues in helices stabilizes them, facilitating the formation of longer helices. These characteristics are distinct to α-helices. This study offers valuable insights for researchers in the field of protein design, enabling the de-novo creation of short helical peptides for a range of applications. We have also developed a web server for extensive analysis of proteins from different databases. The web server is housed at https://proseqanalyser.iitgn.ac.in/

The Change Rate of the Fbxl21 Gene and the Amino Acid Composition of Its Protein Correlate with the Species-Specific Lifespan in Placental Mammals.

This article proposes a methodology for establishing a relationship between the change rate of a given gene (relative to a given taxon) together with the amino acid composition of the proteins encoded by this gene and the traits of the species containing this gene. The methodology is illustrated based on the mammalian genes responsible for regulating the circadian rhythms that underlie a number of human disorders, particularly those associated with aging. The methods used are statistical and bioinformatic ones. A systematic search for orthologues, pseudogenes, and gene losses was performed using our previously developed methods. It is demonstrated that the least conserved Fbxl21 gene in the Euarchontoglires superorder exhibits a statistically significant connection of genomic characteristics (the median of dN/dS for a gene relative to all the other orthologous genes of a taxon, as well as the preference or avoidance of certain amino acids in its protein) with species-specific lifespan and body weight. In contrast, no such connection is observed for Fbxl21 in the Laurasiatheria superorder. This study goes beyond the protein-coding genes, since the accumulation of amino acid substitutions in the course of evolution leads to pseudogenization and even gene loss, although the relationship between the genomic characteristics and the species traits is still preserved. The proposed methodology is illustrated using the examples of circadian rhythm genes and proteins in placental mammals, e.g., longevity is connected with the rate of Fbxl21 gene change, pseudogenization or gene loss, and specific amino acid substitutions (e.g., asparagine at the 19th position of the CRY-binding domain) in the protein encoded by this gene.

Specific amino acid changes correlate with pathogenic flavobacteria.

Flavobacterium is a genus of microorganisms living in a variety of hosts and habitats across the globe. Some species are found in fish organs, and only a few, such as Flavobacteriumpsychrophilum and Flavobacterium columnare, cause severe disease and losses in fish farms. The evolution of flavobacteria that are pathogenic to fish is unknown, and the protein changes accountable for the selection of their colonization to fish have yet to be determined. A phylogenetic tree was constructed with the complete genomic sequences of 208 species of the Flavobacterium genus using 861 softcore genes. This phylogenetic analysis revealed clade CII comprising nine species, including five pathogenic species, and containing the most species that colonize fish. Thirteen specific amino acid changes were found to be conserved across 11 proteins within the CII clade compared with other clades, and these proteins were enriched in functions related to replication, recombination, and repair. Several of these proteins are known to be involved in pathogenicity and fitness adaptation in other bacteria. Some of the observed amino acid changes can be explained by preferential selection for certain codons and tRNA frequency. These results could help explain how species belonging to the CII clade adapt to fish environments.

A multifaceted approach to investigate interactions of thifluzamide with haemoglobin

This study explores the interaction between the pesticide thifluzamide (TF) and haemoglobin (Hb) to understand potential structural changes that might affect Hb's function. Using a combination of UV–visible and fluorescence spectroscopy, circular dichroism (CD), molecular docking, molecular dynamics (MD) simulations, and electrochemical methods, we investigated these interactions in detail. Spectroscopy results indicated the formation of a stable TF-Hb complex, with a binding constant of 6.64 × 105 M−1 at 298 K and a 1:1 binding ratio. The stability of this complex was confirmed by a free energy change (∆G) of −34.491 kJ mol−1. CD spectroscopy was employed to confirm structural changes in Hb due to thifluzamide binding. Molecular docking studies revealed that TF interacts with specific amino acids in Hb, such as ALA, HIS, VAL, LYS, and LEU, with a binding energy of −25.10 kJ mol−1. MD simulations supported these findings by showing conformational changes in Hb upon TF binding, as indicated by RMSD and RMSF analyses. Electrochemical experiments further confirmed the interaction, evidenced by a consistent decrease in the TF peak in the presence of Hb. Overall, our findings shed light on how TF binds to Hb, causing structural changes that could potentially impact its normal function. This research enhances our understanding of the biochemical effects of TF on Hb, which could have significant implications for biological systems.

Innovative synthesis and molecular modeling of actinomycetes-derived silver nanoparticles for biomedical applications

The rising demand for innovative antimicrobial solutions has shifted focus towards silver nanoparticles (AgNPs), especially those produced through eco-friendly methods. This study introduces a novel approach utilizing actinomycetes strains—Streptomyces albus, Micromonospora maris, and Arthrobacter crystallopoietes—to biosynthesize AgNPs with remarkable antibacterial properties. Through molecular characterization, we identified unique features of these nanoparticles, and computational modeling suggested significant ion-ligand interactions with proteins 6REV and 3K07. Our research highlights the promise of these biogenically synthesized nanoparticles in advancing biomedical applications. Actinomycetes were sourced and screened for their ability to produce metallic nanoparticles, revealing that among 35 samples, only six showed this capability. Notably, Streptomyces albus strain smmdk14 (OR685674), Micromonospora maris strain smmdk13 (OR685672), and Arthrobacter crystallopoietes strain smmdk12 (OR685674) were identified as effective silver nanoparticle producers. The synthesized nanoparticles demonstrated potent antibacterial activity against common pathogens including E. coli, Pseudomonas aeruginosa, Klebsiella spp., Enterococcus faecalis, Staphylococcus aureus, and Acinetobacter spp. The data obtained from color change observation, UV–visible spectrophotometry, Zeta potential, FTIR spectroscopy, and transmission electron microscopy (TEM) characterized AgNPs potentiality. The nanoparticles were spherical, with sizes ranging from 6.46 nm to 24.7 nm. Optimization of production conditions, comparison of antimicrobial effects with antibiotics, evaluation of potential toxicity, and assessment of wound-healing capabilities were also conducted. The biosynthesized AgNPs exhibited superior antibacterial properties compared to traditional antibiotics and significantly accelerated wound healing by approximately 66.4 % in fibroblast cell cultures. Additionally, computational analysis predicted interactions between various metal ions and specific amino acid residues in proteins 6REV and 3K07. Overall, this study demonstrates the successful creation of AgNPs with notable antibacterial and wound-healing properties, underscoring their potential for medical applications.

A pilot metabolomics study across the continuum of interstitial lung disease fibrosis severity.

Interstitial lung diseases (ILDs) include a variety of inflammatory and fibrotic pulmonary conditions. This study employs high-resolution metabolomics (HRM) to explore plasma metabolites and pathways across ILD phenotypes, including non-fibrotic ILD, idiopathic pulmonary fibrosis (IPF), and non-IPF fibrotic ILD. The study used 80 plasma samples for HRM, and involved linear trend and group-wise analyses of metabolites altered in ILD phenotypes. We utilized limma one-way ANOVA and mummichog algorithms to identify differences in metabolites and pathways across ILD groups. Then, we focused on metabolites within critical pathways, indicated by high pathway overlap sizes and low p-values, for further analysis. Targeted HRM identified putrescine, hydroxyproline, prolyl-hydroxyproline, aspartate, and glutamate with significant linear increases in more fibrotic ILD phenotypes, suggesting their role in ILD fibrogenesis. Untargeted HRM highlighted pathway alterations in lysine, vitamin D3, tyrosine, and urea cycle metabolism, all associated with pulmonary fibrosis. In addition, methylparaben level had a significantly increasing linear trend and was higher in the IPF than fibrotic and non-ILD groups. This study highlights the importance of specific amino acids, metabolic pathways, and xenobiotics in the progression of pulmonary fibrosis.

Biochar-driven fouling mitigation in sustainable microalgal-bacterial membrane bioreactors

Microalgal-bacterial membrane bioreactor (MB-MBR) have emerged as a crucial technology for sustainable wastewater treatment. However, membrane fouling caused by free microalgae remains a major obstacle to their cost-effective operation. This study investigated the impact of biochar addition on membrane fouling in MB-MBR. The findings indicated that biochar significantly reduced membrane fouling, primarily due to the enlargement of floc size and the reduction of soluble microbial products (SMP). Detailed analyses suggested that biochar's adsorptive properties and its effect on decreasing the abundance of algae and bacteria species, such as Proteobacteria and Leptolyngbya, which promote fouling, are key factors. This alteration enhanced specific amino acid metabolic pathways, thereby reducing SMP production and fouling potential. Consequently, adhesion interaction energy of the flocs and membrane fouling were both diminished. This study demonstrated that biochar addition is an effective strategy for mitigating membrane fouling in MB-MBR systems, providing a theoretical foundation for their stable and sustainable operation.

Synthesis, Crystal Structure, DFT Calculations, Bioactivity Study, and Docking Results of Bis‐Hydrazone Based on 1,2,4‐Triazol‐3‐Thione

AbstractThe new, unexpected bioactive bis‐hydrazone derivative (4) was obtained, in 74 % yield, by reacting two molar equivalents of pyrazole‐4‐carbaldehyde (1) with one molar equivalent of 2,5‐dihydrazineyl‐1,3,4‐thiadiazole (2). The compound was comprehensively characterized, including X‐ray single crystal, DFT calculations, and bioactivity assessments. Hirschfeld surface analysis confirmed the presence of hydrogen bonding interactions, particularly N−H⋅⋅⋅N and C−H⋅⋅⋅π interactions, which influence the overall crystal packing. The target bis‐hydrazone exhibited significant antimicrobial activity against multi‐drug‐resistant bacterial strains, with the largest activity against S.typhimurium with an inhibition zone of 17.1±0.6 mm and MIC 31.25 μg/mL. The compound also demonstrated significant cytotoxic effects on cancer cells, with a higher IC50 ratio of 134.43 μg/mL against the normal cell line Wi38 and the lowest IC50 value of 45.88 μg/mL against the cancer cell line Caco2. Molecular docking was carried out with estrogen receptor alpha (ERα) and sodium‐glucose transporter SGLT1, which are relevant to Mcf7 and Caco2 cancer cell lines, respectively. Docking suggests the presence of specific amino acids that may influence the binding affinity between the ligand and receptor active sites through residue overlaps in chains A for SGLT1 and B for Erα, offering the ligand as a promising anticancer consistent with the IC50 outcomes.

Progression to type 1 diabetes in the DPT-1 and TN07 clinical trials is critically associated with specific residues in HLA-DQA1-B1 heterodimers.

The aim of this work was to explore molecular amino acids (AAs) and related structures of HLA-DQA1-DQB1 that underlie its contribution to the progression from stages 1 or 2 to stage 3 type 1 diabetes. Using high-resolution DQA1 and DQB1 genotypes from 1216 participants in the Diabetes Prevention Trial-Type 1 and the Diabetes Prevention Trial, we applied hierarchically organised haplotype association analysis (HOH) to decipher which AAs contributed to the associations of DQ with disease and their structural properties. HOH relied on the Cox regression to quantify the association of DQ with time-to-onset of type 1 diabetes. By numerating all possible DQ heterodimers of α- and β-chains, we showed that the heterodimerisation increases genetic diversity at the cellular level from 43 empirically observed haplotypes to 186 possible heterodimers. Heterodimerisation turned several neutral haplotypes (DQ2.2, DQ2.3 and DQ4.4) to risk haplotypes (DQ2.2/2.3-DQ4.4 and DQ4.4-DQ2.2). HOH uncovered eight AAs on the α-chain (-16α, -13α, -6α, α22, α23, α44, α72, α157) and six AAs on the β-chain (-18β, β9, β13, β26, β57, β135) that contributed to the association of DQ with progression of type 1 diabetes. The specific AAs concerned the signal peptide (minus sign, possible linkage to expression levels), pockets 1, 4 and 9 in the antigen-binding groove of the α1β1 domain, and the putative homodimerisation of the αβ heterodimers. These results unveil the contribution made by DQ to type 1 diabetes progression at individual residues and related protein structures, shedding light on its immunological mechanisms and providing new leads for developing treatment strategies. Clinical trial data and biospecimen samples are available through the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository portal ( https://repository.niddk.nih.gov/studies ).

Exhaustive Search of Dietary Intake Biomarkers as Objective Tools for Personalized Nutrimetabolomics and Precision Nutrition Implementation.

To conduct an exhaustive scoping search of existing literature, incorporating diverse bibliographic sources to elucidate the relationships between metabolite biomarkers in human fluids and dietary intake. The search for biomarkers linked to specific dietary food intake holds immense significance for precision health and nutrition research. Using objective methods to track food consumption through metabolites offers a more accurate way to provide dietary advice and prescriptions on healthy dietary patterns by healthcare professionals. An extensive investigation was conducted on biomarkers associated with the consumption of several food groups and consumption patterns. Evidence is integrated from observational studies, systematic reviews, and meta-analyses to achieve precision nutrition and metabolism personalization. Tailored search strategies were applied across databases and gray literature, yielding 158 primary research articles that met strict inclusion criteria. The collected data underwent rigorous analysis using STATA and Python tools. Biomarker-food associations were categorized into 5 groups: cereals and grains, dairy products, protein-rich foods, plant-based foods, and a miscellaneous group. Specific cutoff points (≥3 or ≥4 bibliographic appearances) were established to identify reliable biomarkers indicative of dietary consumption. Key metabolites in plasma, serum, and urine revealed intake from different food groups. For cereals and grains, 3-(3,5-dihydroxyphenyl) propanoic acid glucuronide and 3,5-dihydroxybenzoic acid were significant. Omega-3 fatty acids and specific amino acids showcased dairy and protein foods consumption. Nuts and seafood were linked to hypaphorine and trimethylamine N-oxide. The miscellaneous group featured compounds like theobromine, 7-methylxanthine, caffeine, quinic acid, paraxanthine, and theophylline associated with coffee intake. Data collected from this research demonstrate potential for incorporating precision nutrition into clinical settings and nutritional advice based on accurate estimation of food intake. By customizing dietary recommendations based on individualized metabolic profiles, this approach could significantly improve personalized food consumption health prescriptions and support integrating multiple nutritional data.This article is part of a Nutrition Reviews special collection on Precision Nutrition.

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequence and intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the prediction of the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low response. The model predicts MS1 response with a mean average percentage error of ∼11 % and a Pearson correlation coefficient of ∼0.64. While dimer representation of peptide sequences did not improve predictive capacity compared to single AA representation in earlier work, this work adds valuable insight for a better understanding of peptide response in MS analysis. SignificanceMass spectrometry is not inherently quantitative, and the response of a compound relies not only on its concentration but also on the molecular composition. For mass spectrometry-based analysis of peptides, such as in bottom-up proteomics, this directly implies that the response cannot be used directly to quantify individual peptides. Moreover, the dependency of the response on the amino acid sequence of individual peptides remains poorly understood. Using a deep learning model based on a recurrent neural network with an attention mechanism, we here investigate how the presence of dimer motifs within a peptide affects the MS1 response through the analysis of intended equimolar peptide pools comprising almost 200,000 unique peptides in total. Not only do we identify certain dimer classes and specific dimers that substantially affect the MS1 response, but the model is also able to predict peptide intensity with low error rates within the independent test subset. The findings not only improve our understanding of the link between sequence and response for peptides but also highlight the potential of utilizing deep learning for developing methods allowing for absolute, label-free peptide quantification.

Comparative analysis of the interaction mechanism of γ-globulin and hemoglobin with spherical and rod-shaped gold nanoparticles

The interaction mechanism of spherical gold nanoparticles (AuNPs) and rod-shaped gold nanoparticles (AuNRs) with γ-globulin and hemoglobin was comprehensively and comparatively analyzed. γ-Globulin and hemoglobin have high affinity with AuNPs, and with two different types of binding sites on AuNRs surface. Except hemoglobin interaction with the first binding site of AuNRs, the interaction between γ-globulin/hemoglobin and AuNPs/AuNRs is the spontaneous, endothermic and entropy-driven process, and hydrophobic interaction plays a dominant role. The molecular adsorption mechanism of γ-globulin/hemoglobin on AuNPs and AuNRs surface conforms to Langmuir model and Freundlich model, respectively. The kinetic molecular mechanism between them conforms to the pseudo-second-order model, and chemisorption is the rate-limiting step. AuNPs result in the loosening and unfolding of γ-globulin backbone. AuNRs have no significant effect on γ-globulin backbone. AuNPs/AuNRs result in no significant changes in hemoglobin structure and heme group microenvironment. AuNPs/AuNRs decrease the hydrophobicity of Trp microenvironment of γ-globulin, but there is an intramolecular energy transfer from Trp residue to Tyr residue of hemoglobin. The β-sheet of γ-globulin and the α-helix of hemoglobin reduce by increasing concentrations of AuNPs/AuNRs. Molecular docking is suggesting that the specific amino acid residues of γ-globulin and hemoglobin interaction with AuNPs/AuNRs, and validates the experimental results.