Protein Sequence Research Articles

Effects of Several Bile Acids on the Production of Virulence Factors by Pseudomonas aeruginosa

The presence of bile acids in the cystic fibrosis patient’s lungs contributes to an increase in the inflammatory response, in the dominance of pathogens, as well as in the decline in lung function, increasing morbidity. The aim of this study is to determine the effects of exposure of Pseudomonas aeruginosa to primary and secondary bile acids on the production of several virulence factors which are involved in its pathogenic power. The presence of bile acids in the bacterial culture medium had no effect on growth up to a concentration of 1 mM. However, a slight decrease in the adhesion index as well as a reduction in the virulence of the bacteria on the HT29 cell line could be observed. In this model, exposure of P. aeruginosa to bile acids showed a significant decrease in the production of LasB and AprA proteases due to the reduction in the expression of their genes. A decrease in pyocyanin production was also observed in relation to the effects of bile acids on the quorum sensing regulators. In order to have an effect on gene expression, it is necessary for bile acids to enter the bacteria. P. aeruginosa harbors two potential homologs of the eukaryotic genes encoding the bile acid transporters NTCP1 and NTCP2 that are expressed in hepatocytes and enterocytes, respectively. By carrying out a comparative BLAST-P between the amino acid sequences of the PAO1 proteins and those of NTCP1 and NTCP2, we identified the products of the PA1650 and PA3264 genes as the unique homologs of the two eukaryotic genes. Exposure of the mutant in the PA1650 gene to chenodeoxycholic acid (CDCA) and lithocholic acid (LCA) showed a less significant effect on pyocyanin production than with the isogenic PAO1 strain. Also, no effect of CDCA on the PA3264 gene mutant was observed. This result indicated that CDCA should enter the bacteria by the transporter produced by this gene. The entry of LCA into bacteria seemed more complex and rather responded to a multifactorial system involving the product of the PA1650 gene but also the products of other genes encoding potential transporters.

Identification of BRCA new prognostic targets and neoantigen candidates from fusion genes

Cancer-associated gene fusions serve as a potential source of highly immunogenic neoantigens. In this study, we identified fusion proteins from fusion genes and extracted fusion peptides to accurately predict Breast cancer (BRCA) neo-antigen candidates by high-throughput artificial intelligence computation. Firstly, Deepsurv was used to evaluate the prognosis of patients, providing a landscape of prognostic fusion genes in BRCA. Next, AGFusion was utilized to generate full-length fusion protein sequences and annotate functional domains. Advanced neural networks and Transformer-based analyses were implemented to predict the binding of fusion peptides to 112 types of HLA, thereby forming a new immunotherapy candidates’ library of BRCA neo-antigens (n = 7791, covering 88.41% of patients). Among them, 15 neo-antigens were validated and factually translated into mass spectrometry data of BRCA patients. Finally, AlphaFold2 was applied to predict the binding sites of these neo-antigens to MHC (HLA) molecules. Notably, we identified a prognostic neoantigen from the TBC1D4–COMMD6 fusion that significantly improves patient prognosis and extensively binds to 16 types of HLA alleles. These highly immunogenic and tumor-specific neoantigens offer emerging targets for personalized cancer immunotherapies and act as prospective predictors for tumor survival prognosis and responses to immune checkpoint therapies.

Sucrose synthase gene family in common bean during pod filling subjected to moisture restriction

In common bean (Phaseolus vulgaris L.), leaf photosynthesis is significantly reduced under drought conditions. Previous studies have shown that some drought-tolerant cultivars use the pod walls to compensate the decreased photosynthesis rate in leaves by acting as temporary reservoirs of carbohydrates to support seed filling. Here, we describe a comprehensive molecular characterization of sucrose synthase (SUS, EC 2.4.1.13) gene family through a genome-wide analysis and evaluated the effects of terminal drought on reproductive structures, specifically the pod walls. Seven PvSUS genes were located on six different chromosomes and had 8–16 intron–exon structures (8–16 exons). The PvSUS protein sequences revealed conserved catalytic domains, with molecular weights ranging from 90.5 kDa to 105.1 kDa and lengths from 799 to 929 amino acids. Phylogenetic analysis grouped these sequences into three main clusters with seven subgroups, indicating divergence from SUS sequences in other plant species. Using a docking sequence, we predicted three-dimensional (3-D) structures and evaluated the active sites. Bioinformatics analysis of promoter regions suggested that PvSUS genes may respond to light, hormone signaling, and stress stimuli. Greenhouse experiments were conducted using the cv. OTI, identified as having intermediate drought tolerance. Plants at the R8 growth stage were maintained with regular irrigation at 100% field capacity (FC) or with water restriction to maintain 50% of field capacity. Pods were harvested 5 days, 10 days, 15 days, and 20 days after anthesis. An increase in PvSUS activity under water restriction was associated with higher levels of fructose, while sucrose concentration also increased. qRT-PCR analysis revealed that PvSUS1, PvSUS3, and PvSUS4 were strongly expressed during seed development under water restriction. The fluorescent sucrose analog esculin indicated that transport across the plasma membrane might contribute to the increase in the pith cell diameter of pedicels. The results provide a systematic overview of the PvSUS gene family in P. vulgaris, offering a framework for further research and the potential functional application of PvSUS genes.

ProtChat: An AI Multi-Agent for Automated Protein Analysis Leveraging GPT-4 and Protein Language Model.

Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use. Researchers have integrated LLMs with PLLMs to develop automated protein analysis tools to address these challenges, simplifying analytical workflows. Existing technologies often require substantial human intervention for specific protein-related tasks, maintaining high barriers to implementing automated protein analysis systems. Here, we propose ProtChat, an AI multiagent system for protein analysis that integrates the inference capabilities of PLLMs with the task-planning abilities of LLMs. ProtChat integrates GPT-4 with multiple PLLMs, like ESM and MASSA, to automate tasks such as protein property prediction and protein-drug interactions without human intervention. This AI agent enables users to input instructions directly, significantly improving efficiency and usability, making it suitable for researchers without a computational background. Experiments demonstrate that ProtChat can automate complex protein tasks accurately, avoiding manual intervention and delivering results rapidly. This advancement opens new research avenues in computational biology and drug discovery. Future applications may extend ProtChat's capabilities to broader biological data analysis. Our code and data are publicly available at github.com/SIAT-code/ProtChat.

Genome-Wide Identification and Evolutionary Analysis of Functional BBM-like Genes in Plant Species

Background/Objectives: BABY BOOM (BBM), a transcription factor from the APETALA2 (AP2) protein family, plays a critical role in somatic embryo induction and apomixis. BBM has now been widely applied to induce apomixis or enhance plant transformation and regeneration efficiency through overexpression or ectopic expression. However, the structural and functional evolutionary history of BBM genes in plants is still not well understood. Methods: The protein sequences of 10 selected plant species were used to locate the branch of BBM-Like by key domain identification and phylogenetic tree construction. The identified BBML genes were used for further conserved motif identification, gene structural analysis, miRNA binding site prediction, cis-acting element prediction, collinear analysis, protein–protein interaction network construction, three-dimensional structure modeling, molecular docking, and expression pattern analysis. Results: A total of 24 BBML proteins were identified from 10 representative plant species. Phylogenetic relationship analysis displayed that BBML proteins from eudicots and monocots were divided into two clusters, with monocots exhibiting a higher number of BBMLs. Gene duplication events indicated that whole genome/segmental duplication were the primary drivers of BBML genes’ evolution in the tested species, with purifying selection playing a key role during evolution processes. Comparative analysis of motif, domains, and gene structures revealed that most BBMLs were highly evolutionarily conserved. The expression patterns of BBML genes revealed significant tissue specificity, particularly in the root and embryo. We also constructed protein–protein interaction networks and molecular docking models to identify functional pathways and key amino acid residues of BBML proteins. The functions of BBMLs may differ between monocots and eudicots, as suggested by the functional enrichment of interacting proteins. Conclusions: Our research delved into the molecular mechanism, evolutionary relationships, functional differentiation, and expression patterns of BBML genes across plants, laying the groundwork for further investigations into the molecular properties and biological roles of BBMLs.

Conformation pattern changes in R1-pS262 tau peptide induced endogenous tau aggregation, synaptic damage, and cognitive impairments.

To date, the effect of tau phosphorylation at different amino acid sites on the conformation and function of tau is still unclear in Alzheimer's disease (AD). Protein fingerprinting, also known as the protein folding shape code (PFSC) method, is a protein structure prediction technique based on protein sequence, which can reveal proteins' most likely spatial conformation. To investigate the effect of phosphorylation on tau protein conformation using PFSC technology and further analyze the differences in the effect of phosphorylation on tau aggregation at specific sites. We performed a conformational analysis of wild-type and simulated mutant hTau441 using the PFSC method and synthesized the phosphorylated and non-phosphorylated tau fragments by the chemical solid phase method. We found that the number of Ser262 protein fingerprints increased from six in tau S262A to nine in tau S262E, together with increased conformational changes and enhanced flexibility. The in vitro Thioflavin S assay showed that phosphorylated tau fragments R1-pS262 possessed a stronger activity of inducing tau aggregation. In contrast to the non-phosphorylated tau fragment R1-nS262, R1-pS262 promoted endogenous tau aggregation and decreased synaptic proteins. In rats, R1-pS262 caused cognitive impairments and neuronal loss in addition to endogenous tau aggregation and synaptic damage. Our study firstly reports that tau phosphorylation at Ser262 induces tau aggregation, and phosphorylated tau fragments R1-pS262 directly result in neuropathological changes. These provide new clues to the pathogenesis of tauopathy, such as AD, and a new molecular target for possible intervention.

Coat-protein Gene Based Identification and Characterization of Yellow Mosaic Virus Infecting Blackgram in Northern Karnataka, India

Aims: Coat-protein (CP) based identification and characterization of yellow mosaic virus infecting blackgram. Place and Duration of Study: The study was carried out during Kharif 2023 at College of Agriculture, Vijayapur. Methodology: The whole genomic DNA was isolated from the leaf tissues of both healthy blackgram plants and plants infected with the yellow mosaic virus by using a modified CTAB method. Specific primers for YMV were tried to amplify coat protein region of blackgram YMV. Results: Sequence analysis revealed that the CP gene of the begomovirus under study shared 99.8 per cent similarity with MYMV Shivamogga isolate (OM106035.1) at the nucleotide level. When comparing the deduced amino acid sequences of individual proteins from YMV infecting blackgram at Vijayapur with those of other begomoviruses, the highest identity was found with the MYMV Shivamogga isolate (OM106035.1), showing 84.59 per cent similarity. A phylogenetic tree constructed based on the full-length coat protein gene sequence of MYMV, along with 37 other Geminivirus sequences, formed two major clusters in which MYMV-Vijayapur isolate appeared in Cluster I. Conclusion: The results of the current study, based on similarities in coat protein sequence at both the nucleotide and amino acid levels, as well as phylogenetic analysis, confirmed the prevalence of MYMV strain rather than MYMIV, HgYMV and DoYMV in blackgram in northern parts of Karnataka.

The role of silent mutations in KRAS-mutant tumors.

Silent mutations within the RASgene have garnered increasing attention for their potential roles in tumorigenesis and therapeutic strategies. Kirsten-RAS (KRAS)mutations, predominantly oncogenic, are pivotal drivers in various cancers. While extensive research has elucidated the molecular mechanisms and biological consequences of active KRASmutations, the functional significance of silent mutations remains relatively understudied. This review synthesizes current knowledge on KRASsilent mutations, highlighting their impact on cancer development. Silent mutations, which do not alter protein sequences but can affect RNA stability and translational efficiency, pose intriguing questions regarding their contribution to tumor biology. Understanding these mutations is crucial for comprehensively unraveling KRAS-driven oncogenesis and exploring novel therapeutic avenues. Moreover, investigations into the clinical implications of silent mutations in KRAS-mutant tumors suggest potential diagnostic and therapeutic strategies. Despite being in early stages, research on KRASsilent mutations holds promise for uncovering novel insights that could inform personalized cancer treatments. In conclusion, this review underscores the evolving landscape of KRASsilent mutations, advocating for further exploration to bridge fundamental biology with clinical applications in oncology.

Designing host-associated microbiomes using the consumer/resource model.

Generative models are extremely popular in modern biology. They have been used to model the variation of protein sequences, entire genomes, and RNA sequencing profiles. Importantly, generative models have been used to extrapolate and interpolate to unobserved regimes of data to design biological systems with desired properties. For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. Host-associated microbiomes play important roles in animal health and disease, as well as the productivity and environmental footprint of livestock species. However, there are no generative models of host-associated microbiomes. One chief reason is that off-the-shelf machine-learning models are data hungry, and microbiome studies usually deal with large variability and small sample sizes. Moreover, microbiome compositions are heavily context dependent, with characteristics of the host and the abiotic environment leading to distinct patterns in host-microbiome associations. Consequently, off-the-shelf generative modeling has not been successfully applied to microbiomes.To address these challenges, we develop a generative model for host-associated microbiomes derived from the consumer/resource (C/R) framework. This derivation allows us to fit the model to readily available cross-sectional microbiome profile data. Using data from three animal hosts, we show that this mechanistic generative model has several salient features: the model identifies a latent space that represents variables that determine the growth and, therefore, relative abundances of microbial species. Probabilistic modeling of variation in this latent space allows us to generate realistic in silico microbial communities. The model can assign probabilities to microbiomes, thereby allowing us to discriminate between dissimilar ecosystems. Importantly, the model predictively captures host-associated microbiomes and the corresponding hosts' phenotypes, enabling the design of microbial communities associated with user-specified host characteristics.

Redesigning error control in cross-linking mass spectrometry enables more robust and sensitive protein-protein interaction studies.

Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering schemes and thorough error control. Here, we benchmark existing data filtering schemes combined with error rate estimation strategies utilizing concatenated target-decoy protein sequence databases. These workflows show shortcomings either in sensitivity (many false negatives) or specificity (many false positives). To ameliorate the limited sensitivity without compromising specificity, we develop an alternative target-decoy search strategy using fused target-decoy databases. Furthermore, we devise a different data filtering scheme that takes the inter-link context of the XL-MS dataset into account. Combining both approaches maintains low error rates and minimizes false negatives, as we show by mathematical simulations, analysis of experimental ground-truth data, and application to various biological datasets. In human cells, inter-link identifications increase by 75% and we confirm their structural accuracy through proteome-wide comparisons to AlphaFold2-derived models. Taken together, target-decoy fusion and context-sensitive data filtering deepen and fine-tune XL-MS-based interactomics.

ProHap enables human proteomic database generation accounting for population diversity.

Amid the advances in genomics, the availability of large reference panels of human haplotypes is key to account for human diversity within and across populations. However, mass spectrometry-based proteomics does not benefit from this information. To address this gap, we introduce ProHap, a Python-based tool that constructs protein sequence databases from phased genotypes of reference panels. ProHap enables researchers to account for haplotype diversity in proteomic searches.

TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics.

Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the past decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.

Genome-wide identification, gene cloning, subcellular location and expression analysis of the OPR gene family under salt stress in sweetpotato

BackgroundThe 12-oxo-phytodienoic acid reductase (OPR) enzyme is crucial for the synthesis of jasmonates (JAs), and is involved in the plant stress response. However, the OPR gene family in sweetpotato, an important horticultural crop, remains unidentified.ResultsIn this study, we employed bioinformatics techniques to identify nine IbOPR genes. Phylogenetic analysis revealed that these genes could be divided into Group I and Group II. Synteny analysis indicated that IbOPR evolution was driven by tandem duplication, whole-genome duplication (WGD), and segmental duplication events. The promoter sequences of IbOPRs were found to be associated with stress and hormonal responses. Additionally, we successfully cloned four IbOPRs from "Haida HD7791" and "Haida HD7798" using homologous cloning technology. These sequences were 1203 bp, 1200 bp, 1134 bp, and 1137 bp in length and encoded 400, 399, 377, and 378 amino acids, respectively. The protein sequence similarity between the salt-tolerant variety "Haida HD7791" and the salt-sensitive variety "Haida HD7798" was determined to be 96.75% for IbOPR2, 99.75% for IbOPR3, 92.06% for IbOPR6, and 98.68% for IbOPR7. Phylogenetic analysis categorized IbOPR2 and IbOPR3 proteins into Group II, while IbOPR6 and IbOPR7 proteins belonged to Group I. Subcellular localization experiments showed IbOPR2 protein present in the peroxisome, while IbOPR3, IbOPR6, and IbOPR7 proteins were found in the cytoplasm and nucleus. Salt stress induction experiments demonstrated that IbOPR2, IbOPR3, and IbOPR7 were significantly upregulated only in 'Haida HD7791' after 6 h. In contrast, IbOPR6 was induced in 'Haida HD7798' at 6 h but inhibited in 'Haida HD7791' at later time points (12, 24, 48, and 72 h), highlighting functional differences in salt stress responses.ConclusionsOur findings suggest that IbOPR2 may play a crucial role in sweetpotato's response to salt stress by participating in JAs synthesis. These results provide a foundation for future functional analyses of OPR genes in sweetpotato.

Effects of energetic compounds on soil microbial communities and functional genes at a typical ammunition demolition site.

High concentrations of energetic compounds such as 2,4,6-trinitrotoluene (TNT), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) in military-contaminated sites pose a serious threat to human health and ecosystems. Better understanding about their effects on microbial diversity and functional genes in soil of ammunition demolition sites is required. In this study, the information of soil microbial community composition was obtained by macrogenome sequencing, and the impacts of energetic compounds on microbial community structure at the level of functional genes and enzymes based on Nr (Non-Redundant Protein Sequence Database), KEGG (Kyoto Encyclopedia of Genes and Genomes), CAZy (Carbohydrate-Active En-zymes Database) and other databases were discussed. The results showed that soil microbial diversity and functional gene abundance decreased significantly with the increase of the concentrations of energetic compounds. Conversely, the relative abundance of Proteobacteria increased significantly, reaching over 80% in the heavily TNT-contaminated area near explosive-waste water pool. Furthermore, functional gene analysis indicated that Proteobacteria had an advantage in degrading energetic compounds, and thus had the potential to improve the soil quality at ammunition demolition sites. This study provides a scientific basis for the future remediation and management of contaminated soils at ammunition demolition sites, as well as for the selection of efficient degraders of energetic compounds.

Genetic Analysis of the Peach SnRK1β3 Subunit and Its Function in Transgenic Tomato Plants

Background/Objectives: The sucrose non-fermentation-related kinase 1 (SnRK1) protein complex in plants plays an important role in energy metabolism, anabolism, growth, and stress resistance. SnRK1 is a heterotrimeric complex. The SnRK1 complex is mainly composed of α, β, βγ, and γ subunits. Studies on plant SnRK1 have primarily focused on the functional α subunit, with the β regulatory subunit remaining relatively unexplored. The present study aimed to elucidate the evolutionary relationship, structural prediction, and interaction with the core α subunit of peach SnRK1β3 (PpSnRK1) subunit. Methods: Bioinformatics analysis of PpSnRK1 was performed through software and website. We produced transgenic tomato plants overexpressing PpSnRK1 (OEPpSnRK1). Transcriptome analysis was performed on OEPpSnRK1 tomatoes. We mainly tested the growth index and drought resistance of transgenic tomato plants. Results: The results showed that PpSnRK1 has a 354 bp encoded protein sequence (cds), which is mainly located in the nucleus and cell membrane. Phylogenetic tree analysis showed that PpSnRK1β3 has similar domains to other woody plants. Transcriptome analysis of OEPpSnRK1β3 showed that PpSnRK1β3 is widely involved in biosynthetic and metabolic processes. Functional analyses of these transgenic plants revealed prolonged growth periods, enhanced growth potential, improved photosynthetic activity, and superior drought stress tolerance. Conclusions: The study findings provide insight into the function of the PpSnRK1 subunit and its potential role in regulating plant growth and drought responses. This comprehensive analysis of PpSnRK1 will contribute to further enhancing our understanding of the plant SnRK1 protein complex.

Dominance of recombinant DWV genomes with changing viral landscapes as revealed in national US honey bee and varroa mite survey

Honey bees are essential pollinators for global agriculture. The viromes of US commercial apiaries and their ectoparasitic mites are poorly characterized at a strain level and there is a need to integrate genomics into pathogen surveillance. We sequenced RNA viromes from 383 adult bees and 173 mites pooled samples from 11 major US beekeeping hubs in 2021, assembling 45 complete and 1702 partial genomes. Protein sequence similarity networks and recombinant genome identification revealed a new viral landscape. Sinaivirus (n = 312), Iflavirus sacbroodi (n = 280), and Iflavirus aladeformis (DWV, n = 135) genomes were common. Recombinant DWV genomes with high nucleotide identity were widespread, and DWV type A master variants were rare, with an indication that RT-PCR surveillance may over-detect type A due to the prevalence of recombinant DWV genomes. Future work should use genomic strategies to avoid misidentification of common honey bee virus genomes and their impact on colony health.

The identification of a SARs-CoV2 S2 protein-derived peptide with super-antigen-like stimulatory properties on T-cells.

Severe COVID-19 can trigger a cytokine storm, leading to acute respiratory distress syndrome (ARDS) with similarities to superantigen-induced toxic shock syndrome. An outstanding question is whether SARS-CoV-2 protein sequences can directly induce inflammatory responses. In this study, we identify a region in the SARS-CoV-2 S2 spike protein with sequence homology to bacterial super-antigens (termed P3). Computational modeling predicts P3 binding to sites on MHC class I/II and the TCR that partially overlap with sites for the binding of staphylococcal enterotoxins B and H. Like SEB and SEH peptides, P3 stimulated 25-40% of human CD4+ and CD8+ T cells, increasing IFN-γ and granzyme B production. viSNE and SPADE profiling identified overlapping and distinct IFN-γ and GZMB subsets. The super-antigenic properties of P3 were further evident by its selective expansion of T cells expressing specific TCR Vα and Vβ chain repertoires. In vivo experiments in mice revealed that the administration of P3 led to a significant upregulation of proinflammatory cytokines IL-1β, IL-6, and TNF-α. While the clinical significance of P3 in COVID-19 remains unclear, its homology to other mammalian proteins suggests a potential role for this peptide family in human inflammation and autoimmunity.

Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research

Lyme disease is caused by an infection with the spirochete Borrelia burgdorferi, and is the most common vector-borne disease in North America. B. burgdorferi isolates harbor extensive genomic and proteomic variability and further comparison of isolates is key to understanding the infectivity of the spirochetes and biological impacts of identified sequence variants. Here, we applied both transcriptome analysis and mass spectrometry-based proteomics to assemble peptide datasets of B. burgdorferi laboratory isolates B31, MM1, and the infective isolate B31-5A4, to provide a publicly available Borrelia PeptideAtlas. Included are total proteome, secretome, and membrane proteome identifications of the individual isolates. Proteomic data collected from 35 different experiment datasets, totaling 386 mass spectrometry runs, have identified 81,967 distinct peptides, which map to 1,113 proteins. The Borrelia PeptideAtlas covers 86% of the total B31 proteome of 1,291 protein sequences. The Borrelia PeptideAtlas is an extensible comprehensive peptide repository with proteomic information from B. burgdorferi isolates useful for Lyme disease research.

Assessment of Punicalagin activity Against Influenza a Virus Proteins via In-silico Approaches

Background: To investigate the anti-influenza mechanism of punicalagin and its inhibitory effects on influenza virus proteins, we conducted molecular docking studies targeting 11 viral proteins. Additionally, molecular dynamics simulations were performed for the protein with the lowest free energy and the minimum concentration required for binding in a simulated environment. Methods: The molecular structure and data of punicalagin were obtained from the PubChem database and converted into a PDB file. FASTA sequences of viral proteins were retrieved from UniProt, and their PDB structures were predicted using the I-TASSER server. Molecular docking was performed using AutoDock 4.2 software, while molecular dynamics simulations were conducted with Gromacs 2022 software. Results: The PB1-F2 protein exhibited the best inhibitory performance, with a binding energy of -5.76 kcal/mol and the lowest inhibition constant (Ki). Docking of punicalagin to the PB1-F2 protein led to a significant decrease in the average total energy (TE), radius of gyration (Rg), and root mean square deviation (RMSD), as well as alterations in the secondary structure of the protein (P < 0.001). The most prominent secondary structural change was a reduction in coil structures and an increase in turn structures. Conclusions: Punicalagin displayed a strong binding affinity for the PB1-F2 protein compared to other influenza viral proteins. Considering the role of PB1-F2 in exacerbating inflammation caused by influenza, punicalagin may mitigate inflammation in patients by modulating the activity of this protein.

Genome-wide analysis and expression profiling of the polyamine oxidase gene family in Solanum tuberosum L.

BackgroundPolyamine oxidase (PAO) is a crucial enzyme involved in the breakdown of polyamines (PAs) in plants. It not only regulates the levels of PAs, but also plays a role in the oxidative decomposition of PAs and the release of stress-related signals, contributing to the plant’s response and resistance to various adversities. While there have been numerous studies on the response of PAO to stress in other crops, there is a lack of research on this topic in potatoes, a major food crop.ResultsIn this study, we aimed to explore the biological function of the StPAO gene in potato growth and development, as well as its expression patterns under stress. Using bioinformatics methods, we identified 14 StPAO genes in the potato genome. Protein sequence comparisons revealed a high similarity between the PAO proteins of potato and Arabidopsis. Chromosomal mapping and gene structure analysis showed that the StPAO genes were not evenly distributed on the chromosome and all contained an amino-oxidase domain. Furthermore, analysis of the promoters of these genes revealed the presence of abiotic and stress-related cis-acting elements, indicating their potential role in responding to different stresses. To investigate the expression patterns of these genes under stress, we used qRT-PCR to study their response to high temperature, drought, and ABA stress. Our results showed that StPAO6 and StPAO10 were significantly up-regulated under high temperature stress, indicating that they were involved in the process of potato resistance to high temperatures. Similarly, StPAO1, StPAO3, and StPAO4 were significantly up-regulated under drought stress, indicating their potential role in potatoes’ responses to drought. After ABA treatment, the expression levels of StPAO4, StPAO5, StPAO7, and StPAO14 were significantly up-regulated, suggesting their involvement in chemical defense mechanisms. Interestingly, the expression of StPAO11–13 was inhibited by all three stresses.ConclusionsIn conclusion, our study highlights the multifunctional nature of the StPAO gene family in potatoes, which plays a crucial role in coping with various stresses. This research deepens our understanding of the potato StPAO gene family and provides a reference for future studies on its function. It also serves as a theoretical basis for breeding stress-resistant potato varieties in the future.