Protein Sequence Homology Research Articles

Evolutionary trajectory of transcription factors and selection of targets for metabolic engineering.

Transcription factors (TFs) provide potentially powerful tools for plant metabolic engineering as they often control multiple genes in a metabolic pathway. However, selecting the best TF for a particular pathway has been challenging, and the selection often relies significantly on phylogenetic relationships. Here, we offer examples where evolutionary relationships have facilitated the selection of the suitable TFs, alongside situations where such relationships are misleading from the perspective of metabolic engineering. We argue that the evolutionary trajectory of a particular TF might be a better indicator than protein sequence homology alone in helping decide the best targets for plant metabolic engineering efforts. This article is part of the theme issue 'The evolution of plant metabolism'.

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors.

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

Frequency of Euroglyphus maynei sensitization in respiratory allergies: a real-life study with bioinformatic analysis and geographical exploration of allergen prevalence.

Dermatophagoides pteronyssinus and Dermatophagoides farinae belong to the family Pyroglyphidae (subfamily: "Dermatophagoidinae") and have the respective allergenic proteins of Der p1, Der p2, and Der p23 and Der f1 and Der f2. Euroglyphus maynei, belongs to the family Pyroglyphidae (subfamily: "Pyroglyphinae") and its main allergenic protein is Eur m1, a source of sensitization. Sensitization to D. pteronyssinus and D. farinae is assessed through skin tests, while sensitization to E. maynei is assessed less frequently. This experimental work aims to analyze the prevalence of sensitization to E. maynei in patients with respiratory allergies treated at M. Albanesi Allergy and Immunology Unit in Bari, Italy, and the sequence homology of major allergenic proteins of E. maynei with D. farinae and D. pteronyssinus was analyzed. In this real-life study, 65 patients were enrolled. In particular, patients with respiratory allergy were subjected to skin prick tests for common respiratory allergens, including Euroglyphus maynei. The sequence homology analysis was performed between the major allergenic proteins of E. maynei and those of D. pteronyssinus and D. farinae. Sensitization to E. maynei accounts for 41.5% of patients. All patients with E. maynei sensitization had concomitant sensitization to D. farinae and D. pteronyssinus. The analysis of sequence homology of Der p1 and Der f1 proteins with the sequence of Eur m1 protein demonstrated an identity of 84.4% and 86%, respectively. Nearly 50% of house dust mites-sensitized patients have a concomitant sensitization to E. maynei. The cross-sensitization could be due to Der f1, Der p1, and Eur m1 similarity.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

Tribbles1 is host protective during in vivo mycobacterial infection.

Tuberculosis is a major global health problem and is one of the top 10 causes of death worldwide. There is a pressing need for new treatments that circumvent emerging antibiotic resistance. Mycobacterium tuberculosis parasitises macrophages, reprogramming them to establish a niche in which to proliferate, therefore macrophage manipulation is a potential host-directed therapy if druggable molecular targets could be identified. The pseudokinase Tribbles1 (Trib1) regulates multiple innate immune processes and inflammatory profiles making it a potential drug target in infections. Trib1 controls macrophage function, cytokine production, and macrophage polarisation. Despite wide-ranging effects on leukocyte biology, data exploring the roles of Tribbles in infection in vivo are limited. Here, we identify that human Tribbles1 is expressed in monocytes and is upregulated at the transcript level after stimulation with mycobacterial antigen. To investigate the mechanistic roles of Tribbles in the host response to mycobacteria in vivo, we used a zebrafish Mycobacterium marinum (Mm) infection tuberculosis model. Zebrafish Tribbles family members were characterised and shown to have substantial mRNA and protein sequence homology to their human orthologues. trib1 overexpression was host-protective against Mm infection, reducing burden by approximately 50%. Conversely, trib1 knockdown/knockout exhibited increased infection. Mechanistically, trib1 overexpression significantly increased the levels of proinflammatory factors il-1β and nitric oxide. The host-protective effect of trib1 was found to be dependent on the E3 ubiquitin kinase Cop1. These findings highlight the importance of Trib1 and Cop1 as immune regulators during infection in vivo and suggest that enhancing macrophage TRIB1 levels may provide a tractable therapeutic intervention to improve bacterial infection outcomes in tuberculosis.

Abstract 5798: Preclinical safety assessment for specificity of biotherapeutics using the membrane proteome array

Abstract Rigorous specificity analysis is critical for the development of antibody-based therapies, as even minimal off-target binding can lead to toxicity and clinical failures. Long believed to be exquisitely specific, recent preclinical data and our own work indicate that monoclonal antibodies (MAbs) frequently (~25%) display cross-reactivity. In many cases, off-target interactions occur with unrelated proteins that cannot be predicted by protein sequence homology. Cross-reactivity can lead to serious or even life-threatening consequences especially when MAbs are configured as bispecifics, antibody-drug conjugates (ADC) or CAR-T cell therapies, and IND applications for biotherapeutics require cross-reactivity assessment to prevent adverse events. Tissue cross-reactivity (TCR) studies have traditionally been used to screen for off-target binding, however, with poor predictive value for in vivo safety and toxicity. We developed the Membrane Proteome Array (MPA) platform to de-risk MAb-based therapeutics by testing for specificity across 6,000 human membrane proteins expressed in unfixed cells. The MPA assesses binding interactions by high-throughput flow cytometry allowing for high sensitivity detection and rapid analysis. All targets identified on the MPA screen are validated by secondary titration analysis. In contrast to TCR studies, proteins in the MPA exist in their native conformations and are not altered by fixation. Recently, we used the MPA to select a lead antibody candidate targeting the oncotherapeutic target Claudin 6 (CLDN6). Through MPA screening, we successfully identified a lead candidate devoid of cross-reactivity against other CLDN family members or any other membrane protein across the human genome. The MPA has also been used to rapidly evaluate the specificity of novel CAR-T cell therapies for cancer and autoimmunity. The MPA has been used for lead selection and data from the MPA has been accepted by the FDA as a part of IND applications for antibody-based therapies. The MPA is currently under consideration for being developed as an FDA-qualified Drug Development Tool. Citation Format: Kristopher S. Raghavan, Jonathan T. Sullivan, Carmen Navia, Rachel H. Fong, Benjamin J. Doranz. Preclinical safety assessment for specificity of biotherapeutics using the membrane proteome array [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5798.

MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search.

Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by incorporating the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to the nucleotide (nt) database and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI's nt database or 60-fold larger than RNAcentral. The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSAs) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.

In the twilight zone of protein sequence homology: do protein language models learn protein structure?

Protein language models based on the transformer architecture are increasingly improving performance on protein prediction tasks, including secondary structure, subcellular localization, and more. Despite being trained only on protein sequences, protein language models appear to implicitly learn protein structure. This paper investigates whether sequence representations learned by protein language models encode structural information and to what extent. We address this by evaluating protein language models on remote homology prediction, where identifying remote homologs from sequence information alone requires structural knowledge, especially in the "twilight zone" of very low sequence identity. Through rigorous testing at progressively lower sequence identities, we profile the performance of protein language models ranging from millions to billions of parameters in a zero-shot setting. Our findings indicate that while transformer-based protein language models outperform traditional sequence alignment methods, they still struggle in the twilight zone. This suggests that current protein language models have not sufficiently learned protein structure to address remote homology prediction when sequence signals are weak. We believe this opens the way for further research both on remote homology prediction and on the broader goal of learning sequence- and structure-rich representations of protein molecules. All code, data, and models are made publicly available.

Reclassification of ASFV into 7 Biotypes Using Unsupervised Machine Learning.

In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different genotypes. However, a newly proposed system recategorized all ASFV isolates into 6 genotypes exclusively using the predicted protein sequences of p72. However, ASFV has a large genome that encodes between 150-200 genes, and classifications using a single gene are insufficient and misleading, as strains encoding an identical p72 often have significant mutations in other areas of the genome. We present here a new classification of ASFV based on comparisons performed considering the entire encoded proteome. A curated database consisting of the protein sequences predicted to be encoded by 220 reannotated ASFV genomes was analyzed for similarity between homologous protein sequences. Weights were applied to the protein identity matrices and averaged to generate a genome-genome identity matrix that was then analyzed by an unsupervised machine learning algorithm, DBSCAN, to separate the genomes into distinct clusters. We conclude that all available ASFV genomes can be classified into 7 distinct biotypes.

Paralogue-Specific Roles of SUMO1 and SUMO2/3 in Protein Quality Control and Associated Diseases.

Small ubiquitin-related modifiers (SUMOs) function as post-translational protein modifications and regulate nearly every aspect of cellular function. While a single ubiquitin protein is expressed across eukaryotic organisms, multiple SUMO paralogues with distinct biomolecular properties have been identified in plants and vertebrates. Five SUMO paralogues have been characterized in humans, with SUMO1, SUMO2 and SUMO3 being the best studied. SUMO2 and SUMO3 share 97% protein sequence homology (and are thus referred to as SUMO2/3) but only 47% homology with SUMO1. To date, thousands of putative sumoylation substrates have been identified thanks to advanced proteomic techniques, but the identification of SUMO1- and SUMO2/3-specific modifications and their unique functions in physiology and pathology are not well understood. The SUMO2/3 paralogues play an important role in proteostasis, converging with ubiquitylation to mediate protein degradation. This function is achieved primarily through SUMO-targeted ubiquitin ligases (STUbLs), which preferentially bind and ubiquitylate poly-SUMO2/3 modified proteins. Effects of the SUMO1 paralogue on protein solubility and aggregation independent of STUbLs and proteasomal degradation have also been reported. Consistent with these functions, sumoylation is implicated in multiple human diseases associated with disturbed proteostasis, and a broad range of pathogenic proteins have been identified as SUMO1 and SUMO2/3 substrates. A better understanding of paralogue-specific functions of SUMO1 and SUMO2/3 in cellular protein quality control may therefore provide novel insights into disease pathogenesis and therapeutic innovation. This review summarizes current understandings of the roles of sumoylation in protein quality control and associated diseases, with a focus on the specific effects of SUMO1 and SUMO2/3 paralogues.

Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.

Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large multiple sequence alignment. A central premise is that covariance between amino acid positions reflects coevolution due to a shared functional or biophysical constraint. In this review, we describe the implementation and discuss the advantages, limitations, and recent progress on two coevolution-based modeling approaches: (1) Potts models of protein sequence (direct coupling analysis [DCA]-like), and (2) the statistical coupling analysis (SCA). Each approach detects interesting features of protein sequence and structure-the former emphasizes local physical contacts throughout the structure, while the latter identifies larger evolutionarily coupled networks of residues. Recent advances in large-scale gene synthesis and high-throughput functional selection now motivate additional work to benchmark model performance across quantitative function prediction and de novo design tasks.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability.

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Genome-wide identification of the histone acetyltransferase gene family in Citrus clementina and its potential roles in citrate metabolism

Abstract Histone acetyltransferase (HAT) catalyzes histone acetylation and is involved in plant growth and development and stress responses. Here, 37 CitHAT genes were identified in Citrus clementina. Their physicochemical properties, chromosomal location, gene structure, conserved domain and motif, and cis-acting elements were characterized. CitHATs were classified into four subfamilies based on protein sequence homology, which was strongly supported by gene structure, conserved domain, and motif analysis. The cis-acting elements in gene promoter regions were predicted to be associated with the regulation of plant growth, stress resistance, and response to hormones. Phenotypic and transcriptomic analyses of citrus callus with mock treatment and HAT inhibitor treatment revealed that increased citric acid content in inhibitor treatment may be attributed to differential expression of CitPEPCK2 and CitGS2, which may be due to the hypo-acetylation of histone H3. The expression patterns of CitHATs in citrus fruit development stages showed that CitHAG11 and CitHAG28 exhibited a negative correlation with citric acid content. Our study associates the potential function of histone acetyltransferases in citrate metabolism and extends the molecular mechanism of citrate metabolism in fruits.

Immunoinformatics for Novel Multi-Epitope Vaccine Development in Canine Parvovirus Infections.

Canine parvovirus (CPV-2) is one of the most important pathogens of dogs of all ages, causing pandemic infections that are characterized by fatal hemorrhagic enteritis. The CPV-2 vaccine is recommended as a core vaccine for pet animals. Despite the intensive practice of active immunization, CPV-2 remains a global threat. In this study, a multi-epitope vaccine against CPV-2 was designed, targeting the highly conserved capsid protein (VP2) via in silico approaches. Several immunoinformatics methods, such as epitope screening, molecular docking, and simulation were used to design a potential vaccine construct. The partial protein sequences of the VP2 gene of CPV-2 and protein sequences retrieved from the NCBI were screened to predict highly antigenic proteins through antigenicity, trans-membrane-topology screening, an allergenicity assessment, and a toxicity analysis. Homologous VP2 protein sequences typically linked to the disease were identified using NCBI BLAST, in which four conserved regions were preferred. Overall, 10 epitopes, DPIGGKTGI, KEFDTDLKP, GTDPDDVQ, GGTNFGYIG, GTFYFDCKP, NRALGLPP, SGTPTN, LGLPPFLNSL, IGGKTG, and VPPVYPN, were selected from the conserved regions to design the vaccine construct. The molecular docking demonstrated the higher binding affinity of these epitopes with dog leukocyte antigen (DLA) molecules. The selected epitopes were linked with Salmonella enterica flagellin FliC adjuvants, along with the PADRE sequence, by GGS linkers to construct a vaccine candidate with 272 nucleotides. The codon adaptation and in silico cloning showed that the generated vaccine can be expressed by the E. coli strain, K12, and the sequence of the vaccine construct showed no similarities with dog protein. Our results suggest that the vaccine construct might be useful in preventing canine parvoviral enteritis (CPE) in dogs. Further in vitro and in vivo experiments are needed for the validation of the vaccine candidate.

NGS Analysis to Detect Mutation in Brain Tumor Diagnostic

Abstract: This study presents an integrated computational approach for analyzing protein sequences and their 3D structures. By leveraging the MMDB Macromolecular database, homologs of a protein sequence of interest are identified, and interactive visualization of their structural properties is provided. The computational alignment method, using BLASTP, allows for efficient determination of sequence similarities and identification of conserved regions among multiple protein sequences. COBALT is employed to refine sequence alignments and facilitate graphical analysis of sequence relationships. RASMOL, a computational analysis program, generates 2-D representations of protein-ligand complexes, enabling visual exploration of their interactions. ORF finder is used to identify coding regions in mRNA sequences, aiding in the prediction of protein-coding regions. The approach is applied to brain tumor diagnostics using human biological samples, exploring the structural properties of brain tumor-related proteins with the help of the 2RHU protein structure and PYMOL visualization software. Overall, this integrated computational framework offers a comprehensive toolkit for protein sequence analysis, structure visualization, and homology modeling, with potential applications in drug discovery, molecular biology, and medical diagnostics

Unsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines.

Data sets in the real world are often complex and to some degree hierarchical, with groups and subgroups of data sharing common characteristics at different levels of abstraction. Understanding and uncovering the hidden structure of these data sets is an important task that has many practical applications. To address this challenge, we present a general method for building relational data trees by exploiting the learning dynamics of the restricted Boltzmann machine. Our method is based on the mean-field approach, derived from the Plefka expansion, and developed in the context of disordered systems. It is designed to be easily interpretable. We tested our method in an artificially created hierarchical data set and on three different real-world data sets (images of digits, mutations in the human genome, and a homologous family of proteins). The method is able to automatically identify the hierarchical structure of the data. This could be useful in the study of homologous protein sequences, where the relationships between proteins are critical for understanding their function and evolution.

A Preliminary Survey of Transfer RNA Modifications and Modifying Enzymes of the Tropical Plant Cocos nucifera L.

The coconut (Cocos nucifera L.) is a commercial crop widely distributed among coastal tropical regions. It provides millions of farmers with food, fuel, cosmetics, folk medicine, and building materials. Among these, oil and palm sugar are representative extracts. However, this unique living species of Cocos has only been preliminarily studied at molecular levels. Benefiting from the genomic sequence data published in 2017 and 2021, we investigated the transfer RNA (tRNA) modifications and modifying enzymes of the coconut in this survey. An extraction method for the tRNA pool from coconut flesh was built. In total, 33 species of modified nucleosides and 66 homologous genes of modifying enzymes were confirmed using a nucleoside analysis using high-performance liquid chromatography combined with high-resolution mass spectrometry (HPLC-HRMS) and homologous protein sequence alignment. The positions of tRNA modifications, including pseudouridines, were preliminarily mapped using a oligonucleotide analysis, and the features of their modifying enzymes were summarized. Interestingly, we found that the gene encoding the modifying enzyme of 2'-O-ribosyladenosine at the 64th position of tRNA (Ar(p)64) was uniquely overexpressed under high-salinity stress. In contrast, most other tRNA-modifying enzymes were downregulated with mining transcriptomic sequencing data. According to previous physiological studies of Ar(p)64, the coconut appears to enhance the quality control of the translation process when subjected to high-salinity stress. We hope this survey can help advance research on tRNA modification and scientific studies of the coconut, as well as thinking of the safety and nutritional value of naturally modified nucleosides.

Expression, localization, and function of P4HB in the spermatogenesis of Chinese mitten crab (Eriocheir sinensis).

The sperm of Chinese mitten crab (Eriocheir sinensis) have special noncondensed nuclei. The formation and stability of the special nuclei are closely related to the correct folding of proteins during spermatogenesis. P4HB plays a key role in protein folding, but its expression and role in the spermatogenesis of E. sinensis are unclear. To investigate the expression and distribution characteristics of P4HB in the spermatogenesis of E. sinensis as well as its possible role. The testis tissues of adult and juvenile E. sinensis were used as materials. We utilized a variety of techniques, including homology modeling, phylogenetic analysis, RT-qPCR, western blotting, and immunofluorescence staining to predict the protein structure and sequence homology of P4HB, analyze its expression in the testis tissues, and localize and semi-quantitatively assess its expression in different male germ cells. The sequence of P4HB protein in E. sinensis shared a high similarity of 58.09% with the human protein disulfide isomerase, and the phylogenetic tree analysis indicated that the protein sequence was highly conserved among crustaceans, arthropods, and other animals species. P4HB was found to be expressed in both juvenile and adult E. sinensis testis tissues, with different localization patterns observed all over the developmental stages of male germ cells. It was higher expressed in the spermatogonia, spermatocytes, and stage I spermatids, followed by the mature sperm than in the stage II and III spermatids. The subcellular localization analysis revealed that P4HB was predominantly expressed in the cytoplasm, cell membrane, and extracellular matrix in the spermatogonia, spermatocytes, stage I and stage II spermatids, with some present in specific regions of the nuclei in the spermatogonia. In contrast, P4HB was mainly localized in the nuclei of stage III spermatids and sperm, with little expression observed in the cytoplasm. P4HB was expressed in the testis tissues of both adult and juvenile E. sinensis, but the expression and localization were different in male germ cells at various developmental stages. The observed differences in the expression and localization of P4HB may be an essential factor in maintaining the cell morphology and structure of diverse male germ cells in E. sinensis. Additionally, P4HB expressed in the nuclei of spermatogonia, late spermatids, and sperm may play an indispensable role in maintaining the stability of the noncondensed spermatozoal nuclei in E. sinensis.

Identification of a covert evolutionary pathway between two protein folds

Although homologous protein sequences are expected to adopt similar structures, some amino acid substitutions can interconvert α-helices and β-sheets. Such fold switching may have occurred over evolutionary history, but supporting evidence has been limited by the: (1) abundance and diversity of sequenced genes, (2) quantity of experimentally determined protein structures, and (3) assumptions underlying the statistical methods used to infer homology. Here, we overcome these barriers by applying multiple statistical methods to a family of ~600,000 bacterial response regulator proteins. We find that their homologous DNA-binding subunits assume divergent structures: helix-turn-helix versus α-helix + β-sheet (winged helix). Phylogenetic analyses, ancestral sequence reconstruction, and AlphaFold2 models indicate that amino acid substitutions facilitated a switch from helix-turn-helix into winged helix. This structural transformation likely expanded DNA-binding specificity. Our approach uncovers an evolutionary pathway between two protein folds and provides a methodology to identify secondary structure switching in other protein families.

Molecular characterization of vancomycin-resistant Staphylococcus aureus isolated from bovine milk.

Vancomycin-resistant Staphylococcus aureus (VRSA) is a zoonotic life-threatening pathogen. Vancomycin exhibits anti-bacterial activity by inhibiting peptidoglycan synthesis by binding to the D-ala-D-ala terminus of the peptidoglycan. But in VRSA, D-ala-D-ala is replaced by D-ala-D-lactate due to the presence of vanA, vanB or vanD genes. This study was intended to identify the molecular prevalence of VRSA in 768 bovine milk samples, risk factor association, antibiogram profile and bioinformatics analysis of VRSA by targeting vanB gene. Out of a total of 248 S. aureus isolates from mastitic milk samples, the phenotypic and genotypic prevalence of VRSA was estimated to be 17.74% and 10.89%, respectively. Farm-level risk factors including use of improper milking technique, lack of milker's care during milking, unhygienic conditions during milking and no dry cow therapy were found to be significantly associated (p < 0.05). Anti-microbial susceptibility testing of VRSA isolates exhibited the highest resistance to oxytetracycline, followed by oxacillin and Trimethoprim+sulfamethoxazole. The current study sequences showed more resemblance with reported sequences from Iraq (MN747834) and Egypt (MK095504, MK087830), which belong to vanB gene from S. aureus as compared to sequences from other countries, which belong to vanB gene from the genus Enterococcus. The Genetic Algorithm for Recombination Detection (GARD) found 234 potential breakpoints, translating into a search room of 123,883,305 models with up to 4 breakpoints. The phylogenetic motif profiling method discovered evolutionarily conserved residues across target genes' homologous protein sequences. These residues were discovered to be conserved in drug-resistant target proteins over the evolutionary process and may play a key role in their function. The current study revealed a molecular prevalence of VRSA in dairy animals, along with molecular analysis of vancomycin resistance in S. aureus by targeting the vanB gene using standard bioinformatics tools. The occurrence of VRSA in animals requires serious attention because this pathogen has zoonotic potential, so it can become a greater risk to consumer health.