Identification Of Proteins Research Articles

Carbon metabolism of a novel isolate from Lacticaseibacillus rhamnosus Probio-M9 derived through space mutant.

Carbon source is a necessary nutrient for bacterial strain growth. In industrial production, the cost of using different carbon sources varies greatly. Moreover, the complex environment in space may cause metabolic a series of changes in the strain, and this method has been successfully applied in some basic research. To date, space mutagenesis is still limited number of studies, particularly in carbon metabolism of probiotics. HG-R7970-41 was isolated from bacterium suspension (Probio-M9) after space flight, which can produce capsular polysaccharide after space mutagenesis. Phenotype Microarray (PM) was used to evaluated the metabolism of HG-R7970-41 in 190 single carbon sources. RNA sequencing and total protein identification of two strains revealed their different carbon metabolism mechanisms. PM results demonstrated the metabolism of 10 carbon sources were different between Probio-M9 and HG-R7970-41. Transcriptomic and proteomic analyses revealed that this change in carbon metabolism of HG-R7970-41 mainly related to changes in phosphorylation and the glycolysis pathway. Based on the metabolic mechanism of different carbon sources and related gene cluster analysis, we found that the final metabolic activities of HG-R7970-41 and Probio-M9 were mainly regulated by PTS-specific membrane embedded permease, carbohydrate kinase and two rate-limiting enzymes (phosphofructokinase and pyruvate kinase) in the glycolysis pathway. The expanded culture test also confirmed that HG-R7970-41 had different metabolic characteristics from original strain. These results suggested that space environment could change carbon metabolism of Probio-M9. The new isolate (HG-R7970-41) showed a different carbon metabolism pattern from the original strain mainly by the regulation of two rate-limiting enzymes.

Standardizing Protein Corona Characterization in Nanomedicine: A Multicenter Study to Enhance Reproducibility and Data Homogeneity.

We recently revealed significant variability in protein corona characterization across various proteomics facilities, indicating that data sets are not comparable between independent studies. This heterogeneity mainly arises from differences in sample preparation protocols, mass spectrometry workflows, and raw data processing. To address this issue, we developed standardized protocols and unified sample preparation workflows, distributing uniform protein corona digests to several top-performing proteomics centers from our previous study. We also examined the influence of using similar mass spectrometry instruments on data homogeneity and standardized database search parameters and data processing workflows. Our findings reveal a remarkable stepwise improvement in protein corona data uniformity, increasing overlaps in protein identification from 11% to 40% across facilities using similar instruments and through a uniform database search. We identify the key parameters behind data heterogeneity and provide recommendations for designing experiments. Our findings should significantly advance the robustness of protein corona analysis for diagnostic and therapeutics applications.

Lymph Node Metastasis in Gastrointestinal Carcinomas: A View from a Proteomics Perspective.

Lymph node metastasis (LNM) is one of the major prognostic factors in human gastrointestinal carcinomas (GICs). The lymph node-positive patients have poorer survival than node-negative patients. LNM is directly associated with the recurrence and poor survival of patients with GICs. The early detection of LNM in patients and designing effective therapies to suppress LNM may significantly impact the survival of these patients. The rapid progress made in proteomic technologies could be successfully applied to identify molecular targets for cancers at high-throughput levels. LC-MS/MS analysis enables the identification of proteins involved in LN metastasis, which can be utilized for diagnostic and therapeutic applications. This review summarizes the studies on LN metastasis in GICs using proteomic approaches to date.

Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images

The mislocalization of proteins leads to breast cancer, one of the world’s most prevalent cancers, which can be identified from immunohistochemical images. Here, based on the deep learning framework, location prediction models were constructed using the features of breast immunohistochemical images. Ultimately, six differentially localized proteins that with stable differentially predictive localization, maximum localization differences, and whose predicted results are not affected by removing a single image are obtained (CCNT1, NSUN5, PRPF4, RECQL4, UTP6, ZNF500). Further verification reveals that these proteins are not differentially expressed, but are closely associated with breast cancer and have great classification performance. Potential mechanism analysis shows that their co-expressed or co-located proteins and RNAs may affect their localization, leading to changes in interactions and functions that further causes breast cancer. They have the potential to help shed light on the molecular mechanisms of breast cancer and provide assistance for its early diagnosis and treatment.

Proteomic Profile of Endometrial Cancer: A Scoping Review.

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Proteome identification of common immunological proteins of two nematode parasites.

Although helminth parasites have different life cycles, their hosts share similar immune responses involving Th2 cell-type. Here, we extracted proteins from the larvae of Anisakis simplex complex and Trichinella spiralis to identify common and specific antigens (or allergens) associated with the Th2 immune response. We performed two-dimensional electrophoresis analysis and Matrix-assisted laser desorption ionization-time of flight/time of flight (MALDI-TOF/TOF) experiments. We found 13 potentially immunogenic proteins, which included 5 spots specific to T. spiralis and 8 common to T. spiralis and A. simplex, by tandem mass spectrometry. These molecules were identified structurally as actin, tropomyosin, col cuticle N domain-containing protein, and heat shock proteins. We also identified molecules related to parasite-host immune modulation and interactions. Our results may contribute to reveal potential roles of immunological proteins in parasite-derived immune modulation.

Proteomic Characterisation of Heart Failure Reveals a Unique Molecular Phenotype for Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a disease, which is difficult to diagnose at an early stage and for which there is a pressing need for more effective treatment options. The purpose of this study was to compare the molecular profile of HCM to that of ischaemic cardiomyopathy (ISCM) and dilated cardiomyopathy (DCM) for identification of protein and pathway targets that could support the development of better diagnostic and treatment options for HCM. A high-throughput mass spectrometry workflow was applied to achieve deep quantitative coverage of left ventricular tissue from HCM, DCM, ISCM and non-heart-failure control patients. HCM had a diverse proteomic profile compared to that of DCM and ISCM. Differentially expressed proteins unique to HCM were identified based on an observed fold change of ≥1.5 or ≤0.67 and q-value ≤ 0.05. Candidate proteins of interest were found to be significantly associated with clinical features of HCM. The significant association between these proteins and HCM was validated in an independent dataset. This represents one of the largest and deepest proteomic datasets for myocardial tissue reported to date. The dataset highlights the diverse proteomic profile of HCM, relative to other cardiomyopathies, and reveals disease-relevant pathways and promising biomarker candidates that are uniquely associated with HCM.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; ncases=51 929; ncontrols=39 980) and subsequent arterial ischemic stroke (AIS; ncases=45 120; ncontrols=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (protein quantitative trait loci) to determine the effect of 1463 plasma protein abundances on subsequent MACE using Mendelian randomization. Two variants were significantly associated with subsequent cardiovascular events: rs76472767 near gene RNF220 (odds ratio, 0.75 [95% CI, 0.64-0.85]; P=3.69×10-8) with subsequent AIS and rs13294166 near gene LINC01492 (odds ratio, 1.52 [95% CI, 1.37-1.67]; P=3.77×10-8) with subsequent MACE. Using Mendelian randomization, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 ([C-C motif chemokine 27], effect odds ratio, 0.77 [95% CI, 0.66-0.88]; adjusted P=0.05) and TNFRSF14 ([tumor necrosis factor receptor superfamily member 14], effect odds ratio, 1.42 [95% CI, 1.24-1.60]; adjusted P=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. We found evidence that 2 proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

A Novel Liquid Biopsy Method Based on Specific Combinations of Vesicular Markers Allows Us to Discriminate Prostate Cancer from Hyperplasia.

Prostate cancer is the second most common cancer in males worldwide, and its incidence is rising. Early detection is crucial for improving the outcomes, but the current screening methods have limitations. While prostate-specific antigen (PSA) testing is the most widely used screening tool, it has poor specificity, leading to a high rate of false positives and unnecessary biopsies. The existing biopsy techniques are invasive and are associated with complications. The liquid biopsy methods that analyze the biomarkers in blood or other bodily fluids offer a non-invasive and more accurate alternative for detecting and characterizing prostate tumors. Here, we present a novel liquid biopsy method for prostate cancer based on the identification of specific proteins in the extracellular vesicles isolated from the blood of patients with prostate cancer. We observed that a specific combination of sEV proteins is a sensitive indicator of prostate cancer. Indeed, we found that the number of clusters expressed by specific combinations of either intra-vesicular (STAT3 and CyclinD1) or surface proteins (ERBB3, ALK, and CD81) allowed us to significantly discriminate the patients with prostate cancer from the individuals with hyperplasia. This new liquid biopsy method has the potential to improve prostate cancer screening by providing a non-invasive and more accurate diagnostic tool.

PEL-PVP: Application of plant vacuolar protein discriminator based on PEFT ESM-2 and bilayer LSTM in an unbalanced dataset

Plant vacuoles, play a crucial role in maintaining cellular stability, adapting to environmental changes, and responding to external pressures. The accurate identification of vacuolar proteins (PVPs) is crucial for understanding the biosynthetic mechanisms of intracellular vacuoles and the adaptive mechanisms of plants. In order to more accurately identify vacuole proteins, this study developed a new predictive model PEL-PVP based on ESM-2. Through this study, the feasibility and effectiveness of using advanced pre-training models and fine-tuning techniques for bioinformatics tasks were demonstrated, providing new methods and ideas for plant vacuolar protein research. In addition, previous datasets for vacuolar proteins were balanced, but imbalance is more closely related to the actual situation. Therefore, this study constructed an imbalanced dataset UB-PVP from the UniProt database，helping the model better adapt to the complexity and uncertainty in real environments, thereby improving the model's generalization ability and practicality. The experimental results show that compared with existing recognition techniques, achieving significant improvements in multiple indicators, with 6.08 %, 13.51 %, 11.9 %, and 5 % improvements in ACC, SP, MCC, and AUC, respectively. The accuracy reaches 94.59 %, significantly higher than the previous best model GraphIdn. This provides an efficient and precise tool for the study of plant vacuole proteins.

PyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

Identification of Host Proteins Involved in Hepatitis B Virus Genome Packaging.

A critical part of the hepatitis B virus (HBV) life cycle is the packaging of the pregenomic RNA (pgRNA) into nucleocapsids. While this process is known to involve several viral elements, much less is known about the identities and roles of host proteins in this process. To better understand the role of host proteins, we isolated pgRNA and characterized its protein interactome in cells expressing either packaging-competent or packaging-incompetent HBV genomes. We identified over 250 host proteins preferentially associated with pgRNA from the packaging-competent version of the virus. These included proteins already known to support capsid formation, enhance viral gene expression, catalyze nucleocapsid dephosphorylation, and bind to the viral genome, demonstrating the ability of the approach to effectively reveal functionally significant host-virus interactors. Three of these host proteins, AURKA, YTHDF2, and ATR, were selected for follow-up analysis. RNA immunoprecipitation qPCR (RIP-qPCR) confirmed pgRNA-protein association in cells, and siRNA knockdown of the proteins showed decreased encapsidation efficiency. This study provides a template for the use of comparative RNA-protein interactome analysis in conjunction with virus engineering to reveal functionally significant host-virus interactions.

Development of uniform ribosome display technology enabling easy and efficient identification of full-length proteins that interact with bioactive small and large molecules.

Identifying target proteins that interact with bioactive molecules is indispensable for understanding their mechanisms of action. In this study, we developed a uniform ribosome display technology using equal-length DNAs and mRNAs to improve molecular display principle for target identification. The equal-length DNAs were designed to contain various coding sequences for full-length proteins with molecular weights of up to 130 kDa and were used to synthesize equal-length mRNAs, which allowed the formation of full-length protein-ribosome-equal-length mRNA complexes. Uniform ribosome display selections of dihydrofolate reductase and haloalkane dehalogenase mutant were performed against methotrexate and chlorohexane, respectively. Quantitative changes of proteins after each selection indicated that the target protein-displaying ribosomal complexes were specifically selected through non-covalent or covalent interactions with the corresponding bioactive molecules. Furthermore, selection of full-length proteins interacting with methotrexate or anti-DDX46 antibody from protein pools showed that only the target proteins could be precisely identified even though the molar amounts of equal-length mRNAs encoding them were adjusted to 1/20,000 of the total equal-length mRNAs. Thus, the uniform ribosome display technology enabled efficient identification of target proteins that interact with bioactive small and large molecules through simplified operations without deep sequencing.

A protein pre-trained model-based approach for the identification of the liquid-liquid phase separation (LLPS) proteins

Liquid-liquid phase separation (LLPS) regulates many biological processes including RNA metabolism, chromatin rearrangement, and signal transduction. Aberrant LLPS potentially leads to serious diseases. Therefore, the identification of the LLPS proteins is crucial. Traditionally, biochemistry-based methods for identifying LLPS proteins are costly, time-consuming, and laborious. In contrast, artificial intelligence-based approaches are fast and cost-effective and can be a better alternative to biochemistry-based methods. Previous research methods employed word2vec in conjunction with machine learning or deep learning algorithms. Although word2vec captures word semantics and relationships, it might not be effective in capturing features relevant to protein classification, like physicochemical properties, evolutionary relationships, or structural features. Additionally, other studies often focused on a limited set of features for model training, including planar π contact frequency, pi-pi, and β-pairing propensities. To overcome such shortcomings, this study first constructed a reliable dataset containing 1206 protein sequences, including 603 LLPS and 603 non-LLPS protein sequences. Then a computational model was proposed to efficiently identify the LLPS proteins by perceiving semantic information of protein sequences directly; using an ESM2-36 pre-trained model based on transformer architecture in conjunction with a convolutional neural network. The model could achieve an accuracy of 85.68% and 89.67%, respectively on training data and test data, surpassing the accuracy of previous studies. The performance demonstrates the potential of our computational methods as efficient alternatives for identifying LLPS proteins.

Identification and Functional Analysis of Odorant Binding Proteins in Apriona germari (Hope).

Apriona germari (Hope) presents a significant threat as a dangerous wood-boring pest, inflicting substantial harm to forest trees. Investigating the olfactory sensory system of A. germari holds substantial theoretical promise for developing eco-friendly control strategies. To date, however, the olfactory perception mechanism in A. germari remains largely unknown. Therefore, we performed transcriptome sequencing of A. germari across four distinct body parts: antennae, foreleg tarsal segments, mouthparts (maxillary and labial palps), and abdomen terminals, pinpointing the odorant binding protein (OBP) genes and analyzing their expression. We found eight AgerOBPs (5, 19, 23, 25, 29, 59, 63, 70) highly expressed in the antennae. In our competitive binding experiments, AgerOBP23 showed strong binding abilities to the pheromone component fuscumol acetate, eight plant volatiles (farnesol, cis-3-hexenal, nerolidol, myristol acetate, cis-3-hexenyl benzoate, (-)-α-cedrene, 3-ethylacetophenone, and decane), and four insecticides (chlorpyrifos, phoxim, indoxacarb, and cypermethrin). However, AgerOBP29 and AgerOBP63 did not show prominent binding activities to these tested chemicals. Through homology modeling and molecular docking, we identified the key amino acid sites involved in the binding process of AgerOBP23 to these ligands, which shed light on the molecular interactions underlying its binding specificity. Our study suggests that AgerOBP23 may serve as a potential target for future investigations of AgerOBP ligand binding. This approach is consistent with the reverse chemical ecology principle, establishing the groundwork for future studies focusing on attractant or repellent development by exploring further the molecular interactions between OBP and various compounds.

Application of Monoclonal Antibodies against Naturally Occurring Bioactive Ingredients.

Monoclonal antibodies (Mabs) are widely used in a variety of fields, including protein identification, life sciences, medicine, and natural product chemistry. This review focuses on Mabs against naturally occurring active compounds. The preparation of Mabs against various active compounds began in the 1980s, and now there are fewer than 50 types. Eastern blotting, which was developed as an antibody staining method for low-molecular-weight compounds, is useful for its ability to visually represent specific components. In this method, a mixture of lower-molecular-weight compounds, particularly glycosides, are separated by thin-layer chromatography (TLC). The compounds are then transferred to a membrane by heating, followed by treatment with potassium periodate (KIO4) to open the sugar moiety of the glycoside on the membrane to form an aldehyde group. Proteins are then added to form Schiff base bonds to enable adsorption on the membrane. A Mab is bound to the glycoside moiety on the membrane and reacts with a secondary antibody to produce color. Double Eastern blotting, which enables the simultaneous coloration of two glycosides, can be used to evaluate quality and estimate pharmacological effects. An example of staining by Eastern blotting and a component search based on the results will also be presented. A Mab-associated affinity column is a method for isolating antigen molecules in a single step. However, the usefulness of the wash fractions that are not bound to the affinity column is unknown. Therefore, we designated the wash fraction the "knockout extract". Comparing the nitric oxide (NO) production of a glycyrrhizin (GL)-knockout extract of licorice with a licorice extract revealed that the licorice extract is stronger. Therefore, the addition of GL to the GL-knockout extract of licorice increased NO production. This indicates that GL has synergic activity with the knockout extract. The GL-knockout extract of licorice inhibited high-glucose-induced epithelial-mesenchymal transition in NRK-52E cells, primarily by suppressing the Notch2 pathway. The real active constituent in licorice may be constituents other than GL, which is the causative agent of pseudohyperaldosteronism. This suggests that a GL-knockout extract of licorice may be useful for the treatment of diabetic nephritis.

Identification of low-abundance proteins in the royal jelly using the Osborne classification method

Royal jelly (RJ) is recognized as healthy food, with a high content of proteins. These proteins play important roles in honeybee caste and human health, but the proteomic analysis of low-abundance proteins in RJ has long been a challenge. Herein, we used the Osborne classification method to separate the RJ proteins of Xinjiang black bees into various fractions. The globulin, ethanol-soluble protein, and glutelin fractions were further separated by SDS-PAGE, and proteomic analysis was carried out by LC-MS/MS and searched against the UniProt database. A total of 23 secretory proteins were identified by proteomic analysis, in which 7 proteins were identified for the first time in RJ. The Osborne classification method combining one-dimensional gel electrophoresis-based proteomic analysis allows the identification of low-abundance proteins in the RJ and greatly extends the knowledge about the components and functions of RJ proteins. The raw data are available via ProteomeXchange with the identifier PXD023315. SignificanceThis study makes an important contribution to the research of the components and functions of low-abundance royal jelly proteins for the following reasons:1.The Osborne method was applied to fractionize the royal jelly proteins, which benefits the identification of low-abundance proteins.2.23 secretory proteins were identified from the globulins, the ethanol-soluble proteins, and the glutelins of the RJ, and there were 7 proteins identified for the first time in royal jelly.3.This study extends the knowledge about the components and functions of royal jelly proteins.

Reducing Washout of Proteins from Defatted Soybean Flakes by Alkaline Extraction: Fractioning and Characterization

Human health, sustainable development, numerous environmental issues, and animal welfare are increasingly driving research and development of plant-based protein products that can serve as meat substitutes. This trend is expected to continue in the coming years due to growing consumer awareness, with people gradually shifting from animal-based foods to more sustainable plant-based options. Soy proteins are a valuable source of plant proteins and are widely used in human and animal diets due to their nutritional value and health benefits. In this study, soybean protein extraction by two methods was compared: water extraction (lower salt content) and Tris-HCl extraction (higher salt content), aiming to characterize the resulting protein fractions. These fractions were studied using differential precipitation based on the isoelectric point. Protein identification by SDS-PAGE, scanning electron microscopy (SEM) for cellular structure assessment, and Fourier-transform infrared spectroscopy (FTIR) were used to determine residual protein left in the solid fraction after extraction using the two methods. Electrophoresis assays revealed the presence of the four main protein fractions (2S, 7S, 11S, and soy whey proteins) in the defatted soybean flakes, establishing the protein profile of Brazilian soybeans and for the two main waste streams of the production process—spent flakes and whey. The separation of fractions was carried out by differential precipitation. FTIR analysis indicated higher residual protein levels in solid residues after the water extraction method compared to the Tris-HCl extraction method. SEM analysis revealed the removal of protein bodies in both extraction methods and the presence of residual oil-containing bodies. Both methodologies are viable alternatives for the industrial separation of soybean protein fractions. Differential precipitation could be implemented to produce isolated products and improve the nutritional profile, increase process yield thus generating less industrial waste and driving the process towards environmental sustainability.

Identification, characterization and classification of prokaryotic nucleoid-associated proteins.

Common throughout life is the need to compact and organize the genome. Possible mechanisms involved in this process include supercoiling, phase separation, charge neutralization, macromolecular crowding, and nucleoid-associated proteins (NAPs). NAPs are special in that they can organize the genome at multiple length scales, and thus are often considered as the architects of the genome. NAPs shape the genome by either bending DNA, wrapping DNA, bridging DNA, or forming nucleoprotein filaments on the DNA. In this mini-review, we discuss recent advancements of unique NAPs with differing architectural properties across the tree of life, including NAPs from bacteria, archaea, and viruses. To help the characterization of NAPs from the ever-increasing number of metagenomes, we recommend a set of cheap and simple invitro biochemical assays that give unambiguous insights into the architectural properties of NAPs. Finally, we highlight and showcase the usefulness of AlphaFold in the characterization of novel NAPs.

Structure based functional identification of an uncharacterized protein from Coxiella burnetii involved in adipogenesis

Coxiella burnetii, the causative agent of Q fever, is an intracellular pathogen posing a significant global public health threat. There is a pressing need for dependable and effective treatments, alongside an urgency for further research into the molecular characterization of its genome. Within the genomic landscape of Coxiella burnetii, numerous hypothetical proteins remain unidentified, underscoring the necessity for in-depth study. In this study, we conducted comprehensive in silico analyses to identify and prioritize potential hypothetical protein of Coxiella burnetii, aiming to elucidate the structure and function of uncharacterized protein. Furthermore, we delved into the physicochemical properties, localization, and molecular dynamics and simulations, and assessed the primary, secondary, and tertiary structures employing a variety of bioinformatics tools. The in-silico analysis revealed that the uncharacterized protein contains a conserved Mth938-like domain, suggesting a role in preadipocyte differentiation and adipogenesis. Subcellular localization predictions indicated its presence in the cytoplasm, implicating a significant role in cellular processes. Virtual screening identified ligands with high binding affinities, suggesting the protein’s potential as a drug target against Q fever. Molecular dynamics simulations confirmed the stability of these complexes, indicating their therapeutic relevance. The findings provide a structural and functional overview of an uncharacterized protein from C. burnetii, implicating it in adipogenesis. This study underscores the power of in-silico approaches in uncovering the biological roles of uncharacterized proteins and facilitating the discovery of new therapeutic strategies. The findings provide valuable preliminary data for further investigation into the protein’s role in adipogenesis.