Current Proteomics - Volume 19, Issue 4, 2022

Background: Picrophilus torridus is a thermoacidophilic archaeon that thrives in an extremely low pH (0-1) and high temperatures (50-60°C). Thus, it is a suitable organism to study microbial genetics and metabolic adaptations to the extremely acidic and moderate thermal environment. Objective: In the present study we have conducted a global proteome analysis of P. torridus and discerned the cytosolic proteome of P. torridus using gel-free, liquid chromatographymass spectrometry (LC-MS/MS). Methods: The cytosolic proteins of P. torridus were extracted and identified using gel-free, LCMS/ MS. Gene Ontology-based pathway analysis and protein-protein interaction studies were performed to understand the role of various cytosolic proteins in sustaining the thermoacidophilic environment. Also, domain analysis of hypothetical/uncharacterized proteins was performed. Results: Using gel-free LC-MS/MS, 408 cytosolic proteins of P. torridus were identified, including 36 hypothetical/uncharacterized proteins. Thus, we could identify 26.58 % of the theoretical proteome of P. torridus. The majority of the cytosolic proteins were observed to be multi-functional and involved in activities related to microbial metabolism. Conclusion: Comparison with an earlier study that used gel-based LC-MS analysis to identify cytosolic proteins of P. torridus revealed that gel-free LC-MS was better in identifying more number of proteins and also, higher/lower molecular weight proteins. The findings of this study may contribute to our understanding of the P. torridus proteome and serve as a foundation for future proteomic research on other thermoacidophilic archaea.

Objective: Meat is biochemically active. Various pre-and post-harvest processes affect meat quality. Metabolomics is a valuable tool to elucidate metabolite changes in meat. The overall goal of this mini-review was to provide an overview of various techniques, data analysis, and application of metabolomics in meat color research. Results: Both targeted and non-targeted approaches are used to determine metabolite profiles in meat. Researchers use gas-, liquid-chromatography, and nuclear magnetic resonance platforms to separate molecules. Metabolomics is used to characterize muscle-specific differences in color stability, meat tenderness, the impact of aging on meat color, and to determine metabolite profile differences between normal-pH and dark-cutting beef. Color stable muscles have more glycolytic metabolites than color labile muscles. Conclusion: The use of metabolomics has greatly enhanced our understanding of metabolites' role in meat quality. There is a need for multiple databases to obtain comprehensive metabolite libraries specific to food. Metabolomics in combination with wet-laboratory techniques can provide novel insights on the relationship between postmortem metabolism and meat color.

Background: Spider venom show abundant diversity in both peptides and proteins, which play essential roles in new drug development and agrochemistry. The venoms of Macrothele yani species have strong toxicity on the victims. Objective: The purpose of this study is to comprehensively characterize the profile of venom proteins and peptides of spider Macrothele yani mainly inhabiting Yunnan province, China. Methods: Using a combination of RNA sequencing of the venom glands and venom proteomics based on Liquid Chromatography-Electrospray Ionization-Tandem Mass Spectrometry (LC-ESI-MS/MS), we provide the first overview of the peptides and proteins synthesized from Macrothele yani. Results: A total of 116 peptide sequences were analyzed, and 43 homologous proteins were matched, of which 38.10% were toxin proteins. High-throughput sequencing by the HiSeq-2000 (Illumina), followed by de novo assembly. As a result, 301,024 similar protein sequences were annotated in the available databases. A total of 68 toxins-related sequences were identified, comparative sequence analyses of these sequences indicated the presence of different types of enzymes and toxin- like genes, including Acetylcholinesterase, Hyaluronidase, cysteine-rich secretory proteins (CRISP), Astacin metalloprotease and other venom components. Conclusion: The venom of a spider is a very abundant resource in nature. They were analyzed to determine their function in pathophysiology. Molecular templates with potential application value in medical and biological fields were obtained by classifying and characterizing the presumed components of spider venom of Macrothele yani, which laid a foundation for further study of the venom in the future.

Background: With the development of medicine and technological advancement, the concept of precision medicine is becoming popular, and the traditional principle of all-in-one therapy has been gradually fading. Utilizing the detection of genome, transcriptome, proteome, and metabolome, combined with big data analysis to discover new pathogenic mechanisms, provides more effective prescriptions with fewer side effects and even shifts the emphasis of medicine from disease treatment to disease prevention. Methods: Proteomics is one of the potential tools for monitoring the alternations of protein expression. This study analyzed the proteomic alternations between normal colon tissue and cancerous colon tissue via two-dimensional difference gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) to select the potential target proteins. Results: The experimental results demonstrated that a total of 90 proteins were identified, which were significantly expressed. These proteins were classified according to their functions. They were found to be mainly associated with cytoskeleton regulation, glycolysis, and protein folding. Furthermore, immunoblotting was used to verify the differentially expressed proteins, and the results were in line with the trends in the proteomic analysis. Conclusion: To sum up, these differentially expressed proteins could be used as potential and precise biomarkers in the diagnosis or treatment of colorectal cancer.

Background: Protein function is closely related to its location within the cell. Determination of protein subcellular location is helpful in uncovering its functions. However, traditional biological experiments to determine the subcellular location are of high cost and low efficiency, which cannot meet today’s needs. In recent years, many computational models have been set up to identify the subcellular location of proteins. Most models use features derived from protein sequences. Recently, features extracted from the protein-protein interaction (PPI) network have become popular in studying various protein-related problems. Objective: A novel model with features derived from multiple PPI networks was proposed to predict protein subcellular location. Methods: Protein features were obtained by a newly designed network embedding algorithm, Mnode2vec, which is a generalized version of the classic Node2vec algorithm. Two classic classification algorithms: support vector machine and random forest, were employed to build the model. Results: Such model provided good performance and was superior to the model with features extracted by Node2vec. Also, this model outperformed some classic models. Furthermore, Mnode2vec was found to produce powerful features when the path length was small. Conclusion: The proposed model can be a powerful tool to determine protein subcellular location, and Mnode2vec can efficiently extract informative features from multiple networks.

Background: Severe acute respiratory syndrome (SARS-CoV-2), a zoonotic virus, is the pathogenic causal agent for the ongoing pandemic. Despite the lethality of the disease, there are no therapeutic agents available to combat the disease outbreak, and the vaccines currently accessible are insufficient to control the widespread, fast-mutating virus infection. Objective: This research study focuses on determining potential epitopes by examining the entire proteome of the SARS-CoV-2 virus using an in-silico approach. Methods: To develop a vaccine for the deadly virus, researchers screened the whole proteome of the SARS-CoV-2 virus for potential epitopes in order to find a powerful peptide candidate that is both unique and fulfils the vaccine's objective. It is mandatory to identify the suitable B-cell and T-cell epitopes of the observed SARS-CoV-2 surface glycoprotein (QKN61229.1). These epitopes were subjected to various tests, including antigenicity, allergenicity, and other physicochemical properties. The T-cell epitopes that met the criteria were subjected to population coverage analysis. It helped in better understanding epitope responses to the target population, computing peptide conservancy, and clustering epitopes based on sequence match, MHC binding, and T-cell restriction sites. Lastly, the interactions between the T-cell receptor (TCR) and a peptide-MHC were studied to thoroughly understand MHC restriction to design a peptide- vaccine. Results: The findings revealed that four B-cell epitopes, two MHC-I epitopes, and four MHC-II epitopes qualified for all of the tests and so have antigen affinity. Conclusion: Based on the results obtained from this study, the estimated peptides are promising candidates for peptide-vaccine design and development.