Biochemical reactions in metabolite-protein interaction

A normalized differential sequence feature encoding method based on amino acid sequences

Protein interactions are the foundation of all metabolic activities of cells, such as apoptosis, the immune response, and metabolic pathways. In order to optimize the performance of protein interaction prediction, a coding method based on normalized difference sequence characteristics (NDSF) of amino acid sequences is proposed. By using the positional relationships between amino acids in the sequences and the correlation characteristics between sequence pairs, NDSF is jointly encoded. Using principal component analysis (PCA) and local linear embedding (LLE) dimensionality reduction methods, the coded 174-dimensional human protein sequence vector is extracted using sequence features. This study compares the classification performance of four ensemble learning methods (AdaBoost, Extra trees, LightGBM, XGBoost) applied to PCA and LLE features. Cross-validation and grid search methods are used to find the best combination of parameters. The results show that the accuracy of NDSF is generally higher than that of the sequence matrix-based coding method (MOS) coding method, and the loss and coding time can be greatly reduced. The bar chart of feature extraction shows that the classification accuracy is significantly higher when using the linear dimensionality reduction method, PCA, compared to the nonlinear dimensionality reduction method, LLE. After classification with XGBoost, the model accuracy reaches 99.2%, which provides the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.

The Knowns and Unknowns in Protein-Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Glutathione-functionalized magnetic thioether-COFs for the simultaneous capture of urinary exosomes and enrichment of exosomal glycosylated and phosphorylated peptides

Exosomes play an irreplaceable role in physiological and pathological processes, and the study of proteomics (especially protein post-translational modifications, PTMs) in exosomes can reveal the pathogenesis of diseases and screen therapeutic disease targets. The separation and enrichment process is an essential step in mass spectroscopy-based exosomal PTMs studies to reduce sample complexity and ionization-suppression effects. Herein, we designed a novel magnetic zwitterionic material, namely glutathione-functionalized thioether covalent organic frameworks (Fe₃O₄@Thio-COF@Au@GSH), possessing fast magnetic responsiveness, regular porosity, and a suitable surface area. Thanks to the hydrophilicity and charge-switchable feature of GSH, for the first time, both the capture of exosomes from biological fluids and enrichment of the inherent glycoproteins/phosphoproteins in the exosomes were achieved with the same material. Furthermore, the high enrichment capacity was validated by theoretical calculations. The low detection limits (0.2/0.4 fmol for HRP/β-casein), high selectivity (1 : 1000 for HRP/β-casein : BSA molar ratio), and high exosomal glycoproteomics/phosphoproteomics profiling capability proved the feasibility of the developed method. This work provides a new heuristic strategy to solve the problems of exosomal capture and glycoproteins/phosphoproteins pretreatment in exosomal proteomics.

Design of a Spiropyran-Based Smart Adsorbent with Dual Response: Focusing on Highly Efficient Enrichment of Phosphopeptides

Smart responsive materials have attractive application prospects due to their tunable behaviors. In this work, we design novel spiropyran (SP)-based magnetic nanoparticles (MNP-SP) with dual response to ultraviolet light and pH and apply them to the enrichment of phosphopeptides. SP is modified on the surface of magnetic nanoparticles through a simple esterification reaction, based on which an MNP-SP-MS phosphopeptide identification platform is established. The capture and release of phosphopeptides are facilely adjusted by changing external light and the pH of the solution. The smart responsive MNP-SP has fast magnetic response performance, high sensitivity (detection limit of 0.4 fmol), and good reusability (6 cycles). In addition, MNP-SP is used for the enrichment of phosphopeptides in skimmed milk, human saliva, and human serum samples, indicating that it is an ideal adsorbent for enriching low-abundance phosphopeptides in complex biological environments.

Identification and Characterization of Robust Hepatocellular Carcinoma Prognostic Subtypes Based on an Integrative Metabolite-Protein Interaction Network

Metabolite-protein interactions (MPIs) play key roles in cancer metabolism. However, our current knowledge about MPIs in cancers remains limited due to the complexity of cancer cells. Herein, the authors construct an integrative MPI network and propose a MPI network based hepatocellular carcinoma (HCC) subtyping and mechanism exploration workflow. Based on the expressions of hub proteins on the MPI network, two prognosis-distinctive HCC subtypes are identified. Meanwhile, multiple interdependent features of the poor prognostic subtype are observed, including hypoxia, DNA hypermethylation of metabolic pathways, fatty acid accumulation, immune pathway up-regulation, and exhausted T-cell infiltration. Notably, the immune pathway up-regulation is probably induced by accumulated unsaturated fatty acids which are predicted to interact with multiple immune regulators like SRC and TGFB1. Moreover, based on tumor microenvironment compositions, the poor prognostic subtype is further divided into two sub-populations showing remarkable differences in metabolism. The subtyping shows a strong consistency across multiple HCC cohorts including early-stage HCC. Overall, the authors redefine robust HCC prognosis subtypes and identify potential MPIs linking metabolism to immune regulations, thus promoting understanding and clinical applications about HCC metabolism heterogeneity.

Prediction and collection of protein-metabolite interactions

Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can all be mediated through protein-metabolite interactions (PMIs). Compared with the rich knowledge of protein-protein interactions, little is known about PMIs. To the best of our knowledge, no existing database has been developed for collecting PMIs. The recent rapid development of large-scale mass spectrometry analysis of biomolecules has led to the discovery of large amounts of PMIs. Therefore, we developed the PMI-DB to provide a comprehensive and accurate resource of PMIs. A total of 49 785 entries were manually collected in the PMI-DB, corresponding to 23 small molecule metabolites, 9631 proteins and 4 species. Unlike other databases that only provide positive samples, the PMI-DB provides non-interaction between proteins and metabolites, which not only reduces the experimental cost for biological experimenters but also facilitates the construction of more accurate algorithms for researchers using machine learning. To show the convenience of the PMI-DB, we developed a deep learning-based method to predict PMIs in the PMI-DB and compared it with several methods. The experimental results show that the area under the curve and area under the precision-recall curve of our method are 0.88 and 0.95, respectively. Overall, the PMI-DB provides a user-friendly interface for browsing the biological functions of metabolites/proteins of interest, and experimental techniques for identifying PMIs in different species, which provides important support for furthering the understanding of cellular processes. The PMI-DB is freely accessible at http://easybioai.com/PMIDB.