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New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies., Competing Interests: Declaration of competing interest The authors have no conflicts of interest, financial or otherwise., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research., Competing Interests: Declaration of competing interest The Authors Declare That There is No Conflict of Interest., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation., (© The Author(s) 2019. Published by Oxford University Press.)

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients' Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

In recent years, a lot of efforts have been made in conformational epitope prediction as antigen proteins usually bind antibodies with an assembly of sequentially discontinuous and structurally compact surface residues. Currently, only a few methods for spatial epitope prediction are available with focus on single residue propensity scales or continual segments clustering. In the method of SEPPA, a concept of 'unit patch of residue triangle' was introduced to better describe the local spatial context in protein surface. Besides that, SEPPA incorporated clustering coefficient to describe the spatial compactness of surface residues. Validated by independent testing datasets, SEPPA gave an average AUC value over 0.742 and produced a successful pick-up rate of 96.64%. Comparing with peers, SEPPA shows significant improvement over other popular methods like CEP, DiscoTope and BEpro. In addition, the threshold scores for certain accuracy, sensitivity and specificity are provided online to give the confidence level of the spatial epitope identification. The web server can be accessed at http://lifecenter.sgst.cn/seppa/index.php. Batch query is supported.

Deregulation of alternative splicing is implicated as a relevant source of molecular heterogeneity in cancer. However, the targets and intrinsic mechanisms of splicing in hepatocarcinogenesis are largely unknown. Here, we report a functional impact of a Splicing Regulatory Glutamine/Lysine-Rich Protein 1 (SREK1) variant and its regulator, Serine/arginine-rich splicing factor 10 (SRSF10). HCC patients with poor prognosis express higher levels of exon 10-inclusive SREK1 (SREK1L). SREK1L can sustain BLOC1S5-TXNDC5 (B-T) expression, a targeted gene of nonsense-mediated mRNA decay through inhibiting exon-exon junction complex binding with B-T to exert its oncogenic role. B-T plays its competing endogenous RNA role by inhibiting miR-30c-5p and miR-30e-5p, and further promoting the expression of downstream oncogenic targets SRSF10 and TXNDC5. Interestingly, SRSF10 can act as a splicing regulator for SREK1L to promote hepatocarcinogenesis via the formation of a SRSF10-associated complex. In summary, we demonstrate a SRSF10/SREK1L/B-T signalling loop to accelerate the hepatocarcinogenesis. Alternative splicing is dysregulated in hepatocellular carcinoma. Here, the authors investigate the role of the splice variant of Splicing Regulatory Glutamic Acid and Lysine Rich Protein 1 (SREK1) and its upstream regulator, Serine/arginine-rich splicing factor 10 (SRSF10) in sustaining the oncogenic signal. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is common and its prognosis is often poor due to difficulties in early diagnosis and optimal treatment strategies. TNM staging system is useful in predicting prognosis but only possible after surgery. Therefore, it is desirable to investigate prognostic factors/markers that may predict prognosis before surgery by which helps appropriate management decisions preoperatively. Methods: A total of 320 GC patients were consecutively recruited from 2004 to 2013 and followed up for 127 months (10.6 years) after surgery. These patients’ were examined for body mass index (BMI) and blood levels of albumin, triglyceride, total cholesterol, low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C). Kaplan-Meier method and log rank test were used to analyze long-term survival using the above potential risk markers. We first employed medians of these variables to reveal maximal potentials of the above prognostic predictors. Results: Three major findings were obtained: (1) Preoperative BMI was positively correlated with albumin (r = 0.144, P<0.05) and triglyceride (r = 0.365, P<0.01), but negatively correlated with TNM staging (r = -0.265, P<0.05). Preoperative albumin levels were positively correlated with triglyceride (r = 0.173, P<0.05) but again, negatively correlated with TNM staging (r = -0.137, P<0.05); (2) Poor survival was observed in GC patients with lower levels of BMI (P = 0.028), albumin (P = 0.004), and triglyceride (P = 0.043), respectively. Receiver operating characteristic (ROC) curve analyses suggested BMI, albumin and triglyceride to have survival-predictor powers similar to TNM system; and (3) Cox multi-factorial analyses demonstrated that age (P = 0.049), BMI (P = 0.016), cell differentiation (P = 0.001), and TNM staging (P = 0.011) were independent overall survival-predictors for GC patients. Conclusions: Preoperative BMI, albumin, and triglyceride levels are capable of predicting survival for GC patients superior to postoperative TNM system in terms of timing for management. As potential survival-predictors, preoperative tests of BMI, albumin and triglyceride, combined with clinical imaging, may help personalized management for GC patients including planning surgical strategy, optimal radio-chemotherapy and appropriate follow-up intervals after surgery. [ABSTRACT FROM AUTHOR]

Background: The pandemic of obesity is a global public health concern. Most studies on obesity are skewed toward high-income and urban settings and few covers low-income populations. This study focused on the prevalence of overweight and obesity and their correlations with blood lipids/metabolites/enzymes (bio-indicators) in a rural community typical of low-income in remote western China. Methods: This study was performed in a Muslim ethnic Uyghur rural community in Kashi Prefecture of Xinjiang, about 4,407 km (2,739 miles) away from Beijing. Body mass index (BMI) and major blood bio-indicators (25 total items) were measured and demographic information was collected from 1,733 eligible healthy women aged 21 to 71 yrs, of whom 1,452 had complete data for analysis. More than 92% of the women lived on US$1.00/day or less. According to the Chinese criteria, overweight and obesity were defined as BMI at 24 to <28 kg/m2 and at ≥28 kg/m2, respectively. Results: The average BMI among these low-income women was 24.0±4.0 (95% CI, 17.5–33.7) kg/m2. The prevalence of obesity and overweight was high at 15.1% and 28.9%, respectively. Among 25 bio-indicators, BMI correlated positively with the levels of 11 bio-indicators including triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TCHOL), glucose (GLU), and uric acid (UA); but negatively with the levels of 5 bio-indicators including high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A/B (APO A/B). Conclusions: This is the first investigation reporting overweight and obesity being common in low-income Muslim Uyghur women, whose BMI correlates with several important blood bio-indicators which are risk factors for diabetes and cardiovascular diseases. These findings may help make preventive public health policies in Uyghur communities. To prevent diabetes and cardiovascular diseases in low-income settings, we therefore propose a cost-effective, two-step strategy first to screen for obesity and then to screen persons with obesity for diabetes and cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Cryptochromes are blue-light receptors that mediate blue-light inhibition of hypocotyl elongation and blue-light stimulation of floral initiation in Arabidopsis. In addition to their blue-light-dependent functions, cryptochromes are also involved in blue-light-independent regulation of the circadian clock, cotyledon unfolding, and hypocotyl inhibition. However, the molecular mechanism associated with the blue-light-independent function of cryptochromes remains unclear. We reported here a comparative proteomics study of the light regulation of protein expression. We showed that, as expected, the protein expression of many metabolic enzymes changed in response to both blue light and red light. Surprisingly, some light-regulated protein expression changes are impaired in the cry1cry2 mutant in both blue light and red light. This result suggests that, in addition to mediating blue-light-dependent regulation of protein expression, cryptochromes are also involved in the blue-light-independent regulation of gene expression. Consistent with this hypothesis, the cry1cry2 mutant exhibited reduced changes of mRNA expression in response to not only blue light, but also red light, although the cryptochrome effects on the red-light-dependent gene expression changes are generally less pronounced. These results support a hypothesis that, in addition to their blue-light-specific functions, cryptochromes also play roles in the control of gene expression mediated by the red/far-red-light receptor phytochromes. [ABSTRACT FROM PUBLISHER]

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The third complement component (C3) is an acute phase protein that plays a central role in reperfusion injury in several organ models. To investigate the contribution of local synthesis of C3 and distinguish it from that of circulating complement mainly produced by hepatic synthesis, we employed a mouse renal isograft model. Our model demonstrated a close relationship between the extent of intrarenal expression of C3 and cold-ischemia induced injury. Ischemic C3-positive donor kidneys transplanted into C3-positive or C3-negative recipients developed widespread tissue damage and severe acute renal failure. In contrast, ischemic C3-negative isografts exhibited only mild degrees of functional and structural disturbance, even when transplanted into normal C3-positive recipients. Thus local synthesis of C3, mostly identified in the tubular epithelium, was essential for complement-mediated reperfusion damage, whereas circulating C3 had a negligible effect. Our results suggest a two-compartment model for the pathogenic function of C3, in which the extravascular compartment is the domain of local synthesis of C3, and where the role of circulating C3 is redundant. Our data cast new light on the mechanism of complement-mediated tissue injury in nonimmunological disorders, and challenges the longstanding dogma that circulating components are the main complement effectors of extravascular tissue damage. [ABSTRACT FROM AUTHOR]

Previously, a novel protein, MGr1-Ag, was associated with tumor multidrug resistance (MDR), and the role and the underlying mechanisms of MGr1-Ag in MDR of gastric cancer cells were characterized. Initial studies using the introduction of sense or antisense vectors for MGr1-Ag resulted in the genetical up- or downregulation of MGr1-Ag in gastric cancer cells, respectively. Subsequent studies revealed the expression of MGr1-Ag, P-glycoprotein (P-gp), MDR-associated protein (MRP), Bcl-2 and Bax in gastric cancer cells via Western blot analysis. The sensitivity of gastric cancer cells to chemotherapeutic drugs was assessed using the colony-forming assay, and Adriamycin (ADM) accumulation was evaluated by flow cytometry. Further study of ADM-induced apoptosis was detected by annexin-V/propidium iodide staining. The expression level of MGr1-Ag in MDR gastric cancer cells is much higher than that in their parental cells. Overexpression of exogenous MGr1-Ag may promote the MDR phenotype of gastric cancer cells, decrease intracellular ADM accumulation and protect gastric cancer cells from ADM-induced apoptosis, whereas downregulation of MGr1-Ag had reverse effects. Western blot analysis suggested that MGr1-Ag may regulate the expression of P-gp, MRP, Bcl-2 and Bax. In conclusion, MGr1-Ag may promote MDR of gastric cancer cells via a decrease in intracellular drug accumulation and inhibition of drug-induced apoptosis. Copyright © 2006 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

The post-translational glycosylation of proteins by O-linked α-mannose is conserved from bacteria to humans. Due to advances in high-throughput mass spectrometry-based approaches, a variety of glycoproteins are identified to be O-mannosylated. Various proteins with O-mannosylation are involved in biological processes, providing essential necessity for proper growth and development. In this review, we summarize the process and regulation of O-mannosylation. The multi-step O-mannosylation procedures are quite dynamic and complex, especially when considering the structural and functional inspection of the involved enzymes. The widely studied O-mannosylated proteins in human include α-Dystroglycan (α-DG), cadherins, protocadherins, and plexin, and their aberrant O-mannosylation are associated with many diseases. In addition, O-mannosylation also contributes to diverse functions in lower eukaryotes and prokaryotes. Finally, we present the relationship between O-mannosylation and gut microbiota (GM), and elucidate that O-mannosylation in microbiome is of great importance in the dynamic balance of GM. Our study provides an overview of the processes of O-mannosylation in mammalian cells and other organisms, and also associated regulated enzymes and biological functions, which could contribute to the understanding of newly discovered O-mannosylated glycoproteins. [Display omitted] • O-mannosylation is conserved from mammalian cells to microorganisms. • The multi-step procedures are quite complex that involves many crucial enzymes. • Aberrant O-mannosylation is engaged in distinct pathological states. • O-mannosylation plays important roles in through gut microbiota. [ABSTRACT FROM AUTHOR]

Telomeric repeat binding factor 1 (TRF1) is a component of the multiprotein complex "shelterin," which organizes the telomere into a high-order structure. TRF1 knockout embryos suffer from severe growth defects without apparent telomere dysfunction, suggesting an obligatory role for TRF1 in cell cycle control. To date, the mechanism regulating the mitotic increase in TRF1 protein expression and its function in mitosis remains unclear. Here, we identify guanine nucleotide-binding protein-like 3 (GNL3L), a GTP-binding protein most similar to nucleostemin, as a novel TRF1-interacting protein in vivo. GNL3L binds TRF1 in the nucleoplasm and is capable of promoting the homodimerization and telomeric association of TRF1, preventing promyelocytic leukemia body recruitment of telomere-bound TRF1, and stabilizing TRF1 protein by inhibiting its ubiquitylation and binding to FBX4, an E3 ubiquitin ligase for TRF1. Most importantly, the TRF1 protein-stabilizing activity of GNL3L mediates the mitotic increase of TRF1 protein and promotes the metaphase-to-anaphase transition. This work reveals novel aspects of TRF1 modulation by GNL3L. [ABSTRACT FROM AUTHOR]