Analysis of the human endogenous coregulator complexome.

Elucidation of endogenous cellular protein-protein interactions and their networks is most desirable for biological studies. Here we report our study of endogenous human coregulator protein complex networks obtained from integrative mass spectrometry-based analysis of 3290 affinity purifications. By preserving weak protein interactions during complex isolation and utilizing high levels of reciprocity in the large dataset, we identified many unreported protein associations, such as a transcriptional network formed by ZMYND8, ZNF687, and ZNF592. Furthermore, our work revealed a tiered interplay within networks that share common proteins, providing a conceptual organization of a cellular proteome composed of minimal endogenous modules (MEMOs), complex isoforms (uniCOREs), and regulatory complex-complex interaction networks (CCIs). This resource will effectively fill a void in linking correlative genomic studies with an understanding of transcriptional regulatory protein functions within the proteome for formulation and testing of future hypotheses.

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially determined and screened by modeling. As a result, this study created a new pipeline aimed at using protein sequence to predict protein crystallization propensity in the protein material production stage, purification stage and production of crystal stage. The newly created pipeline proposed a new feature selection method, which involves combining Chi-square (${\chi }^{2}$) and recursive feature elimination together with the 12 selected features, followed by a linear discriminant analysisfor dimensionality reduction and finally, a support vector machine algorithm with hyperparameter tuning and 10-fold cross-validation is used to train the model and test the results. This new pipeline has been tested on three different datasets, and the accuracy rates are higher than the existing pipelines. In conclusion, our model provides a new solution to predict multistage protein crystallization propensity which is a big challenge in computational biology.

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.

Emerging Enterovirus A71 Subgenogroup B5 Causing Severe Hand, Foot, and Mouth Disease, Vietnam, 2023.

We report on a 2023 outbreak of severe hand, foot, and mouth disease in southern Vietnam caused by an emerging lineage of enterovirus A71 subgenogroup B5. Affected children were significantly older than those reported during previous outbreaks. The virus should be closely monitored to assess its potential for global dispersal.

mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences.

In the past decade, convolutional neural networks (CNNs) have been used as powerful tools by scientists to solve visual data tasks. However, many efforts of convolutional neural networks in solving protein function prediction and extracting useful information from protein sequences have certain limitations. In this research, we propose a new method to improve the weaknesses of the previous method. mCNN-ETC is a deep learning model which can transform the protein evolutionary information into image-like data composed of 20 channels, which correspond to the 20 amino acids in the protein sequence. We constructed CNN layers with different scanning windows in parallel to enhance the useful pattern detection ability of the proposed model. Then we filtered specific patterns through the 1-max pooling layer before inputting them into the prediction layer. This research attempts to solve a basic problem in biology in terms of application: predicting electron transporters and classifying their corresponding complexes. The performance result reached an accuracy of 97.41%, which was nearly 6% higher than its predecessor. We have also published a web server on http://bio219.bioinfo.yzu.edu.tw, which can be used for research purposes free of charge.

Association between dietary factors and breast cancer risk: a matched case-control study in Vietnam.

BACKGROUND: The role of diet in breast cancer prevention is controversial and limited in low-middle-income countries (LMICs). This study aimed to investigate the association between different dietary factors and breast cancer risk in Vietnamese women. METHODS: Three hundred seventy newly histologically confirmed breast cancer cases and 370 controls matched by 5-year age from September 2019 to March 2020 in Ho Chi Minh City were recorded dietary intake using a validated food frequency questionnaire. Odds ratios (OR) and 95% confidence intervals (95% CI) were evaluated using conditional logistic regression and adjusted with potential confounders. RESULTS: Compared to the lowest quartile of intake, we found that the highest intake of vegetables, fruit, soybean products, coffee, and egg significantly decreased breast cancer risk, including dark green vegetables (OR 0.46, 95% CI 0.27-0.78, ptrend=0.022), legumes (OR 0.19, 95% CI 0.08-0.44, ptrend <0.001), starchy vegetables (OR 0.37, 95% CI 0.21-0.66, ptrend=0.003), other vegetables (OR 0.46, 95% CI 0.28-0.77, ptrend=0.106), fruits (OR 0.44, 95% CI 0.26-0.74, ptrend <0.001), soybean product (OR 0.45, 95% CI 0.24-0.86, ptrend=0.311), coffee (OR 0.47, 95% CI 0.23-0.95, ptrend 0.004), and egg (OR 0.4, 95% CI 0.23-0.71, ptrend=0.002). CONCLUSION: Greater consumption of vegetables, fruit, soybean products, coffee, and eggs is associated with a lower risk of breast cancer. This study provides evidence of breast cancer prevention by increasing the intake of these dietary groups, especially in LMICs.

Endogenous ZAP affects Zika virus RNA interactome.

One of the most recent advances in the analysis of viral RNA-cellular protein interactions is the Comprehensive Identification of RNA-binding Proteins by Mass Spectrometry (ChIRP-MS). Here, we used ChIRP-MS in mock-infected and Zika-infected wild-type cells and cells knockout for the zinc finger CCCH-type antiviral protein 1 (ZAP). We characterized 'ZAP-independent' and 'ZAP-dependent' cellular protein interactomes associated with flavivirus RNA and found that ZAP affects cellular proteins associated with Zika virus RNA. The ZAP-dependent interactome identified with ChIRP-MS provides potential ZAP co-factors for antiviral activity against Zika virus and possibly other viruses. Identifying the full spectrum of ZAP co-factors and mechanisms of how they act will be critical to understanding the ZAP antiviral system and may contribute to the development of antivirals.

Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease.

Hand foot and mouth disease (HFMD) is caused by a variety of enteroviruses, and occurs in large outbreaks in which a small proportion of children deteriorate rapidly with cardiopulmonary failure. Determining which children are likely to deteriorate is difficult and health systems may become overloaded during outbreaks as many children require hospitalization for monitoring. Heart rate variability (HRV) may help distinguish those with more severe diseases but requires simple scalable methods to collect ECG data.We carried out a prospective observational study to examine the feasibility of using wearable devices to measure HRV in 142 children admitted with HFMD at a children's hospital in Vietnam. ECG data were collected in all children. HRV indices calculated were lower in those with enterovirus A71 associated HFMD compared to those with other viral pathogens.HRV analysis collected from wearable devices is feasible in a low and middle income country (LMIC) and may help classify disease severity in HFMD.

Epilepsy self-management mobile health application: A needs assessment in people with epilepsy and caregivers in Viet Nam.

OBJECTIVES: This study aimed to determine (1) the needsof Vietnamese people with epilepsy (PWE) and their caregivers for self-management mobile health applications and (2) the self-management features expected to be included in an application. METHODS: The survey consisted of an anonymous self-administered questionnaire that was distributed to PWE and caregivers from the age of 18 in Vietnam through online platforms and onsite at Nguyen Tri Phuong Hospital and University Medical Center, Ho Chi Minh City, from February 2022 to May 2022. The questionnaire assessed the participants' attitudes toward epilepsy self-management mobile applications, their willingness to use applications, and their expectations of the contents of an application. RESULTS: Responses from 103 participants were submitted. Eighty-one participants (78.6%) reported using a smartphone, but only 50.6% of those claimed to know about self-management applications. Most respondents (70.9%) thought the applications would be useful for disease self-management, and 68.9% were willing to use epilepsy self-management applications. In addition, the most expected features to be included in self-management applications were epilepsy information, seizure first aid, connecting with medical professionals, and a seizure diary. CONCLUSION: Most Vietnamese PWE and caregivers had a willingness to use epilepsy self-management applications.The expected features are related to all aspects of self-management, including information, seizure, medication, and safety management.

Unveiling the applicability of the Brussels Infant and Toddler Stool Scale for hard stool in young children: A South Asian perspective.

BACKGROUND AND AIM: The Rome IV criteria, the standard for diagnosing functional constipation (FC), deem the Bristol Stool Scale (BSS) unsuitable for assessing stool consistency in young children. Hence, the Brussels Infant and Toddler Stool Scale (BITSS) was developed. We aimed to validate and test the reliability of BITSS for hard stools and FC among infants and toddlers, where there is limited evidence in Asian populations. METHODS: The research evaluated FC in children aged 0-48 months who came for medical examination using Rome IV criteria. Stool properties provided by caregivers were assessed sequentially through three methods: the BSS, the BITSS, and caregiver reports. RESULTS: A total of 370 responses were received, with an average age of 26.2 months. Substantial agreement was observed between the BITSS and caregiver reports for hard stools (concordance rate: 91.9%, κ = 0.75), while near-perfect agreement was found between BITSS and BSS (concordance rate: 93.5%, κ = 0.81). The BITSS exhibited higher sensitivity than the BSS in assessing hard stools (95.3% vs 87.5%, P < 0.001). And the BITSS (23.5%) identified the highest prevalence of FC than the BSS (20.5%) and caregiver report (18.7%), with near-perfect agreement. Moderate agreement was reported when evaluating the test-retest reliability between BITSS and caregiver reports (concordance rate: 86.2%, κ = 0.44). CONCLUSIONS: The BITSS, more sensitive than the BSS in identifying abnormal, especially hard stools, aids in early FC detection in young children. These findings support using BITSS over BSS for evaluating hard stools in infants and toddlers, both in Vietnam and globally.

Pediatric living donor liver transplant for Budd-Chiari syndrome using a cryopreserved pulmonary vein graft for retro-hepatic vena cava reconstruction: A case report.

INTRODUCTION: In pediatric patients with Budd-Chiari syndrome (BCS), living donor liver transplantation (LDLT) raises substantial challenges regarding IVC reconstruction. CASE PRESENTATION: We present a case of an 8-year-old girl with BCS caused by myeloproliferative syndrome with JAK2 V617F mutation. She had a complete thrombosis of the inferior vena cava (IVC) with multiple collaterals, developing a Budd-Chiari syndrome. She underwent LDLT with IVC reconstruction with a cryopreserved pulmonary vein graft obtained from a provincial biobank. The living donor underwent a laparoscopic-assisted left lateral hepatectomy. The reconstruction of the vena cava took place on the back table and the liver was implanted en bloc with the reconstructed IVC in the recipient. Anticoagulation was immediately restarted after the surgery because of her pro-thrombotic state. Her postoperative course was complicated by a biliary anastomotic leak and an infected biloma. The patient recovered progressively and remained well on outpatient clinic follow-up 32 weeks after the procedure. CONCLUSION: IVC reconstruction using a cryopreserved pulmonary vein graft is a valid option during LDLT for pediatric patients with BCS where reconstruction of the IVC entails considerable challenges. Early referral to a pediatric liver transplant facility with a multidisciplinary team is also important in the management of pediatric patients with BCS.

Evaluation of the long-term protection conferred by an organosilicon-based disinfectant formulation against bacterial contamination of surfaces.

AIMS: The aim of this work was to evaluate the efficacy of an organosilicon-based, commercially available antimicrobial formulation in the My-shield® product line against bacterial surface contamination. METHODS AND RESULTS: The antimicrobial product was tested in vitro for its long-term persistence on surfaces and effectiveness against Staphylococcus aureus biofilms in comparison to 70% ethanol and 0.1% or 0.6% sodium hypochlorite. Field testing was also conducted over 6 weeks at a university athletic facility. In vitro studies demonstrated the log reductions achieved by the test product, 70% ethanol, and 0.1% sodium hypochlorite were 3.6, 3.1, and 3.2, respectively. The test product persisted on surfaces after washing and scrubbing, and pre-treatment with this product prevented S. aureus surface colonization for up to 30 days. In comparison, pre-treatment with 70% ethanol or 0.6% sodium hypochlorite was not protective against S. aureus biofilm formation after seven days. The field test demonstrated that weekly applications of the test product were more effective at reducing surface bacterial load than daily applications of a control product. CONCLUSIONS: The test product conferred greater long-term protection against bacterial growth and biofilm formation by S. aureus than ethanol and sodium hypochlorite. Even with less frequent applications, the test product maintained a high level of antimicrobial activity.

IL36RN Mutations and Correlated Characteristics in Generalized Pustular Psoriasis Patients in Can Tho City, Vietnam: A Cross-sectional study.

INTRODUCTION: Pustular psoriasis is a rare and severe form of psoriasis characterized by sterile pustules on an erythematous background. The disease ranges from localized to generalized forms, with the latter being particularly life-threatening and recurrent. Understanding the genetic basis of pustular psoriasis, particularly IL36RN mutations, is crucial for developing better treatments. This study aims to determine the prevalence and types of IL36RN gene mutations and their relationship with clinical and paraclinical features in patients with pustular psoriasis in Can Tho City, Vietnam. METHODS: A cross-sectional study was conducted at Can Tho Dermatology Hospital involving 59 patients diagnosed with generalized pustular psoriasis (GPP) according to ERASPEN and Japanese Dermatological Association criteria. Data on demographic, clinical, and laboratory characteristics were collected. IL36RN gene mutations were identified through genomic DNA sequencing. Statistical analyses were performed to explore associations between IL36RN mutations and clinical features. RESULTS: The study included 59 participants, predominantly female (69.5%), with an average age of 39.12 years. A significant proportion (83.1%) had a history of psoriasis, with frequent recurrences (94.9%). The most common IL36RN mutation identified was p.Arg10ArgfsX1, present in 44.1% of patients. Other mutations included p.Pro76Leu (20.3%) and p.Arg102Trp (1.7%). Patients with IL36RN mutations were younger and had an earlier disease onset. Significant associations were found between IL36RN mutations and clinical features such as fever (OR = 11, p < 0.0001) and geographic tongue (OR = 14.67, p < 0.0001). CONCLUSION: Our study reveals a high prevalence of IL36RN mutations, particularly p.Arg10ArgfsX1, in Vietnamese pustular psoriasis patients, strongly associating these mutations with clinical features like fever and geographic tongue.

In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study.

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

Dual-energy computed tomography in supporting prostatic artery embolization for benign prostatic hyperplasia.

Objective: Our study aims to evaluate the value of 256-slice dual-energy computed tomography (DECT) in supporting prostatic artery embolization (PAE) under digital subtraction angiography (DSA) for benign prostatic hyperplasia (BPH). Methods: The study was conducted on 88 patients who underwent PAE to treat BPH from January 2022 to November 2023. Of these, 38 patients who had PAE without DECT were placed in group 1, while the other 50 patients with pre-interventional DECT were assigned to group 2. The results of DECT imaging of the prostate artery (PA) were compared with the results of DSA imaging. Test for statistically significant differences between the variables of the two research groups using the T - student test and Mann-Whitney test algorithms with p < 0.05 corresponding to a 95% confidence interval. The data were analyzed according to medical statistical methods using SPSS 20.0 software. Results: DECT can detect the PA origin in 96.1% of cases, identify atherosclerosis at the root of the artery with a sensitivity of 66.7% and a specificity of 89.5%, and present anastomosis with a sensitivity of 72.7% and a specificity of 72.2%. There is no statistically significant difference in PA diameter on DECT compared to DSA with 95% confidence. Group 2 used DECT for 3D rendering of the PA before PAE had procedure time reduced by 25.8%, fluoroscopy time reduced by 23.2%, dose-area product (DAP) reduced by 25.6%, contrast medium volume reduced by 33.1% compared to group 1 not using DECT, statistically significant with 95% confidence. Conclusion: DECT is a valuable method for planning before PAE to treat BPH. 3D rendering DECT of PA provides anatomical information that minimizes procedure time, fluoroscopy time, dose-area product, and contrast medium volume.

Killing of Gram-negative and Gram-positive bacteria by a bifunctional cell wall-targeting T6SS effector.

The type VI secretion system (T6SS) is a powerful tool deployed by Gram-negative bacteria to antagonize neighboring organisms. Here, we report that Acinetobacter baumannii ATCC 17978 (Ab17978) secretes D-lysine (D-Lys), increasing the extracellular pH and enhancing the peptidoglycanase activity of the T6SS effector Tse4. This synergistic effect of D-Lys on Tse4 activity enables Ab17978 to outcompete Gram-negative bacterial competitors, demonstrating that bacteria can modify their microenvironment to increase their fitness during bacterial warfare. Remarkably, this lethal combination also results in T6SS-mediated killing of Gram-positive bacteria. Further characterization revealed that Tse4 is a bifunctional enzyme consisting of both lytic transglycosylase and endopeptidase activities, thus representing a family of modularly organized T6SS peptidoglycan-degrading effectors with an unprecedented impact in antagonistic bacterial interactions.

Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.

With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental changes in the embryo culture process. TLS also significantly advances predicting embryo quality, a crucial determinant of IVF cycle success. However, the current subjective nature of embryo assessments is due to inter- and intra-observer subjectivity, resulting in highly variable results. To address this challenge, reproductive medicine has gradually turned to artificial intelligence (AI) to establish a standardized and objective approach, aiming to achieve higher success rates. Extensive research is underway investigating the utilization of AI in TLS to predict multiple outcomes. These studies explore the application of popular AI algorithms, their specific implementations, and the achieved advancements in TLS. This review aims to provide an overview of the advances in AI algorithms and their particular applications within the context of TLS and the potential challenges and opportunities for further advancements in reproductive medicine.

Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.

PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. METHODS: This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. RESULTS: The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. CONCLUSION: The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.

Multi-Omics Analysis Reveals the IFI6 Gene as a Prognostic Indicator and Therapeutic Target in Esophageal Cancer.

The role of the IFI6 gene has been described in several cancers, but its involvement in esophageal cancer (ESCA) remains unclear. This study aimed to identify novel prognostic indicators for ESCA-targeted therapy by investigating IFI6's expression, epigenetic mechanisms, and signaling activities. We utilized public data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) to analyze IFI6's expression, clinical characteristics, gene function, pathways, and correlation with different immune cells in ESCA. The TIMER2.0 database was employed to assess the pan-cancer expression of IFI6, while UALCAN was used to examine its expression across tumor stages and histology subtypes. Additionally, the KEGG database helped identify related pathways. Our findings revealed 95 genes positively correlated and 15 genes negatively correlated with IFI6 in ESCA. IFI6 was over-expressed in ESCA and other cancers, impacting patient survival and showing higher expression in tumor tissues than normal tissues. IFI6 was also correlated with CD4+ T cells and B cell receptors (BCRs), both essential in immune response. GO Biological Process (GO BP) enrichment analysis indicated that IFI6 was primarily associated with the Type I interferon signaling pathway and the defense response to viruses. Intriguingly, KEGG pathway analysis demonstrated that IFI6 and its positively correlated genes in ESCA were mostly linked to the Cytosolic DNA-sensing pathway, which plays a crucial role in innate immunity and viral defense, and the RIG-I-like receptor (RLR) signaling pathway, which detects viral infections and activates immune responses. Pathways related to various viral infections were also identified. It is important to note that our study relied on online databases. Given that ESCA consists of two distinct subgroups (ESCC and EAC), most databases combine them into a single category. Future research should focus on evaluating IFI6 expression and its impact on each subgroup to gain more specific insights. In conclusion, inhibiting IFI6 using targeted therapy could be an effective strategy for treating ESCA considering its potential as a biomarker and correlation with immune cell factors.

Leveraging transformers-based language models in proteome bioinformatics.

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.