Gly-PseAAC: Identifying protein lysine glycation through sequences

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

A Mechanism of Action of Metformin in the Brain: Prevention of Methylglyoxal-Induced Glutamatergic Impairment in Acute Hippocampal Slices

Metformin, a biguanide compound (N-1,1-dimethylbiguanide), is widely prescribed for diabetes mellitus type 2 (T2D) treatment. It also presents a plethora of properties, such as anti-oxidant, anti-inflammatory, anti-apoptosis, anti-tumorigenic, and anti-AGE formation activity. However, the precise mechanism of action of metformin in the central nervous system (CNS) needs to be clarified. Herein, we investigated the neuroprotective role of metformin in acute hippocampal slices exposed to methylglyoxal (MG), a highly reactive dicarbonyl compound and a key molecule in T2D developmental pathophysiology. Metformin protected acute hippocampal slices from MG-induced glutamatergic neurotoxicity and neuroinflammation by reducing IL-1β synthesis and secretion and RAGE protein expression. The drug also improved astrocyte function, particularly with regard to the glutamatergic system, increasing glutamate uptake. Moreover, we observed a direct effect of metformin on glutamate transporters, where the compound prevented glycation, by facilitating enzymatic phosphorylation close to Lys residues, suggesting a new neuroprotective role of metformin via PKC ζ in preventing dysfunction in glutamatergic system induced by MG.

iGly-IDN: Identifying Lysine Glycation Sites in Proteins Based on Improved DenseNet

Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet). First, one hot encoding was adopted to obtain the original feature maps. Afterward, the improved DenseNet was adopted to capture feature information with the importance degrees during the feature learning. According to the experimental results, Acc reaches 66%, and Mathews correlation coefficient reaches 0.33 on the independent testing data set, which indicates that the iGly-IDN can provide more effective glycation site identification than the current predictors.

Unambiguous Identification of Glucose-Induced Glycation in mAbs and other Proteins by NMR Spectroscopy

OBJECTIVE

Glycation is a non-enzymatic and spontaneous post-translational modification (PTM) generated by the reaction between reducing sugars and primary amine groups within proteins. Because glycation can alter the properties of proteins, it is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) and should therefore be carefully monitored. The most abundant product of glycation is formed by glucose and lysine side chains resulting in fructoselysine after Amadori rearrangement. In proteomics, which routinely uses a combination of chromatography and mass spectrometry to analyze PTMs, there is no straight-forward way to distinguish between glycation products of a reducing monosaccharide and an additional hexose within a glycan, since both lead to a mass difference of 162 Da.

METHODS

To verify that the observed mass change is indeed a glycation product, we developed an approach based on 2D NMR spectroscopy spectroscopy and full-length protein samples denatured using high concentrations of deuterated urea.

RESULTS

The dominating β-pyranose form of the Amadori product shows a characteristic chemical shift correlation pattern in 1H-13C HSQC spectra suited to identify glucose-induced glycation. The same pattern was observed in spectra of a variety of artificially glycated proteins, including two mAbs, as well as natural proteins.

CONCLUSION

Based on this unique correlation pattern, 2D NMR spectroscopy can be used to unambiguously identify glucose-induced glycation in any protein of interest. We provide a robust method that is orthogonal to MS-based methods and can also be used for cross-validation.

On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks

One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high-quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine-containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.

A systematic review of recent trends in research on therapeutically significant L-asparaginase and acute lymphoblastic leukemia

L-asparaginases are mostly obtained from bacterial sources for their application in the therapy and food industry. Bacterial L-asparaginases are employed in the treatment of Acute Lymphoblastic Leukemia (ALL) and its subtypes, a type of blood and bone marrow cancer that results in the overproduction of immature blood cells. It also plays a role in the food industry in reducing the acrylamide formed during baking, roasting, and frying starchy foods. This importance of the enzyme makes it to be of constant interest to the researchers to isolate novel sources. Presently L-asparaginases from E. coli native and PEGylated form, Dickeya chrysanthemi (Erwinia chrysanthemi) are in the treatment regime. In therapy, the intrinsic glutaminase activity of the enzyme is a major drawback as the patients in treatment experience side effects like fever, skin rashes, anaphylaxis, pancreatitis, steatosis in the liver, and many complications. Its significance in the food industry in mitigating acrylamide is also a major reason. Acrylamide, a potent carcinogen was formed when treating starchy foods at higher temperatures. Acrylamide content in food was analyzed and pre-treatment was considered a valuable option. Immobilization of the enzyme is an advancing and promising technique in the effective delivery of the enzyme than in free form. The concept of machine learning by employing the Artificial Network and Genetic Algorithm has paved the way to optimize the production of L-asparaginase from its sources. Gene-editing tools are gaining momentum in the study of several diseases and this review focuses on the CRISPR-Cas9 gene-editing tool in ALL.

BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for Homo sapiens

As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models.

iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features

Posttranslational modification (PTM) is an important biological mechanism to promote functional diversity among the proteins. So far, a wide range of PTMs has been identified. Among them, glycation is considered as one of the most important PTMs. Glycation is associated with different neurological disorders including Parkinson and Alzheimer. It is also shown to be responsible for different diseases, including vascular complications of diabetes mellitus. Despite all the efforts have been made so far, the prediction performance of glycation sites using computational methods remains limited. Here we present a newly developed machine learning tool called iProtGly-SS that utilizes sequential and structural information as well as Support Vector Machine (SVM) classifier to enhance lysine glycation site prediction accuracy. The performance of iProtGly-SS was investigated using the three most popular benchmarks used for this task. Our results demonstrate that iProtGly-SS is able to achieve 81.61%, 93.62%, and 92.95% prediction accuracies on these benchmarks, which are significantly better than those results reported in the previous studies. iProtGly-SS is implemented as a web-based tool which is publicly available at http://brl.uiu.ac.bd/iprotgly-ss/ .

βLact-Pred: A Predictor Developed for Identification of Beta-Lactamases Using Statistical Moments and PseAAC via 5-Step Rule

Beta-lactamase (β-lactamase) produced by different bacteria confers resistance against β-lactam-containing drugs. The gene encoding β-lactamase is plasmid-borne and can easily be transferred from one bacterium to another during conjugation. By such transformations, the recipient also acquires resistance against the drugs of the β-lactam family. β-Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis. Herein, we report a prediction classifier named as βLact-Pred for the identification of β-lactamase proteins. The computational model uses the primary amino acid sequence structure as its input. Various metrics are derived from the primary structure to form a feature vector. Experimentally determined data of positive and negative beta-lactamases are collected and transformed into feature vectors. An operating algorithm based on the artificial neural network is used by integrating the position relative features and sequence statistical moments in PseAAC for training the neural networks. The results for the proposed computational model were validated by employing numerous types of approach, i.e., self-consistency testing, jackknife testing, cross-validation, and independent testing. The overall accuracy of the predictor for self-consistency, jackknife testing, cross-validation, and independent testing presents 99.76%, 96.07%, 94.20%, and 91.65%, respectively, for the proposed model. Stupendous experimental results demonstrated that the proposed predictor “βLact-Pred” has surpassed results from the existing methods.

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.

Synergistic sequence contributions bias glycation outcomes

The methylglyoxal-derived hydroimidazolone isomer, MGH-1, is an abundant advanced glycation end-product (AGE) associated with disease and age-related disorders. As AGE formation occurs spontaneously and without an enzyme, it remains unknown why certain sites on distinct proteins become modified with specific AGEs. Here, we use a combinatorial peptide library to determine the chemical features that favor MGH-1. When properly positioned, tyrosine is found to play an active mechanistic role that facilitates MGH-1 formation. This work offers mechanistic insight connecting multiple AGEs, including MGH-1 and carboxyethylarginine (CEA), and reconciles the role of negative charge in influencing glycation outcomes. Further, this study provides clear evidence that glycation outcomes can be influenced through long- or medium-range cooperative interactions. This work demonstrates that these chemical features also predictably template selective glycation on full-length protein targets expressed in mammalian cells. This information is vital for developing methods that control glycation in living cells and will enable the study of glycation as a functional post-translational modification.

Advanced glycation end-products (AGEs), such as methylglyoxal-derived hydroimidazolone isomer (MGH-1), are associated with disease and age-related disorders, and occur spontaneously, so it is unclear why specific protein sites become modified with specific AGEs. Here, the authors use a combinatorial peptide library to determine the chemical features that favour MGH-1 formation for short peptides and demonstrate a key role of tyrosine in this process.

A comprehensive review of the imbalance classification of protein post-translational modifications

Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.

Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

BACKGROUND

Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.

METHOD

We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.

RESULTS

In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN .

CONCLUSIONS

The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

ABC-Gly: Identifying Protein Lysine Glycation Sites with Artificial Bee Colony Algorithm

Background:

Glycation is a nonenzymatic post-translational modification process by attaching a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular mechanisms of glycation, computational prediction methods have been developed because of their convenience and high speed. However, a more effective computational tool is still a challenging task in computational biology.

Methods:

In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine glycation sites. At first, we utilized three informative features, including position-specific amino acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and generalization ability of the model, we developed a two-step feature selection, which combined the Fisher score and an improved binary artificial bee colony algorithm based on the support vector machine. Finally, based on the optimal feature subset, we constructed an effective model by using the Support Vector Machine on the training dataset.

Results:

The performance of the proposed predictor ABC-Gly was measured with the sensitivity of 76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the Area Under the receiveroperating characteristic Curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of 0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor achieved significant improvement in the AUC of 0.156 and MCC of 0.336.

Conclusion:

The detailed analysis results indicated that our predictor may serve as a powerful complementary tool to other existing methods for predicting protein lysine glycation. The source code and datasets of the ABC-Gly were provided in the Supplementary File 1.

Prediction of Neddylation Sites Using the Composition of k-spaced Amino Acid Pairs and Fuzzy SVM

Introduction:

Neddylation is the process of ubiquitin-like protein NEDD8 attaching substrate lysine via isopeptide bonds. As a highly dynamic and reversible post-translational modification, lysine neddylation has been found to be involved in various biological processes and closely associated with many diseases.

Objective:

The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As traditional experimental methods are often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites.

Methods:

In this study, a novel predictor named CKSAAP_NeddSite is developed to detect neddylation sites. An effective feature encoding technology, the composition of k-spaced amino acid pairs, is used to encode neddylation sites. And the F-score feature selection method is adopted to remove the redundant features. Moreover, a fuzzy support vector machine algorithm is employed to overcome the class imbalance and noise problem.

Results:

As illustrated by 10-fold cross-validation, CKSAAP_NeddSite achieves an AUC of 0.9848. Independent tests also show that CKSAAP_NeddSite significantly outperforms existing neddylation sites predictor. Therefore, CKSAAP_NeddSite can be a useful bioinformatics tool for the prediction of neddylation sites. Feature analysis shows that some residues around neddylation sites may play an important role in the prediction.

Conclusion:

The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of neddylation. A user-friendly web-server for CKSAAP_NeddSite is established at 123.206.31.171/CKSAAP_NeddSite.

Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components

Introduction

Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation.

Objective

As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites.

Methods

In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction.

Results

Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy.

Conclusion

Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate

The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deamidation, using the random decision forest method. We show that our categorical model predicts antibody deamidation with nearly 5% increased accuracy and 0.2 MCC over the best currently available models. Surprisingly, our model also paces or outperforms advanced and conventional models on an independent non-antibody dataset. In addition to deamidation probability, we are able to accurately predict deamidation rate (R² = 0.963 and Q2 = 0.822), a capability with no peer in current models. This method should enable significant improvement in protein candidate selection, especially in biopharmaceutical development, and can be applied with similar accuracy to enzymes, monoclonal antibodies, next-generation formats, vaccine component antigens, and gene therapy vectors such as adeno-associated virus.

The Oncogenic Action of NRF2 Depends on De-glycation by Fructosamine-3-Kinase

The NRF2 transcription factor controls a cell stress program that is implicated in cancer and there is great interest in targeting NRF2 for therapy. We show that NRF2 activity depends on Fructosamine-3-kinase (FN3K)-a kinase that triggers protein de-glycation. In its absence, NRF2 is extensively glycated, unstable, and defective at binding to small MAF proteins and transcriptional activation. Moreover, the development of hepatocellular carcinoma triggered by MYC and Keap1 inactivation depends on FN3K in vivo. N-acetyl cysteine treatment partially rescues the effects of FN3K loss on NRF2 driven tumor phenotypes indicating a key role for NRF2-mediated redox balance. Mass spectrometry reveals that other proteins undergo FN3K-sensitive glycation, including translation factors, heat shock proteins, and histones. How glycation affects their functions remains to be defined. In summary, our study reveals a surprising role for the glycation of cellular proteins and implicates FN3K as targetable modulator of NRF2 activity in cancer.

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design.

GlyStruct: glycation prediction using structural properties of amino acid residues

Background

Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs.

Results

We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew’s correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold cross-validation.

Conclusion

Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and non-glycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2547-x) contains supplementary material, which is available to authorized users.

PredGly: predicting lysine glycation sites for Homo sapiens based on XGboost feature optimization

Motivation

Protein glycation is a familiar post-translational modification (PTM) which is a two-step non-enzymatic reaction. Glycation not only impairs the function but also changes the characteristics of the proteins so that it is related to many human diseases. It is still much more difficult to systematically detect glycation sites due to the glycated residues without crucial patterns. Computational approaches, which can filter supposed sites prior to experimental verification, can extremely increase the efficiency of experiment work. However, the previous lysine glycation prediction method uses a small number of training datasets. Hence, the model is not generalized or pervasive.

Results

By searching from a new database, we collected a large dataset in Homo sapiens. PredGly, a novel software, can predict lysine glycation sites for H.sapiens, which was developed by combining multiple features. In addition, XGboost was adopted to optimize feature vectors and to improve the model performance. Through comparing various classifiers, support vector machine achieved an optimal performance. On the basis of a new independent test set, PredGly outperformed other glycation tools. It suggests that PredGly can provide more instructive guidance for further experimental research of lysine glycation.

Availability and implementation

https://github.com/yujialinncu/PredGly

Supplementary information

Supplementary data are available at Bioinformatics online.

SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure

Post Translational Modification (PTM) is defined as the modification of amino acids along the protein sequences after the translation process. These modifications significantly impact on the functioning of proteins. Therefore, having a comprehensive understanding of the underlying mechanism of PTMs turns out to be critical in studying the biological roles of proteins. Among a wide range of PTMs, sumoylation is one of the most important modifications due to its known cellular functions which include transcriptional regulation, protein stability, and protein subcellular localization. Despite its importance, determining sumoylation sites via experimental methods is time-consuming and costly. This has led to a great demand for the development of fast computational methods able to accurately determine sumoylation sites in proteins. In this study, we present a new machine learning-based method for predicting sumoylation sites called SumSec. To do this, we employed the predicted secondary structure of amino acids to extract two types of structural features from neighboring amino acids along the protein sequence which has never been used for this task. As a result, our proposed method is able to enhance the sumoylation site prediction task, outperforming previously proposed methods in the literature. SumSec demonstrated high sensitivity (0.91), accuracy (0.94) and MCC (0.88). The prediction accuracy achieved in this study is 21% better than those reported in previous studies. The script and extracted features are publicly available at: https://github.com/YosvanyLopez/SumSec.

iProtGly-SS: Identifying protein glycation sites using sequence and structure based features

Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly-SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.

Proteomic biomarkers for lung cancer progression

Aim: Lung adenocarcinoma (LUAD) and lung squamous-cell carcinoma (LUSC) are two major subtypes of lung cancer and constitute about 70% of all the lung cancer cases. The patient's lifespan and living quality will be significantly improved if they are diagnosed at an early stage and adequately treated. Methods & results: This study comprehensively screened the proteomic dataset of both LUAD and LUSC, and proposed classification models for the progression stages of LUAD and LUSC with accuracies 86.51 and 89.47%, respectively. Discussion & conclusion: A comparative analysis was also carried out on related transcriptomic datasets, which indicates that the proposed biomarkers provide discerning power for accurate stage prediction, and will be improved when larger-scale proteomic quantitative technologies become available.

Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine

Glycation is a non-enzymatic process occurring inside or outside the host body by attaching a sugar molecule to a protein or lipid molecule. It is an important form of post-translational modification (PTM), which impairs the function and changes the characteristics of the proteins so that the identification of the glycation sites may provide some useful guidelines to understand various biological functions of proteins. In this study, we proposed an accurate prediction tool, named Glypre, for lysine glycation. Firstly, we used multiple informative features to encode the peptides. These features included the position scoring function, secondary structure, AAindex, and the composition of k-spaced amino acid pairs. Secondly, the distribution of distinctive features of the residues surrounding the glycation and non-glycation sites was statistically analysed. Thirdly, based on the distribution of these features, we developed a new predictor by using different optimal window sizes for different properties and a two-step feature selection method, which utilized the maximum relevance minimum redundancy method followed by a greedy feature selection procedure. The performance of Glypre was measured with a sensitivity of 57.47%, a specificity of 90.78%, an accuracy of 79.68%, area under the receiver-operating characteristic (ROC) curve (AUC) of 0.86, and a Matthews’s correlation coefficient (MCC) of 0.52 by 10-fold cross-validation. The detailed analysis results showed that our predictor may play a complementary role to other existing methods for identifying protein lysine glycation. The source code and datasets of the Glypre are available in the Supplementary File.

Predicting lysine glycation sites using bi-profile bayes feature extraction

Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to understand the underlying molecular mechanisms of glycation. As the traditional experimental methods are often labor-intensive and time-consuming, it is desired to develop computational methods to predict glycation sites. In this study, a novel predictor named BPB_GlySite is proposed to predict lysine glycation sites by using bi-profile bayes feature extraction and support vector machine algorithm. As illustrated by 10-fold cross-validation, BPB_GlySite achieves a satisfactory performance with a Sensitivity of 63.68%, a Specificity of 72.60%, an Accuracy of 69.63% and a Matthew's correlation coefficient of 0.3499. Experimental results also indicate that BPB_GlySite significantly outperforms three existing glycation sites predictors: NetGlycate, PreGly and Gly-PseAAC. Therefore, BPB_GlySite can be a useful bioinformatics tool for the prediction of glycation sites. A user-friendly web-server for BPB_GlySite is established at 123.206.31.171/BPB_GlySite/.