Glutathione kinetically outcompetes reactions between dimedone and a cyclic sulfenamide or physiological sulfenic acids

Dimedone and its derivates are used as selective probes for the nucleophilic detection of sulfenic acids in biological samples. Qualitative analyses suggested that dimedone also reacts with cyclic sulfenamides. Furthermore, under physiological conditions, dimedone must compete with the highly concentrated nucleophile glutathione. We therefore quantified the reaction kinetics for a cyclic sulfenamide model peptide and the sulfenic acids of glutathione and a model peroxiredoxin in the presence or absence of dimedone and glutathione. We show that the cyclic sulfenamide is stabilized at lower pH and that it reacts with dimedone. While reactions between dimedone and sulfenic acids or the cyclic sulfenamide have similar rate constants, glutathione kinetically outcompetes dimedone as a nucleophile by several orders of magnitude. Our comparative in vitro and intracellular analyses challenge the selectivity of dimedone. Consequently, the dimedone labeling of cysteinyl residues inside living cells points towards unidentified reaction pathways or unknown, kinetically competitive redox species.

BiGRUD-SA: Protein S-sulfenylation sites prediction based on BiGRU and self-attention

S-sulfenylation is a vital post-translational modification (PTM) of proteins, which is an intermediate in other redox reactions and has implications for signal transduction and protein function regulation. However, there are many restrictions on the experimental identification of S-sulfenylation sites. Therefore, predicting S-sulfoylation sites by computational methods is fundamental to studying protein function and related biological mechanisms. In this paper, we propose a method named BiGRUD-SA based on bi-directional gated recurrent unit (BiGRU) and self-attention mechanism to predict protein S-sulfenylation sites. We first use AAC, BLOSUM62, AAindex, EAAC and GAAC to extract features, and do feature fusion to obtain original feature space. Next, we use SMOTE-Tomek method to handle data imbalance. Then, we input the processed data to the BiGRU and use self-attention mechanism to do further feature extraction. Finally, we input the data obtained to the deep neural networks (DNN) to identify S-sulfenylation sites. The accuracies of training set and independent test set are 96.66% and 95.91% respectively, which indicates that our method is conducive to identifying S-sulfenylation sites. Furthermore, we use a data set of S-sulfenylation sites in Arabidopsis thaliana to effectively verify the generalization ability of BiGRUD-SA method, and obtain better prediction results.

GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs

Versatile RNA modifications play important roles in post-transcriptional regulations of gene expression, among which glycosylation modifications on small RNAs emerge as a novel clade whose characteristics need further interrogations. Here, we demonstrated that the sequence pattern around RNA glycosylation sites was not random and could be exploited for glycosylation site prediction. A machine learning predictor, GlyinsRNA, which integrated multiple RNA sequence representation encodings, was established. GlyinsRNA achieved AUROC (area under the receiver operating characteristic curve) of 0.7933 and 0.7979 in five-fold cross-validation and independent tests, respectively. GlyinsRNA was implemented as an online webserver, where both the predicted glycosylation sites and the overrepresented RNA-binding protein (RBP)-related motifs were annotated to facilitate the users. GlyinsRNA webserver is freely available at http://www.rnanut.net/glyinsrna.

DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder

BACKGROUND

S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine.

OBJECTIVE

In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites.

METHODS

In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via nSegmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2D-Convolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication.

RESULTS

Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies.

CONCLUSION

In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

DeepCSO: A Deep-Learning Network Approach to Predicting Cysteine S-Sulphenylation Sites

Cysteine S-sulphenylation (CSO), as a novel post-translational modification (PTM), has emerged as a potential mechanism to regulate protein functions and affect signal networks. Because of its functional significance, several prediction approaches have been developed. Nevertheless, they are based on a limited dataset from Homo sapiens and there is a lack of prediction tools for the CSO sites of other species. Recently, this modification has been investigated at the proteomics scale for a few species and the number of identified CSO sites has significantly increased. Thus, it is essential to explore the characteristics of this modification across different species and construct prediction models with better performances based on the enlarged dataset. In this study, we constructed several classifiers and found that the long short-term memory model with the word-embedding encoding approach, dubbed LSTM_WE, performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the receiver operating characteristic (ROC) curve for LSTM_WE ranged from 0.82 to 0.85 for different organisms, which was superior to the reported CSO predictors. Moreover, we developed the general model based on the integrated data from different species and it showed great universality and effectiveness. We provided the on-line prediction service called DeepCSO that included both species-specific and general models, which is accessible through http://www.bioinfogo.org/DeepCSO.

Using deep neural networks and biological subwords to detect protein S-sulfenylation sites

Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature. Identifying S-sulfenylation sites, therefore, could be the key to decipher its mysterious structures and functions, which are important in cell biology and diseases. However, due to the lack of effective methods, scientists in this field tend to be limited in merely a handful of some wet lab techniques that are time-consuming and not cost-effective. Thus, this motivated us to develop an in silico model for detecting S-sulfenylation sites only from protein sequence information. In this study, protein sequences served as natural language sentences comprising biological subwords. The deep neural network was consequentially employed to perform classification. The performance statistics within the independent dataset including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve rates achieved 85.71%, 69.47%, 77.09%, 0.5554 and 0.833, respectively. Our results suggested that the proposed method (fastSulf-DNN) achieved excellent performance in predicting S-sulfenylation sites compared to other well-known tools on a benchmark dataset.

DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization

Due to rising development costs and stagnant product outputs of traditional drug discovery methods, drug repositioning, which discovers new indications for existing drugs, has attracted increasing interest. Computational drug repositioning can integrate prioritization information and accelerate time lines even further. However, most existing methods for predicting drug repositioning have low precisions. The present article proposed a new method named DDAPRED (https://github.com/nongdaxiaofeng/DDAPRED) for drug repositioning prediction. The method integrated multiple sources of drug similarity and disease similarity information, and it used the regularized logistic matrix decomposition method to significantly improve the prediction performance. In 5-fold cross-validation, the areas under the receiver operating characteristic curve (AUROC) and the precision-recall curve (AUPRC) of DDAPRED reached 0.932 and 0.438, respectively, exceeding other methods. The present study also analyzed the parameters influencing the model performance and the effect of different drug similarity information in-depth, and it verified the treatment relationship of the top 50 predictions with unknown relationships in the training set, further demonstrating the practicability of our method.

SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

BACKGROUND

S-sulphenylation is a ubiquitous protein post-translational modification (PTM) where an S-hydroxyl (-SOH) bond is formed via the reversible oxidation on the Sulfhydryl group of cysteine (C). Recent experimental studies have revealed that S-sulphenylation plays critical roles in many biological functions, such as protein regulation and cell signaling. State-of-the-art bioinformatic advances have facilitated high-throughput in silico screening of protein S-sulphenylation sites, thereby significantly reducing the time and labour costs traditionally required for the experimental investigation of S-sulphenylation.

RESULTS

In this study, we have proposed a novel hybrid computational framework, termed SIMLIN, for accurate prediction of protein S-sulphenylation sites using a multi-stage neural-network based ensemble-learning model integrating both protein sequence derived and protein structural features. Benchmarking experiments against the current state-of-the-art predictors for S-sulphenylation demonstrated that SIMLIN delivered competitive prediction performance. The empirical studies on the independent testing dataset demonstrated that SIMLIN achieved 88.0% prediction accuracy and an AUC score of 0.82, which outperforms currently existing methods.

CONCLUSIONS

In summary, SIMLIN predicts human S-sulphenylation sites with high accuracy thereby facilitating biological hypothesis generation and experimental validation. The web server, datasets, and online instructions are freely available at http://simlin.erc.monash.edu/ for academic purposes.

PACES: prediction of N4-acetylcytidine (ac4C) modification sites in mRNA

N4-acetylcytidine (ac4C) is a highly conserved RNA modification and is the first acetylation event described in mRNA. ac4C in mRNA has been demonstrated to be involved in the regulation of mRNA stability, processing and translation, but the exact means by which ac4C works remain unclear. In addition, ac4C is widely distributed within the human transcriptome at physiologically relevant levels and so far only a small fraction of modified sequences have been detected by experiments. In this study, we developed a predictor of ac4C sites in human mRNA named PACES to help mining possible modified motifs. PACES combines two random forest classifiers, position-specific dinucleotide sequence profile and K-nucleotide frequencies. With genomic sequences as input, PACES gives possible modified sequences based on the training model. PACES is freely available at http://www.rnanut.net/paces/.

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Protein fold recognition based on multi-view modeling

Motivation

Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem.

Results

In this study, we proposed two algorithms: MV-fold and MT-fold. MV-fold is a new computational predictor based on the multi-view learning model for fold recognition. Different features of proteins were treated as different views of proteins, including the evolutionary information, secondary structure information and physicochemical properties. These different views constituted the latent space. The ε-dragging technique was employed to enlarge the margins between different protein folds, improving the predictive performance of MV-fold. Then, MV-fold was combined with two template-based methods: HHblits and HMMER. The ensemble method is called MT-fold incorporating the advantages of both discriminative methods and template-based methods. Experimental results on five widely used benchmark datasets (DD, RDD, EDD, TG and LE) showed that the proposed methods outperformed some state-of-the-art methods in this field, indicating that MV-fold and MT-fold are useful computational tools for protein fold recognition and protein homology detection and would be efficient tools for protein sequence analysis. Finally, we constructed an update and rigorous benchmark dataset based on SCOPe (version 2.07) to fairly evaluate the performance of the proposed method, and our method achieved stable performance on this new dataset. This new benchmark dataset will become a widely used benchmark dataset to fairly evaluate the performance of different methods for fold recognition.

Supplementary information

Supplementary data are available at Bioinformatics online.

Prediction of S-nitrosylation sites by integrating support vector machines and random forest

Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes.

Research progress in protein posttranslational modification site prediction

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

HITS-PR-HHblits: protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search

As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits, by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis.

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites

Protein S-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew’s correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.

AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest

The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction

Background

The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.

Results

In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI).

Conclusions

The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.

RFAthM6A: a new tool for predicting m6A sites in Arabidopsis thaliana

We curated a reliable dataset of m⁶A sites in Arabidopsis thaliana, built competitive models for predicting m⁶A sites, extracted predominant rules from the prediction models and analyzed the most important features. In biological RNA, approximately 150 chemical modifications have been discovered, of which N⁶-methyladenine (m⁶A) is the most prevalent and abundant. This modification plays an essential role in a myriad of biological mechanisms and regulates RNA localization, nuclear export, translation, stability, alternative splicing, and other processes. However, m⁶A-seq and other wet-lab techniques do not easily facilitate accurate and complete determination of m⁶A sites across the transcriptome. Therefore, the use of computational methods to establish accurate models for predicting m⁶A sites is essential. In this work, we manually curated a reliable dataset of m⁶A sites and non-m⁶A sites and developed a new tool called RFAthM6A for predicting m⁶A sites in Arabidopsis thaliana. Briefly, RFAthM6A consists of four independent models named RFPSNSP, RFPSDSP, RFKSNPF and RFKNF and strict benchmarks show that the AUC values of the four models reached 0.894, 0.914, 0.920 and 0.926, respectively in a fivefold cross validation and the prediction performance of RFPSDSP, RFKSNPF and RFKNF exceeded that of three previously reported models (AthMethPre, M6ATH and RAM-NPPS). Linear combination of the prediction scores of RFPSDSP, RFKSNPF and RFKNF improved the prediction performance. We also extracted several predominant rules that underlie the m⁶A site identification from the trained models. Furthermore, the most important features of the predictors for the m⁶A site identification were also analyzed in depth. To facilitate use of our proposed models by interested researchers, all the source codes and datasets are publicly deposited at https://github.com/nongdaxiaofeng/RFAthM6A .

PredCSO: an ensemble method for the prediction of S-sulfenylation sites in proteins

Predicting S-sulfenylation sites in proteins based on sequence and structural features by building an ensemble model by gradient tree boosting.

Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information

Cysteine S-sulfenylation is a major type of posttranslational modification that contributes to protein structure and function regulation in many cellular processes.

iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines

An efficient multi-label classifier for identifying human phosphorylated proteins has been developed by introducing multiple kernel learning based support vector machines.