iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC

Ju, Zhe; Cao, Junzhe; Gu, Hong

doi:10.1016/j.jtbi.2015.07.030

Cited by 27 publications

(6 citation statements)

References 60 publications

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“…In this study, the composition of the k -spaced amino acid pairs (CKSAAP)-based encoding scheme was applied to encode each sample. CKSAAP could show the association of the residues surrounding pupylation sites and it has been successfully applied to other kinds of PTM site prediction problems [ 20 , 21 , 22 ]. Taking k = 0 as an example, for a sequence fragment including 2n + 1 amino acids, there are 441 0-spaced residue pairs (i.e., AA, AC,…).…”

Section: Methodsmentioning

confidence: 99%

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites

et al. 2017

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Protein pupylation is a type of post-translation modification, which plays a crucial role in cellular function of bacterial organisms in prokaryotes. To have a better insight of the mechanisms underlying pupylation an initial, but important, step is to identify pupylation sites. To date, several computational methods have been established for the prediction of pupylation sites which usually artificially design the negative samples using the verified pupylation proteins to train the classifiers. However, if this process is not properly done it can affect the performance of the final predictor dramatically. In this work, different from previous computational methods, we proposed an enhanced positive-unlabeled learning algorithm (EPuL) to the pupylation site prediction problem, which uses only positive and unlabeled samples. Firstly, we separate the training dataset into the positive dataset and the unlabeled dataset which contains the remaining non-annotated lysine residues. Then, the EPuL algorithm is utilized to select the reliably negative initial dataset and then iteratively pick out the non-pupylation sites. The performance of the proposed method was measured with an accuracy of 90.24%, an Area Under Curve (AUC) of 0.93 and an MCC of 0.81 by 10-fold cross-validation. A user-friendly web server for predicting pupylation sites was developed and was freely available at .

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Section: Methodsmentioning

confidence: 99%

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites

et al. 2017

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“…The CKSAAP encoding has been widely used to various posttranslational modifications' site prediction [16–18]. The CKSAAP features [13, 19] with k = 0, 1, 2, 3, and 4 were used to encode each residue of lysine fragment in this study.…”

Section: Methodsmentioning

confidence: 99%

Positive-Unlabeled Learning for Pupylation Sites Prediction

Jiang

Cao

2016

BioMed Research International

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Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have not been experimentally validated yet. Unlike previous methods, in this study, the experimentally annotated pupylation sites were used as the positive training set whereas the remaining nonannotated lysine residues were used as the unlabeled training set. A novel method named PUL-PUP was proposed to predict pupylation sites by using positive-unlabeled learning technique. Our experimental results indicated that PUL-PUP outperforms the other methods significantly for the prediction of pupylation sites. As an application, PUL-PUP was also used to predict the most likely pupylation sites in nonannotated lysine sites.

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“…Since different sliding windows may have distinct prediction performances, optimization of the sliding window sizes is required for selecting features and training models. The sliding windows were considered ranging from size 9 to 23, which covers all sliding window sizes used by previous methods [10][11][12][13][14][15]24,25 about the prediction of methylation sites and methylation types. The accuracy was also employed as the performance index to evaluate the performance of different sliding window sizes.…”

Section: Analysis Of Compositional Biases Around Methylation Sites Tmentioning

confidence: 99%

“…performance on independent test sets and comparison with existing methods. Aiming to further evaluate the performance of the Met-predictor, we compared the Met-predictor with other existing methods, including MEMO 10 , MASA 11 , PLMLA 12 , PmeS 13 , MethK 14 , iLM_2L 15 and GPS-MSP 25 on two independent test sets. These tools or servers were run with their default settings.…”

Section: Does the Combination Of Sequence-based Features With Structumentioning

confidence: 99%

Two-Level Protein Methylation Prediction using structure model-based features

Zheng

Wuyun

Cheng

et al. 2020

Sci Rep

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protein methylation plays a vital role in cell processing. Many novel methods try to predict methylation sites from protein sequence by sequence information or predicted structural information, but none of them use protein tertiary structure information in prediction. In particular, most of them do not build models for predicting methylation types (mono-, di-, tri-methylation). To address these problems, we propose a novel method, Met-predictor, to predict methylation sites and methylation types using a support vector machine-based network. Met-predictor combines a variety of sequence-based features that are derived from protein sequences with structure model-based features, which are geometric information extracted from predicted protein tertiary structure models, and are firstly used in methylation prediction. Met-predictor was tested on two independent test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine and from 0.723 and 0.640 to 0.734 and 0.643 for arginine. When compared with other state-of-the-art methods, Met-predictor had 13.1% (3.9%) and 8.5% (16.4%) higher accuracy than the best of other methods for methyllysine and methylarginine prediction on the independent test set I (II). Furthermore, Met-predictor also attains excellent performance for predicting methylation types.

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iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC

Cited by 27 publications

References 60 publications

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites

EPuL: An Enhanced Positive-Unlabeled Learning Algorithm for the Prediction of Pupylation Sites

Positive-Unlabeled Learning for Pupylation Sites Prediction

Two-Level Protein Methylation Prediction using structure model-based features

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