pSuc-PseRat: Predicting Lysine Succinylation in Proteins by Exploiting the Ratios of Sequence Coupling and Properties

Ai, Haixin; Wu, Runlin; Zhang, Li; Wu, Xuewei; Ma, Jianzhong; Hu, Huan; Huang, Liangchao; Chen, Wen; Jian, Zhao; Liu, Huan

doi:10.1089/cmb.2016.0206

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…Nowadays, several machine learning-based predictors have been employed to identify succinylation sites [54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70]. The SucPred [54] is the first succinylation site predictor, which was established by Zhao et al in 2015 through different encoding descriptors, including position amino acids weight composition, van der Waals volume normalized, grouped weight-based encoding, and auto-correlation functions, via SVM.…”

Section: Existing Prediction Modelsmentioning

confidence: 99%

Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

Hasan

Khatun

Kurata

2019

Cells

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Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools.

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Section: Existing Prediction Modelsmentioning

confidence: 99%

Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

Hasan

Khatun

Kurata

2019

Cells

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“…As it was explained in the previous section, we applied 12 types of machine learning algorithms on the total and separate species that are trained using 10-fold cross-validation. Among all these algorithms, XGBoost [39], SVM [57], [60], LightGBM [73], GB [79], and MLP [80] obtained the best results. Among these classifiers, LightGBM [73] obtained the best results both for Homo sapiens and Mus musculus species.…”

Section: A Analysis Of the Results For Different Speciesmentioning

confidence: 99%

“…Performance comparison between our proposed method and MaloPred [37], kmal-sp [39] for predicting the malonylation sites of the individual species (Homo sapiens, Mus musculus) and total species (six species) based on the independent test. Extra-Trees (ET) [78], Gradient Boosting (GB) [79], and Multi-layer Perceptron (MLP) [80], [81]. Finally, we consider the LightGBM [73] as our classifier as it obtained the best results regarding all aspects compared to other classifiers.…”

Section: E Classification Algorithmmentioning

confidence: 99%

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

et al. 2020

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Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/) INDEX TERMS Cluster centroid based majority under-sampling technique, evolutionary information, light gradient boosting, lysine malonylation, machine learning, post translational modifications.

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“…We focused on the computational methods to predict succinylation. In the past decades, more than ten computational methods have been developed for identifying succinylation [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Most of these computational methods extracted features directly from protein sequences, which were subsequently used for training model.…”

Section: Introductionmentioning

confidence: 99%

LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites

Huang

Zhang

et al. 2021

BioMed Research International

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Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods were developed to deal with this challenge, few considered semantic relationship between residues. We combined long short-term memory (LSTM) and convolutional neural network (CNN) into a deep learning method for predicting succinylation site. The proposed method obtained a Matthews correlation coefficient of 0.2508 on the independent test, outperforming state of the art methods. We also performed the enrichment analysis of succinylation proteins. The results showed that functions of succinylation were conserved across species but differed to a certain extent with species. On basis of the proposed method, we developed a user-friendly web server for predicting succinylation sites.

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pSuc-PseRat: Predicting Lysine Succinylation in Proteins by Exploiting the Ratios of Sequence Coupling and Properties

Cited by 14 publications

References 31 publications

Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites

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