Prediction of Lysine Malonylation Sites Based on Pseudo Amino Acid

Xiang, Qilin; Kong, Feng; Liao, Bo; Liu, Yuewu; Huang, Guo‐Hua

doi:10.2174/1386207320666170314102647

Cited by 29 publications

(11 citation statements)

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“…As one of the most widely used ML algorithms applied to classification problems [11,13,14,28,35,37,39,42,53], SVM [54] maps the input data into a high-dimensional space through the use of kernel functions and finds a hyperplane that maximizes the distance between the hyperplane and two types of samples. By mapping the unseen samples into the same space, SVM can predict the new samples based on which side of the hyperplane they fall in.…”

Section: Support Vector Machinementioning

confidence: 99%

“…However, wet-laboratory experimental validations are often time-consuming and cost-prohibitive. Recently, several computational methods (summarized in Table S1) have been introduced to predict malonylation sites based on machine learning (ML) models [11][12][13][14][15]. Xu et al [11] developed the 1st computational method, Mal-Lys, to predict Kmal sites based on protein sequences using data from [10].…”

Section: Introductionmentioning

confidence: 99%

“…This work also confirmed that different species have different biological processes and pathways and that their enzymes have unique sequence preferences, making it suitable for training with and to predict Kmal sites separately for individual species. Xiang et al [14] proposed a computational method to predict malonylation sites by training SVM models with the pseudo amino acid composition encoding scheme. Their method achieved a reasonable performance based on a relatively smallscale benchmark test.…”

Section: Introductionmentioning

confidence: 99%

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Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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show abstract

“…There has been an increasing interest in computational methods for predicting PTM sites in protein sequences [15][16][17][18][19][20][21][22][23][24][25]. It is because the experimental procedures for identifying PTM sites based in laboratories have demonstrated to be time-consuming, inefficient and a costly endeavor [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction

et al. 2019

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Background: Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate. Results: We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid's local structure conformations and profile bigrams of position-specific scoring matrices.

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“…Three separate models were developed for three species ( Escherichia coli , Mus musculus and Homo sapiens ). Xiang et al predicted malonylation sites by employing pseudo amino acid compositions to train the SVM model . Du et al further employed function annotation features for predicting sites of lysine acylation including malonylation …”

Section: Introductionmentioning

confidence: 99%

Predicting lysine‐malonylation sites of proteins using sequence and predicted structural features

Taherzadeh

Yang

et al. 2018

J Comput Chem

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Malonylation is a recently discovered post-translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT-Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half-sphere exposure provides additional improvement to the prediction performance. SPRINT-Mal trained on mouse data yields robust performance for 10-fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews' Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT-Mal achieved comparable performance when testing on H. sapiens proteins without species-specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT-Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc.

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Prediction of Lysine Malonylation Sites Based on Pseudo Amino Acid

Cited by 29 publications

References 1 publication

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction

Predicting lysine‐malonylation sites of proteins using sequence and predicted structural features

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