ProAcePred: prokaryote lysine acetylation sites prediction based on elastic net feature optimization

Chen, Guodong; Cao, Man; Luo, Keming; Wang, Lina; Wen, Pingping; Shi, Shengli

doi:10.1093/bioinformatics/bty444

Cited by 32 publications

(24 citation statements)

References 51 publications

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“…In previous studies, different models had their own window sizes to maximize the efficiency of the model. Wang et al [4] predicted Kace sites with a window size of L = 33, Wu et al [39] used L = 31, Suo et al [18] used L = 21, and other researchers used 19 [15], 21 [17], etc. To select the optimal Kace sites window size for MDC-Kace and ensure that the amount of input information is sufficient, especially to make full use of the characteristics of the modular densely connected convolutional networks can extract features efficiently.…”

Section: Results and Discussion A Selection Of The Kace Site Wmentioning

confidence: 99%

“…Since many models use different training data and do not provide independent tools, it is difficult to make direct comparisons. We selected seven available and representative models, namely MusiteDeep [27], CapsNet [4], DeepAcet [39], PSKAcePred [18], EnsemblePail [15], GPS-PAIL2.0 [7] and ProAcePred [17] for the experiments. MusiteDeep, which was originally designed as a predictor of phosphorylation sites [27], adopts CNNs and attention mechanisms.…”

Section: B Evaluation Of Mdc-kace Prediction Abilitymentioning

confidence: 99%

“…GPS-PAIL2.0 adopted an algorithm of Group-Based Prediction System (GPS) to predict specific acetylation sites of lysine acetyltransferases [7]. ProAcePred predicted nine species of prokaryote lysine acetylation sites based on the combination of sequence-based, physicochemical property and evolutionary information features via elastic net [17]. Since ProAcePred is a prokaryotespecific lysine acetylation site predictor [17], we evaluated it on only the Escherichia coli dataset.…”

Section: B Evaluation Of Mdc-kace Prediction Abilitymentioning

confidence: 99%

“…Some studies have shown that metabolic process can be controlled through using drugs to modify lysine acetylation (Kace), which can alter the activities of a series of related enzymes in human body, thereby effectively preventing and treating metabolic diseases [8]- [10]. Thus, the identification of Kace sites and their substrates is of great significance to understand the potential molecular mechanisms of acetylation modification [14], and it also provides useful site modification information for the design of drugs for the treatments of related metabolic diseases [11], [12], [17].…”

Section: Introductionmentioning

confidence: 99%

“…To address the uniqueness of original protein sequence information, Lee et al [13] increased the solvent accessibility and amino acid physicochemical property information and achieved a higher accuracy than Li et al [12]. Chen et al [17] used the original protein sequence, amino acid physicochemical properties and protein evolution information to predict Kace sites of prokaryotes and achieved better results than previous studies. Suo et al [18] combined the amino acid composition, protein evolutionary similarity, and amino acid physicochemical properties, which greatly improved the prediction accuracy of Kace sites.…”

Section: Introductionmentioning

confidence: 99%

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MDC-Kace: A Model for Predicting Lysine Acetylation Sites Based on Modular Densely Connected Convolutional Networks

et al. 2020

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Lysine acetylation (Kace) is a conservative protein posttranslational modification (PTM) closely related to various metabolic diseases. Therefore, Kace sites identification is of great significance for investigating metabolic disease treatments. Existing studies have shown that protein structural properties contain highly useful structural information, which provides a strong basis for identifying PTMs. During the feature learning process, features at different levels are complementary, and taking them into consideration can effectively improve the quality of the features. However, existing deep-learning methods used protein sequence-level information as input without considering the protein structural properties. Furthermore, only high-level features were focused on, resulting in considerable information loss and weakening the prediction results. Therefore, we propose a novel deep-learning model based on modular densely connected convolutional networks (MDC) for Kace sites prediction, called MDC-Kace. MDC-Kace introduces the protein structural properties and combines them with the original protein sequence and the amino acid physicochemical properties to construct the site's feature space. Then, modular densely connected convolutional networks are used to capture the information of features at different levels and reduce information loss and crosstalk during the feature learning step. We add a squeeze-excitation layer to evaluate the importance of different features and improve the network abstraction ability to identify potential Kace sites. The experimental results of tenfold cross-validation and independent testing in human, Mus musculus and Escherichia coli datasets showed that our MDC-Kace model outperforms the existing Kace site predictors and can predict potential Kace sites effectively. MDC-Kace can be available at https://github.com/lianglianggg/MDC-Kace. INDEX TERMS Lysine acetylation, protein structure properties, densely connected convolutional networks, modular network structure, feature learning.

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