An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

The mechanisms, regulations, and functions of histone lysine crotonylation

Histone lysine crotonylation (Kcr) is a new acylation modification first discovered in 2011, which has important biological significance for gene expression, cell development, and disease treatment. In the past over ten years, numerous signs of progress have been made in the research on the biochemistry of Kcr modification, especially a series of Kcr modification-related "reader", "eraser", and "writer" enzyme systems are identified. The physiological function of crotonylation and its correlation with development, heredity, and spermatogenesis have been paid more and more attention. However, the development of disease is usually associated with abnormal Kcr modification. In this review, we summarized the identification of crotonylation modification, Kcr-related enzyme system, biological functions, and diseases caused by abnormal Kcr. This knowledge supplies a theoretical basis for further exploring the function of crotonylation in the future.

Functions and mechanisms of protein lysine butyrylation (Kbu): Therapeutic implications in human diseases

Post-translational modifications (PTM) are covalent modifications of proteins or peptides caused by proteolytic cleavage or the attachment of moieties to one or more amino acids. PTMs play essential roles in biological function and regulation and have been linked with several diseases. Modifications of protein acylation (Kac), a type of PTM, are known to induce epigenetic regulatory processes that promote various diseases. Thus, an increasing number of studies focusing on acylation modifications are being undertaken. Butyrylation (Kbu) is a new acylation process found in animals and plants. Kbu has been recently linked to the onset and progression of several diseases, such as cancer, cardiovascular diseases, diabetes, and vascular dementia. Moreover, the mode of action of certain drugs used in the treatment of lymphoma and colon cancer is based on the regulation of butyrylation levels, suggesting that butyrylation may play a therapeutic role in these diseases. In addition, butyrylation is also commonly involved in rice gene expression and thus plays an important role in the growth, development, and metabolism of rice. The tools and analytical methods that could be utilized for the prediction and detection of lysine butyrylation have also been investigated. This study reviews the potential role of histone Kbu, as well as the mechanisms underlying this process. It also summarizes various enzymes and analytical methods associated with Kbu, with the goal of providing new insights into the role of Kbu in gene regulation and diseases.

MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction

N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/ .

Post-Translational Modifications by Lipid Metabolites during the DNA Damage Response and Their Role in Cancer

Genomic DNA damage occurs as an inevitable consequence of exposure to harmful exogenous and endogenous agents. Therefore, the effective sensing and repair of DNA damage are essential for maintaining genomic stability and cellular homeostasis. Inappropriate responses to DNA damage can lead to genomic instability and, ultimately, cancer. Protein post-translational modifications (PTMs) are a key regulator of the DNA damage response (DDR), and recent progress in mass spectrometry analysis methods has revealed that a wide range of metabolites can serve as donors for PTMs. In this review, we will summarize how the DDR is regulated by lipid metabolite-associated PTMs, including acetylation, S-succinylation, N-myristoylation, palmitoylation, and crotonylation, and the implications for tumorigenesis. We will also discuss potential novel targets for anti-cancer drug development.

Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition

Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.

PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences

RNA–protein interactions play an indispensable role in many biological processes. Growing evidence has indicated that aberration of the RNA–protein interaction is associated with many serious human diseases. The precise and quick detection of RNA–protein interactions is crucial to finding new functions and to uncovering the mechanism of interactions. Although many methods have been presented to recognize RNA-binding sites, there is much room left for the improvement of predictive accuracy. We present a sequence semantics-based method (called PRIP) for predicting RNA-binding interfaces. The PRIP extracted semantic embedding by pre-training the Word2vec with the corpus. Extreme gradient boosting was employed to train a classifier. The PRIP obtained a SN of 0.73 over the five-fold cross validation and a SN of 0.67 over the independent test, outperforming the state-of-the-art methods. Compared with other methods, this PRIP learned the hidden relations between words in the context. The analysis of the semantics relationship implied that the semantics of some words were specific to RNA-binding interfaces. This method is helpful to explore the mechanism of RNA–protein interactions from a semantics point of view.

Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques

Lysine formylation is a newly discovered and mostly interested type of post-translational modification (PTM) that is generally found on core and linker histone proteins of prokaryote and eukaryote and plays various important roles on the regulation of various cellular mechanisms. Hence, it is very urgent to properly identify formylation site in protein for understanding the molecular mechanism of formylation deeply and defining drug for relevant diseases. As experimentally identification of formylation site using traditional processes are expensive and time consuming, a simple and high speedy mathematical model for predicting accurately lysine formylation sites is highly desired. A useful computational model named PLF_SVM is deigned and proposed in this study by using binary encoding (BE), amino acid composition (AAC), reverse position relative incidence matrix (RPRIM), position relative incidence matrix (PRIM), and position specific amino acid propensity (PSAAP) feature generation methods for predicting formylated and non-formylated lysine sites. Besides, the Synthetic Minority Oversampling Technique (SMOTE) and a proposed sample selection strategy named EnSVM are applied to handle the imbalance training dataset problem. Thereafter, the optimal number of features are selected by F-score method to train the model. Finally, it has been seen that PLF_SVM outperforms the state-of-the-art approaches in validation and independent test with an accuracy of 98.61% and 98.77% respectively. At https://plf-svm.herokuapp.com/, a user-friendly web tool is also created for identifying formylation sites. Therefore, the proposed method may be helpful guideline for the analysis and prediction of formylated lysine and knowing the process of cellular regulation.

A Transfer Learning-Based Approach for Lysine Propionylation Prediction

Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient (MCC) of 0.6615 on the 10-fold crossvalidation and 0.3174 on the independent test, outperforming state-of-the-art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/.