A protein succinylation sites prediction method based on the hybrid architecture of LSTM network and CNN

Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique

Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging. To address this, we introduce an approach called CbiLSuccSite, that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for the accurate prediction of protein succinylation sites. Our approach employs a word embedding layer to encode protein sequences, enabling the automatic learning of intricate patterns and dependencies without manual feature extraction. In 10-fold cross-validation, CBiLSuccSite achieved superior predictive performance, with an Area Under the Curve (AUC) of 0.826 and a Matthews Correlation Coefficient (MCC) of 0.502. Independent testing further validated its robustness, yielding an AUC of 0.818 and an MCC of 0.53. The integration of CNN and Bi-LSTM leverages the strengths of both architectures, establishing CBiLSuccSite as an effective tool for protein language processing and succinylation site prediction. Our model and code are publicly accessible at: https://github.com/nuinvtnu/CBiLSuccSite.

FRP-XGBoost: Identification of ferroptosis-related proteins based on multi-view features

Ferroptosis represents a novel form of programmed cell death. Pan-cancer bioinformatics analysis indicates that identifying and modulating ferroptosis offer innovative approaches for preventing and treating diverse tumor pathologies. However, the precise detection of ferroptosis-related proteins via conventional wet-laboratory techniques remains a formidable challenge, largely due to the constraints of existing methodologies. These traditional approaches are not only labor-intensive but also financially burdensome. Consequently, there is an imperative need for the development of more sophisticated and efficient computational tools to facilitate the detection of these proteins. In this paper, we presented a XGBoost and multi-view features-based machine learning prediction method for predicting ferroptosis-related proteins, which was referred to as FRP-XGBoost. In this study, we explored four types of protein feature extraction methods and evaluated their effectiveness in predicting ferroptosis-related proteins using six of the most commonly used traditional classifiers. To enhance the representational power of the hybrid features, we employed a two-step feature selection technique to identify the optimal subset of features. Subsequently, we constructed a prediction model using the XGBoost algorithm. The FRP-XGBoost achieved an accuracy of 96.74 % in 10-fold cross-validation and a further accuracy of 91.52 % in an independent test. The implementation source code of FRP-XGBoost is available at https://github.com/linli5417/FRP-XGBoost.

Succinylated lysine residue prediction revisited

Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value.

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites

Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.