DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

Abstract

Motivation Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the ‘life and death’ cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases’ functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. Results We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. Availability and implementation The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/. Supplementary information Supplementary data are available at Bioinformatics online.

Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

“…Although our proposed method showed improved performance over other methods, it still has room for improvement. Recently, several novel computational approaches have been proposed in computational biology [68,78,[80][81][82][83][84][85] to identify function from the sequence. Hence, developing a novel prediction model by utilizing such approaches may improve the prediction performance.…”

An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP

“…To evaluate the prediction performance of XG-m7G we used four metrics, that is, Sn, Sp, Acc, and MCC, which have previously been used to assess the performance of predictors in other studies. 33 , 34 We also used ROC curves, 35 , 36 , 37 , 38 which plot the true-positive rate against the false-positive rate, and AUC to further assess the model performance. Sn, Sp, Acc, and MCC are defined as follows: where represents the total number of m7G site-containing sequences, represents the total number of non-m7G sequences, represents the number of m7G site-containing sequences incorrectly predicted as non-m7G sequences, and represents the number of non-m7G sequences incorrectly predicted as m7G site-containing sequences.…”

NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data

“…To improve the performance of the NonClasGP model, an ensemble learning model was built in this study, which used majority voting to integrate the prediction results of the above ten individual models, each of which was built on the optimal feature combinations. The performance of the ensemble model NonClasGP-Pred was evaluated by five commonly used metrics [15,40,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]…”