iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

Abstract

Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.

An Atlas of Phosphorylation and Proteolytic Processing Events During Excitotoxic Neuronal Death Reveals New Therapeutic Opportunities

“…Based upon the limited data of confirmed calpain cleavage sites in protein substrates and results of peptide library studies to define the substrate specificity determinants near the cleavage sites in synthetic peptides (Cuerrier et al, 2005;Shinkai-Ouchi et al, 2016), a number of algorithms for prediction of calpain substrates and the cleavage sites were designed. The most notable algorithms include calCleaveMKL (duVerle and Mamitsuka, 2019), iProt-Sub (Song et al, 2019) , DeepCalpain (Liu et al, 2019) and GPS-CCD (Liu et al, 2011). Here, we demonstrated for the first time a high degree of conformity of amino acid preferences at the P6-P5' positions in both the potential calpain substrates in neurons and the in vitro peptide substrates of calpains 1 and 2 ( Figures 6B and 6C).…”

IDP–CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields

“…Previous studies have indicated that incorporating the predicted solvent accessibility information is useful for improving the prediction of protein functional sites [ 73 , 74 , 75 , 76 ]. In this study, Sable version 2 package [ 77 , 78 ] is adopted to generate relative solvent accessibility information for each target residue, and the dimension of this feature is one for each target residue.…”

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

“…To evaluate the effectiveness of the XGBoost classifier, we compared it with five commonly used machine learning algorithms, including Random Forest (RF) ( Liu B. et al, 2015 ; Li et al, 2016 ; Wei et al, 2017b ), Naïve Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN)( Huang and Li, 2018 ), Support Vector Machine (SVM) ( Song et al, 2010 , 2012 , 2018 ; Wang M. et al, 2014 ; Wei et al, 2017 ), and Gradient Boosting Decision Tree (GBDT) ( Liao et al, 2018 ), respectively. For fair comparison, the machine learning algorithms were trained and evaluated with 10-fold cross validation on the benchmark datasets, respectively.…”