Background:
S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification,
which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and
apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have
been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient
feature schemes, severe imbalance issues, and lack of an intelligent learning engine.
Objective:
In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and
non-sulfenylation sites.
Methods:
In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed
to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication.
Results:
Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased
presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide
sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data
and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method.
These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies.
Conclusion :
In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE
technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will
provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed
predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs
in general.
Understanding Dyslexia Through Personalized Large-Scale Computational Models
