A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino acid side chain of a peptide. Recent studies have shown that this post-translational modification is directly responsible for the regulation of neurodevelopment and neurodegeneration and is also involved in many physiological processes. Despite the importance of this post-translational modification, there is no peptide sequence dataset available for conducting computation analysis. Therefore, so far, no computational approach has been proposed for predicting AMPylation. In this study, we introduce a new dataset of this distinct post-translational modification and develop a new machine learning tool using a deep convolutional neural network called DeepAmp to predict AMPylation sites in proteins. DeepAmp achieves 77.7%, 79.1%, 76.8%, 0.55, and 0.85 in terms of Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, and Area Under Curve for AMPylation site prediction task, respectively. As the first machine learning model, DeepAmp demonstrate promising results which highlight its potential to solve this problem. Our presented dataset and DeepAmp as a standalone predictor are publicly available at https://github.com/MehediAzim/DeepAmp .

OriC-ENS: A sequence-based ensemble classifier for predicting origin of replication in S. cerevisiae.

DNA Replication plays the most crucial part in biological inheritance, ensuring an even flow of genetic information from parent to offspring. The beginning site of DNA Replication which is called the Origin of Replication (ORI), plays a significant role in understanding the molecular mechanisms and genomic analysis of DNA. Hence, it is paramount to accurately identify the origin of replication to gain a more accurate understanding of the biochemical and genomic properties of DNA. In this paper, We have proposed a new approach named OriC-ENS that uses sequence-based feature extraction techniques, K-mer, K-gapped Mono-Di, and Di Mono, and an ensemble classification technique that uses majority voting for the identification of Origin of Replication. We have used three SVM classifiers, one for the K-mer features and two more for K-Gapped Mono-Di and K-Gapped Di-mono features. Finally, we used majority voting to combine the prediction by each predictor. Experimental results on the S. Cerevisiae dataset have shown that our method achieves an accuracy of 91.62 % which outperforms other state-of-the-art methods by a significant margin. We have also tested our method using other evaluation metrics such as Matthews Correlation Coefficient (MCC), Area Under Curve(AUC), Sensitivity, and Specificity, where it has achieved a score of 0.83, 0.98, 0.90, and 0.92 respectively. We have further evaluated our model on an independent test set collected from OriDB, consisting of the sequences of Schizosaccharomyces pombe where we have seen that our model can predict the origin of replication efficiently and with great precision. We have made our python-based source code available at https://github.com/MehediAzim/OriC-ENS.