During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. Hence, computational methods have emerged as viable alternatives. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. Īlthough advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Our presented dataset and DeepAmp as a standalone predictor are publicly available at. As the first machine learning model, DeepAmp demonstrate promising results which highlight its potential to solve this problem. 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. 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. Therefore, so far, no computational approach has been proposed for predicting AMPylation.
Despite the importance of this post-translational modification, there is no peptide sequence dataset available for conducting computation analysis. 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. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino acid side chain of a peptide. ĪMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. SEMal is publicly available as an online predictor at. Compared to the previously proposed methods, SEMal outperforms them in all metrics such as sensitivity (0.94 and 0.89), accuracy (0.94 and 0.91), and Matthews correlation coefficient (0.88 and 0.82), for Homo Sapiens and Mus Musculus species, respectively. To the best of our knowledge, our extracted features as well as our employed classifier have never been used for this problem. It also uses Rotation Forest (RoF) as its classification technique to predict Malonylation sites. It uses both structural and evolutionary-based features to solve this problem.
This paper proposes a novel approach, called SEMal, to identify Malonylation sites in protein sequences. However, this process is both costly and time-consuming which has inspired research to find more efficient and fast computational methods to solve this problem.
Malonylation can be detected experimentally using mass spectrometry. It has been shown that Malonylation has an important impact on different biological pathways including glucose and fatty acid metabolism. One of the most recently identified PTMs is Malonylation. Post Transactional Modification (PTM) is a vital process which plays an important role in a wide range of biological interactions.
0 kommentar(er)
