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RISS 인기검색어
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- 저자 : Arslan Siraj (검색결과 2 건)
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Prediction of Lysine 2-Hydroxysisobutyrylation sites by using a deep learning approach
Arslan Siraj,Jung Mu Kim,Minho Song,Kil To Chong 대한전기학회 2021 정보 및 제어 심포지엄 논문집 Vol.2021 No.4
Protein hydroxysisobutyrylation is an important post-translational modification process that performs a critical role in a wide range of biological processes as regulating protein function, regulating gene transcriptional activity, tricarboxylic acid cycle, role in glycolysis/gluconeogenesis, and especially enriched in mitochondrial proteins within energy metabolic networks. Owing to the significant role of protein hydroxysisobutyrylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time-consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In this study, we proposed a novel approach for predicting plant hydroxysisobutyrylation sites using a deep learning model by utilizing different types of techniques. The proposed method uses the actual protein sequence will take inputs to the model and provides more robust predictions.
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Prediction of Lysine 2-Hydroxysisobutyrylation sites by using a deep learning approach
Arslan Siraj,Jung Mu Kim,Minho Song,Kil To Chong 대한전기학회 2021 대한전기학회 워크샵 Vol.2021 No.4
Protein hydroxysisobutyrylation is an important post-translational modification process that performs a critical role in a wide range of biological processes as regulating protein function, regulating gene transcriptional activity, tricarboxylic acid cycle, role in glycolysis/gluconeogenesis, and especially enriched in mitochondrial proteins within energy metabolic networks. Owing to the significant role of protein hydroxysisobutyrylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time-consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In this study, we proposed a novel approach for predicting plant hydroxysisobutyrylation sites using a deep learning model by utilizing different types of techniques. The proposed method uses the actual protein sequence will take inputs to the model and provides more robust predictions.
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