Gormez, YasinAydin, Zafer2024-10-262024-10-2620231545-59631557-9964https://doi.org/10.1109/TCBB.2022.3191395https://hdl.handle.net/20.500.12418/29284Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model.en10.1109/TCBB.2022.3191395info:eu-repo/semantics/closedAccessProteinsPredictive modelsDeep learningSolventsAmino acidsRecurrent neural networksFeature extractionFeature extraction or constructionmachine learningprotein structure predicitionbioinformaticsdeep learningArticle20211131104358496632-s2.0-85135223840Q2WOS:000965674700029Q1