Protein tertiary structure prediction and refinement using deep learning
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Building the high-quality structure of a protein from its amino acid sequence has important applications in protein engineering and drug design. The problem of accurate protein three-dimensional structure prediction from its amino acid sequence has not been completely solved yet. In the past several decades, many successful applications emerged on studying the protein structure prediction in one, two, and three dimensions. Prediction tools developed in one dimension as the protein secondary structure predictors are mature and generally applicable to the study of other onedimensional structure property predictions(e.g. protein solvent accessibility, protein disorder prediction) as well. Prediction of protein structure in two dimensions, i.e. protein inter-residue contact/distance prediction has been significantly advanced by the coevolutionary analysis and the application of deep learning with reasonably high accuracy. As a result, protein structure prediction in the three-dimension, especially the ab initio protein tertiary structure prediction has been dramatically improved by the accurate protein contact/distance prediction and deep learning techniques in the field of computer vision and natural language processing. In this thesis, efforts have been made on studying the protein sequence-to-structure relationship in one, two, and three dimensions. Four major contributions are listed: (a) The fast and effective method for protein secondary structure prediction--TransPross has been developed by applying the 1D transformer network and the attention mechanism. (b) The factors that affect the performance of deep learning in protein contact/ distance prediction have been systematically investigated. (c) The protein realvalue inter-residue distance predictor with the deep residual convolutional network- DeepDist was developed. (d) A novel end-to-end protein structure refinement tool called ATOMRefine was proposed. All the methods above have stand-alone software tools that are freely released to the public.
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Ph. D.