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dc.contributor.advisorShang, Yi, 1967-eng
dc.contributor.authorLi, Zhaoyueng
dc.date.issued2020eng
dc.date.submitted2020 Springeng
dc.description.abstractProtein structure prediction is of great importance in bioinformatics and computational biology. Over the past 30 years, many machine learning methods have been developed for this problem in homology-based and ab-initio approaches. Recently, deep learning has been successfully applied and has outperformed previous methods. Deep learning methods could effectively handle high dimensional feature inputs in modeling the complex mapping from protein primary amino acid sequences to protein 2-D or 3-D structures. In this dissertation, new deep learning methods and deep learning networks have been proposed for three problems in protein structure prediction: loop modeling, contact map prediction, and contact map refinement. They have been implemented in the state-of-the-art MUFOLD software and obtained significant performance improvement. The goal of loop modeling is to predict the conformation of a relatively short stretch of protein backbone. A new method based on Generative Adversarial Network (GAN), called MUFOLD-LM, is proposed. The protein 3-D structure can be represented using the 2-D distance map of C [subscript alpha] atoms. The missing region in the structure will be a missing region in the distance map correspondingly. Our network uses the Generator Network to fill in the missing regions in the distance map based on the context, and the Discriminator Network will take both the predicted complete distance map and the ground truth as input to distinguish between them. The method utilizes both the features and context of the missing loop region to make better prediction of the 3-D structure of the loop region. In experiments using commonly used benchmark datasets 8-Res and 12-Res, MUFOLD-LM outperformed previous methods significantly, up to 43.9 [percent] and 4.13 [percent] in RMSD, respectively. To the best of our knowledge, it is the first successful GAN application in protein structure prediction. The goal of contact map prediction is to predict whether the distance between two C [subscript beta] atoms (C [subscript alpha] for Glycine) in a protein falls within a certain threshold. It can help to determine the global s"tructure of a protein in order to assist the 3D modeling process. In this work, a new two-stage multi-branch neural network based on Fully Convolutional Network and Dilated Residual Network, called MUFOLD_Contact, is proposed. It formulates the problem as a pixel-wise regression and classification problem. The first stage predicts distance maps for short-, medium-, and long-range residue pairs. The second stage takes the predicted distances from stage 1 along with other features as input to predict a binary contact map. The method utilizes the distance distribution information in the feature set to improve the binary prediction results. In experiments using CASP13 targets, the new method outperformed single stage networks and is comparable with the best existing tools. In addition to predicting contact directly using deep neural networks, a new method, called TPCref (Template Prediction Correction refinement), is proposed to refine and improve the prediction results of a contact predictor using protein templates. Based on the idea of collaborative filtering from recommendation system, TPCref first finds multiple template sequences based on the target sequence and uses the templates' structures and the templates' predicted contact map generated by a contact predictor to form a target contact map filter using the idea of collaborative filtering. Then the contact-map filter is used to refine the predicted contact map. In experimental results using recently released PDB proteins, TPCref significantly improved the contact prediction results of existing predictors, improving MUFOLD_Contact, MetaPSICOV, and CCMPred by 5.0 [percent], 12.8 [percent], and 37.2 [percent], respectively. The proposed new methods have been implemented in MUFOLD, a comprehensive platform for protein structure prediction. It provides a rich set of functions, including database generation, secondary and supersecondary structure prediction, beta-turn and gamma-turn prediction, contact map prediction and refinement, protein 3D structure prediction, loop modeling, model quality assessment, and model refinement. In this work, a new modularized MUFOLD pipeline has been designed and developed. Each module is decoupled from each other and provides standard communication protocol interfaces for other programs to call. The modularization provides the capability to easily integrate new algorithms and tools to have a fast iteration during research. In addition, a new web portal for MUFOLD has been designed and implemented to provide online services or APIs of our tools to the community.eng
dc.identifier.urihttps://hdl.handle.net/10355/83767
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.titleComputational protein structure prediction using deep learningeng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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