A New Approach Of Applying Deep Learning To Protein Model Quality Assessment Computational protein structure prediction is an important problem in bioinformatics. In recent years, several deep learning methods have been developed for protein 3D model quality assessment (QA) and achieved promising results. In this paper, we propose a new approach of applying deep learning to the QA problem, called DeepPTQA: training one customized deep learning network for each target protein whose models are to be evaluated, i.e. the pertarget training approach. For each target protein, a deep neural network is first trained using a labeled training set created based on templates of the target protein, and then applied to evaluate the quality of models of the target protein. We implemented this approach using new deep inception networks based on templates of the target protein. To predict the quality of a protein model, the deep networks use an extensive set of 1D and 2D features of the target protein and the target model, including predicted and real secondary structures, solvent accessibility, distance matrix, coevolutionary information, to predict the modelu0027s true GDTTS score, a widely used quality score in the range of 0, 1. Experimental results using CASP targets show that DeepPTQA outperformed the best existing QA methods on the CASP Stage 2 QA task.

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 Prediction of residueresidue distance relationships (e.g. contacts) has become the key direction to advance protein tertiary structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, contact distancedriven templatefree (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction, in addition to an update of other components such as template library, sequence database, and alignment tools. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both templatefree and templatebased protein structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residueresidue features such as coevolution scores to substantially improve interresidue contact distance prediction, which played a decisive role in correctly folding some free modeling and hard templatebased modeling targets from scratch. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system in the CASP13 experiment clearly shows that protein contact distance prediction and model selection driven by powerful deep learning holds the key of solving protein structure prediction problem. However, there are still major challenges in accurately predicting protein contact distance when there are few homologous sequences to generate coevolutionary signals, folding proteins from noisy contact distances, and ranking models of hard targets.