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Abstract MotivationQuality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. ResultsWe develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. Availability and implementationThe source code is available at https://github.com/BioinfoMachineLearning/EnQA. Supplementary informationSupplementary data are available at Bioinformatics online.
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Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorization
Finding the inherent organization in the structure space of a protein molecule is central in many computational studies of proteins. Grouping or clustering tertiary structures of a protein has been leveraged to build representations of the structure-energy landscape, highlight sta- ble and semi-stable structural states, support models of structural dy- namics, and connect them to biological function. Over the years, our laboratory has introduced methods to reveal structural states and build models of state-to-state protein dynamics. These methods have also been shown competitive for an orthogonal problem known as model selection, where model refers to a computed tertiary structure. Building on this work, in this paper we present a novel, tensor factorization-based method that doubles as a non-parametric clustering method. While the method has broad applicability, here we focus and demonstrate its efficacy on the estimation of model accuracy (EMA) problem. The method outperforms state-of-the-art methods, including single-model methods that leverage deep neural networks and domain-specific insight.
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- Award ID(s):
- 1900061
- PAR ID:
- 10343781
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Intl Conf on Comput Adv in Bio and Medical Sciences (ICCABS) 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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