ArticleBioinformatics (Oxford, England)2017
QAcon: single model quality assessment using protein structural and contact information with machine learning techniques.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Estimating protein complex model accuracy using graph transformers and pairwise similarity graphs.Bioinformatics advances · 2025Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- DeepUMQA3: a web server for accurate assessment of interface residue accuracy in protein complexes.Bioinformatics (Oxford, England) · 2023Article
- Predicting residue-specific qualities of individual protein models using residual neural networks and graph neural networks.Proteins · 2022Article
- Using steered molecular dynamic tension for assessing quality of computational protein structure models.Journal of computational chemistry · 2022Article
- ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.Briefings in bioinformatics · 2022Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores.Computational and structural biotechnology journal · 2021Article
- MUfoldQA_G: High-accuracy protein model QA via retraining and transformation.Computational and structural biotechnology journal · 2021Article
- Empirical Comparison and Analysis of Web-Based DNAMolecular therapy. Nucleic acids · 2020Article
- MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.BMC bioinformatics · 2020Review
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- Deep learning methods in protein structure prediction.Computational and structural biotechnology journal · 2020Review
- Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13.Proteins · 2019Article
- A simple neural network implementation of generalized solvation free energy for assessment of protein structural models.RSC advances · 2019Article
- PSICA: a fast and accurate web service for protein model quality analysis.Nucleic acids research · 2019Article
- Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.International journal of molecular sciences · 2019Article
- Validation and quality assessment of macromolecular structures using complex network analysis.Scientific reports · 2019Article
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6 authors.
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Abstract
Motivation: Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool. Results: In this paper, we develop a novel single-model quality assessment method QAcon utilizing structural features, physicochemical properties, and residue contact predictions. We apply residue-residue contact information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new score as feature for quality assessment. This novel feature and other 11 features are used as input to train a two-layer neural network on CASP9 datasets to predict the quality of a single protein model. We blindly benchmarked our method QAcon on CASP11 dataset as the MULTICOM-CLUSTER server. Based on the evaluation, our method is ranked as one of the top single model QA methods. The good performance of the features based on contact prediction illustrates the value of using contact information in protein quality assessment. Availability and Implementation: The web server and the source code of QAcon are freely available at: http://cactus.rnet.missouri.edu/QAcon. Contact: chengji@missouri.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.