ArticleBioinformatics (Oxford, England)2015
Large-scale model quality assessment for improving protein tertiary structure prediction.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Phytochemical targeting of SASP2-mediated DNA protection to impair spore resilience in Bacillus cereus: A Computational Investigation.Molecular diversity · 2026Article
- Improving AlphaFold2- and AlphaFold3-Based Protein Complex Structure Prediction With MULTICOM4 in CASP16.Proteins · 2026Article
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- Estimating protein complex model accuracy using graph transformers and pairwise similarity graphs.Bioinformatics advances · 2025Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- 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
- Computational Methods for the Elucidation of Protein Structure and Interactions.Methods in molecular biology (Clifton, N.J.) · 2021Review
- MUfoldQA_G: High-accuracy protein model QA via retraining and transformation.Computational and structural biotechnology journal · 2021Article
- QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.Bioinformatics (Oxford, England) · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 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
- Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions.Computational and structural biotechnology journal · 2019Review
- PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.Frontiers in immunology · 2018Article
- MQAPRank: improved global protein model quality assessment by learning-to-rank.BMC bioinformatics · 2017Article
- QAcon: single model quality assessment using protein structural and contact information with machine learning techniques.Bioinformatics (Oxford, England) · 2017Article
- Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.Scientific reports · 2017Article
- Identify High-Quality Protein Structural Models by EnhancedBioMed research international · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
motivationSampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well.
resultsHere, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. AVAILABILITY AND IMPLEMENTATION: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/.
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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.