ArticleBMC bioinformatics2012
Improved model quality assessment using ProQ2.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.
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99 citing papers in PubMed, 206 citations in OpenAlex.
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- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
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- DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.BMC bioinformatics · 2022Article
- ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.Briefings in bioinformatics · 2022Article
- Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14.Proteins · 2022Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- GraphQA: protein model quality assessment using graph convolutional networks.Bioinformatics (Oxford, England) · 2021Article
- P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features.Bioengineering (Basel, Switzerland) · 2021Article
- Improved protein structure refinement guided by deep learning based accuracy estimation.Nature communications · 2021Article
- Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.Frontiers in molecular biosciences · 2021Review
39 more citing papers are in PubMed but not listed here.
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3 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEmploying methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.
resultsHere, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.
conclusionsProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson's correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.
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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.