ArticleScientific reports2015
De novo protein conformational sampling using a probabilistic graphical model.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Protein Structure Prediction: Conventional and Deep Learning Perspectives.The protein journal · 2021Review
- Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13.Proteins · 2019Article
- Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures.Biophysical journal · 2017Article
- Assessing Predicted Contacts for Building Protein Three-Dimensional Models.Methods in molecular biology (Clifton, N.J.) · 2017Article
- ConEVA: a toolbox for comprehensive assessment of protein contacts.BMC bioinformatics · 2016Article
- DeepQA: improving the estimation of single protein model quality with deep belief networks.BMC bioinformatics · 2016Article
- UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling.Bioinformatics (Oxford, England) · 2016Article
- FRAGSION: ultra-fast protein fragment library generation by IOHMM sampling.Bioinformatics (Oxford, England) · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Efficient exploration of protein conformational space remains challenging especially for large proteins when assembling discretized structural fragments extracted from a protein structure data database. We propose a fragment-free probabilistic graphical model, FUSION, for conformational sampling in continuous space and assess its accuracy using 'blind' protein targets with a length up to 250 residues from the CASP11 structure prediction exercise. The method reduces sampling bottlenecks, exhibits strong convergence, and demonstrates better performance than the popular fragment assembly method, ROSETTA, on relatively larger proteins with a length of more than 150 residues in our benchmark set. FUSION is freely available through a web server at http://protein.rnet.missouri.edu/FUSION/.
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Registered trials
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.