ArticlePloS one2016
CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.Journal of proteome research · 2021Article
- Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.BMC bioinformatics · 2019Article
- Identification and localization of Tospovirus genus-wide conserved residues in 3D models of the nucleocapsid and the silencing suppressor proteins.Virology journal · 2019Article
- Finding the needle in the haystack: towards solving the protein-folding problem computationally.Critical reviews in biochemistry and molecular biology · 2018Review
- Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.Proteins · 2017Article
- Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.
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