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ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling

Overview of attention for article published in Bioinformatics, April 2014
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Title
ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling
Published in
Bioinformatics, April 2014
DOI 10.1093/bioinformatics/btu187
Pubmed ID
Authors

Björn Wallner

Abstract

Model Quality Assessment Programs (MQAPs) are used to predict the quality of modeled protein structures. These usually use two approaches: methods using consensus of many alternative models and methods requiring only a single model to do its prediction. The consensus methods are useful to improve overall accuracy; however, they frequently fail to pick out the best possible model and cannot be used to generate and score new structures. Single-model methods, on the other hand, do not have these inherent shortcomings and can be used to both sample new structures and improve existing consensus methods. Here, we present ProQM-resample, a membrane protein-specific single-model MQAP, that couples side-chain resampling with MQAP rescoring by ProQM to improve model selection. The side-chain resampling is able to improve side-chain packing for 96% of all models, and improve model selection by 24% as measured by the sum of the Z-score for the first-ranked model (from 25.0 to 31.1), even better than the state-of-the-art consensus method Pcons. The improved model selection can be attributed to the improved side-chain quality, which enables the MQAP to rescue good backbone models with poor side-chain packing.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 6%
Brazil 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Researcher 4 25%
Student > Bachelor 3 19%
Student > Master 2 13%
Student > Doctoral Student 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 38%
Biochemistry, Genetics and Molecular Biology 3 19%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Computer Science 1 6%
Physics and Astronomy 1 6%
Other 3 19%
Unknown 1 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 July 2014.
All research outputs
#20,655,488
of 25,373,627 outputs
Outputs from Bioinformatics
#11,736
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Outputs of similar age
#177,952
of 241,515 outputs
Outputs of similar age from Bioinformatics
#150
of 167 outputs
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