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


ABSTRACT: SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: http://proqm.wallnerlab.org/download/. CONTACT: bjornw@ifm.liu.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUBMITTER: Wallner B 

PROVIDER: S-EPMC4103597 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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

Wallner Björn B  

Bioinformatics (Oxford, England) 20140408 15


<h4>Summary</h4>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 n  ...[more]

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