Dataset Information
ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling.
Ontology highlight
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.seSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Wallner B
PROVIDER: S-EPMC4103597 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
Publications
ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling.
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]