PreDisorder: ab initio sequence-based prediction of protein disordered regions. Background: Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation. Results: This paper presents our protein disorder prediction server, PreDisorder. It is based on our ab initio prediction method (MULTICOM-CMFR) which, along with our meta (or consensus) prediction method (MULTICOM), was recently ranked among the top disorder predictors in the eighth edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP8). We systematically benchmarked PreDisorder along with 26 other protein disorder predictors on the CASP8 data set and assessed its accuracy using a number of measures. The results show that it compared favourably with other ab initio methods and its performance is comparable to that of the best meta and clustering methods. Conclusion: PreDisorder is a fast and reliable server which can be used to predict protein disordered regions on genomic scale. It is available at http://casp.rnet.missouri.edu/predisorder.html.
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References in zbMATH (referenced in 2 articles )
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- Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
- Deng, Xin; Eickholt, Jesse; Cheng, Jianlin: Predisorder: ab initio sequence-based prediction of protein disordered regions (2009) ioport