Brief. Bioinformatics - Critical assessment of high-throughput standalone methods for secondary structure prediction.


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Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the ?- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.

Resumo Limpo

sequencebas predict protein secondari structur ss enjoy widespread increas use analysi predict numer structur function characterist protein lack recent comprehens largescal comparison numer predict method result often arbitrari select ss predictor address void compar analyz popular standalon highthroughput predictor larg set protein provid indepth novel practic insight show univers best predictor thus detail compar studi need support inform select ss predictor given applic studi show threestat accuraci q segment overlap sov ss predict current reach respect demonstr care design consensusbas predictor improv q addit homolog modelingbas method signific better q ab initio approach empir analysi reveal solvent expos flexibl coil predict higher qualiti buri rigid coil invers true strand helic also show longer helic easier predict contrast longer strand harder find current method confus strand residu helic residu vice versa perform poor residu bridg helix conform final compar predict standalon implement four wellperform method correspond web server

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