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• W2100697211 abstract "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 β- bridge and 310-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers." @default.
• W2100697211 created "2016-06-24" @default.
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• W2100697211 date "2011-01-20" @default.
• W2100697211 modified "2023-10-16" @default.
• W2100697211 title "Critical assessment of high-throughput standalone methods for secondary structure prediction" @default.
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• W2100697211 doi "https://doi.org/10.1093/bib/bbq088" @default.
• W2100697211 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21252072" @default.
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