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• W2131617076 abstract "Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions." @default.
• W2131617076 created "2016-06-24" @default.
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• W2131617076 date "2013-09-10" @default.
• W2131617076 modified "2023-10-17" @default.
• W2131617076 title "A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction" @default.
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• W2131617076 doi "https://doi.org/10.1038/srep02619" @default.
• W2131617076 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3965362" @default.
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