Bioinformatics Advance Access originally published online on September 7, 2007
Bioinformatics 2007 23(21):2836-2842; doi:10.1093/bioinformatics/btm456
Modeling protein loops with knowledge-based prediction of sequence-structure alignment
Genomics Research Center, Academia Sinica. 128 Academia Road, Section 2, Nankang District, Taipei 115, Taiwan, R.O.C.
*To whom correspondence should be addressed.
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Abstract |
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Motivation: As protein structure database expands, protein loop modeling remains an important and yet challenging problem. Knowledge-based protein loop prediction methods have met with two challenges in methodology development: (1) loop boundaries in protein structures are frequently problematic in constructing length-dependent loop databases for protein loop predictions; (2) knowledge-based modeling of loops of unknown structure requires both aligning a query loop sequence to loop templates and ranking the loop sequence-template matches.
Results: We developed a knowledge-based loop prediction method that circumvents the need of constructing hierarchically clustered length-dependent loop libraries. The method first predicts local structural fragments of a query loop sequence and then structurally aligns the predicted structural fragments to a set of non-redundant loop structural templates regardless of the loop length. The sequence-template alignments are then quantitatively evaluated with an artificial neural network model trained on a set of predictions with known outcomes. Prediction accuracy benchmarks indicated that the novel procedure provided an alternative approach overcoming the challenges of knowledge-based loop prediction.
Availability: http://cmb.genomics.sinica.edu.tw
Contact: yangas{at}gate.sinica.edu.tw
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Alfonso Valencia
Received on July 4, 2007; revised on August 16, 2007; accepted on August 23, 2007
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