Modeling protein loops with knowledge-based prediction of sequence-structure alignment

Hung-Pin Peng; An-Suei Yang
November 2007
Bioinformatics;Nov2007, Vol. 23 Issue 21, p2836
Academic Journal
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@gate.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.


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