Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization

Motivation: Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy. Method: We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods. Results: On challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q₈ accuracy by more than 2-10%, and Q₃ accuracy by more than 1-3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction. Availability and implementation: A preliminary implementation in a new tool we call Nnessy is available free for non-commercial use at http://nnessy.cs.Arizona.edu.