Assessing the performance of fold recognition methods by means of a comprehensive benchmark.
Recently there has been an explosion of methods for fold recognition. These methods seek to align a protein sequence to a three-dimensional structure and measure the compatibility of the sequence to the structure. In this work, we present a benchmark to assess the performance of such methods. The benchmark consists of a set of protein sequences matched by superposition to known structures. This set covers a wide range of protein families, and includes matching proteins with insignificant sequence similarity. To demonstrate the usefulness of this benchmark, we apply it here to compare different fold-recognition methods developed through the years in our group as well as several sequence-sequence substitution matrices. The results show that "global-local" alignments are superior to either local or global alignments. The most effective sequence-sequence matching matrix is the Gonnet table. The best performance overall is obtained by a method which combines the 3D-1D profiles of Bowie et al. with a substitution matrix and takes into account residue pairwise interactions.
Fischer
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UCLA-DOE Lab. of Structural Biology & Molecular Medicine, Molecular Biology Institute 90095-1570, USA.
Elofsson
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Rice
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Eisenberg
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D000595:Amino Acid Sequence; D003198:Computer Simulation; D004798:Enzymes / Q000737:chemistry; D008967:Molecular Biology / Q000379:methods; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D012984:Software
D004798:Enzymes; D011506:Proteins