Affiliations: Fraunhofer Institute for Algorithms and Scientific
Computing (SCAI), Schloss Birlinghoven, D-53754 Sankt Augustin, Germany E-mail:
{mario.albrecht, daniel.hanisch, ralf.zimmer,
thomas.lengauer}@scai.fhg.de
Note: [] Corresponding author
Abstract: We present a comprehensive analysis of methods for improving the
fold recognition rate of the threading approach to protein structure prediction
by the utilization of few additional distance constraints. The distance
constraints between protein residues may be obtained by experiments such as
mass spectrometry or NMR spectroscopy. We applied a post-filtering step with
new scoring functions incorporating measures of constraint satisfaction to
ranking lists of 123D threading alignments. The detailed analysis of the
results on a small representative benchmark set show that the fold recognition
rate can be improved significantly by up to 30% from about 54%-65% to 77%-84%,
approaching the maximal attainable performance of 90% estimated by structural
superposition alignments. This gain in performance adds about 10% to the
recognition rate already achieved in our previous study with cross-link
constraints only. Additional recent results on a larger benchmark set involving
a confidence function for threading predictions also indicate notable
improvements by our combined approach, which should be particularly valuable
for rapid structure determination and validation of protein models.
Keywords: protein threading, fold recognition, structure prediction, experimental data, distance constraints, cross-linking reagents, mass spectrometry, NOE restraints, NMR
