Bioinformatics, Vol 15, 53-65, Copyright © 1999 by Oxford University Press
CC Chen, JP Singh and RB Altman
MOTIVATION: Until ab initio structure prediction methods are perfected, the
estimation of structure for protein molecules will depend on combining
multiple sources of experimental and theoretical data. Secondary structure
predictions are a particularly useful source of structural information, but
are currently only approximately 70% correct, on average. Structure
computation algorithms which incorporate secondary structure information
must therefore have methods for dealing with predictions that are
imperfect. EXPERIMENTS PERFORMED: We have modified our algorithm for
probabilistic least squares structural computations to accept 'disjunctive'
constraints, in which a constraint is provided as a set of possible values,
each weighted with a probability. Thus, when a helix is predicted, the
distances associated with a helix are given most of the weight, but some
weights can be allocated to the other possibilities (strand and coil). We
have tested a variety of strategies for this weighting scheme in
conjunction with a baseline synthetic set of sparse distance data, and
compared it with strategies which do not use disjunctive constraints.
RESULTS: Naive interpretations in which predictions were taken as 100%
correct led to poor-quality structures. Interpretations that allow
disjunctive constraints are quite robust, and even relatively poor
predictions (58% correct) can significantly increase the quality of
computed structures (almost halving the RMS error from the known
structure). CONCLUSIONS: Secondary structure predictions can be used to
improve the quality of three-dimensional structural computations. In fact,
when interpreted appropriately, imperfect predictions can provide almost as
much improvement as perfect predictions in three-dimensional structure
calculations.
ARTICLES
Using imperfect secondary structure predictions to improve molecular structure computations
Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA.
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