CONTRAfold

MOTIVATION: For several decades, free energy minimization methods have been the
dominant strategy for single sequence RNA secondary structure prediction. More
recently, stochastic context-free grammars (SCFGs) have emerged as an alternative
probabilistic methodology for modeling RNA structure. Unlike physics-based
methods, which rely on thousands of experimentally-measured thermodynamic
parameters, SCFGs use fully-automated statistical learning algorithms to derive
model parameters. Despite this advantage, however, probabilistic methods have not
replaced free energy minimization methods as the tool of choice for secondary
structure prediction, as the accuracies of the best current SCFGs have yet to
match those of the best physics-based models. RESULTS: In this paper, we present
CONTRAfold, a novel secondary structure prediction method based on conditional
log-linear models (CLLMs), a flexible class of probabilistic models which
generalize upon SCFGs by using discriminative training and feature-rich scoring.
In a series of cross-validation experiments, we show that grammar-based secondary
structure prediction methods formulated as CLLMs consistently outperform their
SCFG analogs. Furthermore, CONTRAfold, a CLLM incorporating most of the features
found in typical thermodynamic models, achieves the highest single sequence
prediction accuracies to date, outperforming currently available probabilistic
and physics-based techniques. Our result thus closes the gap between
probabilistic and thermodynamic models, demonstrating that statistical learning
procedures provide an effective alternative to empirical measurement of
thermodynamic parameters for RNA secondary structure prediction. AVAILABILITY:
Source code for CONTRAfold is available at
http://contra.stanford.edu/contrafold/.

CONTRAfold: RNA secondary structure prediction without physics-based models.

CONTRAfold is a new way to predict secondary structure in proteins, which uses the flexible and powerful approach of conditional log-linear models (CLLMs). It applies discriminative training and features from traditional thermodynamic models to offer more accurate predictions than previous methods like SCFGs.

CONTRAfold

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