Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures.

ABSTRACT:

Background

The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models.

Results

Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues.

Conclusion

Cascaded

SUBMITTER: Boden M 

PROVIDER: S-EPMC1386714 | biostudies-literature | 2006 Feb

REPOSITORIES: biostudies-literature