Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Open Badges Methodology article

How reliably can we predict the reliability of protein structure predictions?

István Miklós1*, Ádám Novák '1, Balázs Dombai2 and Jotun Hein1

Author Affiliations

1 Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK

2 e-Science Regional Knowledge Centre, Eötvös Loránd University, Pázmány Péter sétány 1/a. 1117 Budapest, Hungary

For all author emails, please log on.

BMC Bioinformatics 2008, 9:137  doi:10.1186/1471-2105-9-137

Published: 3 March 2008



Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences.


Stochastic sequence alignment methods define a posterior distribution of possible multiple alignments. They can highlight the most likely alignment, and above that, they can give posterior probabilities for each alignment column. We made a comprehensive study on the HOMSTRAD database of structural alignments, predicting secondary structures in four different ways. We showed that alignment posterior probabilities correlate with the reliability of secondary structure predictions, though the strength of the correlation is different for different protocols. The correspondence between the reliability of secondary structure predictions and alignment posterior probabilities is the closest to the identity function when the secondary structure posterior probabilities are calculated from the posterior distribution of multiple alignments. The largest deviation from the identity function has been obtained in the case of predicting secondary structures from a single optimal pairwise alignment. We also showed that alignment posterior probabilities correlate with the 3D distances between Cα amino acids in superimposed tertiary structures.


Alignment posterior probabilities can be used to a priori detect errors in comparative models on the sequence alignment level.