A graphical model for protein secondary structure prediction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Segmentation conditional random fields (SCRFs): a new approach for protein fold recognition
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Rapid protein side-chain packing via tree decomposition
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A Bayesian approach to protein model quality assessment
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Protein Design by Sampling an Undirected Graphical Model of Residue Constraints
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Adaptive Exact Inference in Graphical Models
The Journal of Machine Learning Research
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We present a technique for approximating the free energy of protein structures using Generalized Belief Propagation (GBP). The accuracy and utility of these estimates are then demonstrated in two different application domains. First, we show that the entropy component of our free energy estimates can be useful in distinguishing native protein structures from decoys -- structures with similar internal energy to that of the native structure, but otherwise incorrect. Our method is able to correctly identify the native fold from among a set of decoys with 87.5% accuracy over a total of 48 different immunoglobin folds. The remaining 12.5% of native structures are ranked among the top 4 of all structures. Second, we show that our estimates of ΔΔG upon mutation upon mutation for three different data sets have linear correlations between 0.63-0.70 with experimental values and statistically significant p-values. Together, these results suggests that GBP is an effective means for computing free energy in all-atom models of protein structures. GBP is also efficient, taking a few minutes to run on a typical sized protein, further suggesting that GBP may be an attractive alternative to more costly molecular dynamic simulations for some tasks.