Using Sequence Alignments to Predict Protein Structure and Stability With High Accuracy

Abstract

We present a sequence-based probabilistic formalism that directly addresses co-operative effects in networks of interacting positions in proteins, providing significantly improved contact prediction, as well as accurate quantitative prediction of free energy changes due to non-additive effects of multiple mutations. In addition to these practical considerations, the agreement of our sequence-based calculations with experimental data for both structure and stability demonstrates a strong relation between the statistical distribution of protein sequences produced by natural evolutionary processes, and the thermodynamic stability of the structures to which these sequences fold.

This manuscript was originally written in 2002 and available from http://library.lanl.gov/cgi-bin/getfile?01038177.pdf It's being deposited here for greater ease of access

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• Boltzmann machine learning and regularization methods for inferring evolutionary fields and couplings from a multiple sequence alignment
• Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
• Predicting diverse M-best protein contact maps
• Unsupervised and Supervised Structure Learning for Protein Contact Prediction