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Studying evolutionary correlations in alignments of homologous sequences bymeans of an inverse Potts model has proven useful to obtain residue-residuecontact energies and to predict contacts in proteins. The quality of theresults depend much on several choices of the detailed model and on thealgorithms used. We built, in a very controlled way, synthetic alignments withstatistical properties similar to those of real proteins, and used them toassess the performance of different inversion algorithms and of their variants.Realistic synthetic alignments display typical features of low--temperaturephases of disordered systems, a feature that affects the inversion algorithms.We showed that a Boltzmann--learning algorithm is computationally feasible andperforms well in predicting the energy of native contacts. However, allalgorithms suffer of false positives quite equally, making the quality of theprediction of native contacts with the different algorithm muchsystem--dependent.

physical biology

Statistical mechanical properties of sequence space determine the  efficiency of the various algorithms to predict interaction energies and  native contacts from protein coevolution