Studying evolutionary correlations in alignments of homologous sequences by means of an inverse Potts model has proven useful to obtain residue-residue contact energies and to predict contacts in proteins. The quality of the results depend much on several choices of the detailed model and on the algorithms used. We built, in a very controlled way, synthetic alignments with statistical properties similar to those of real proteins, and used them to assess the performance of different inversion algorithms and of their variants. Realistic synthetic alignments display typical features of low-temperature phases of disordered systems, a feature that affects the inversion algorithms. We showed that a Boltzmann-learning algorithm is computationally feasible and performs well in predicting the energy of native contacts. However, all algorithms, when applied to alignments of realistic size, suffer of false positives quite equally, making the quality of the prediction of native contacts with the different algorithm much system-dependent.

中文翻译：

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序列空间的统计机械性能决定了各种算法从蛋白质协同进化预测相互作用能和天然接触的效率。

已经证明通过逆Potts模型研究同源序列的比对中的进化相关性对于获得残基-残基接触能和预测蛋白质中的接触是有用的。结果的质量很大程度上取决于详细模型的几种选择以及所使用的算法。我们以非常可控的方式构建了具有与真实蛋白质相似的统计特性的合成比对，并使用它们来评估不同反演算法及其变体的性能。实际的合成比对显示无序系统的低温相的典型特征，该特征会影响反演算法。我们证明了Boltzmann学习算法在计算上是可行的，并且在预测本地联系人的能量方面表现良好。但是，所有算法