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

## TL;DR

This study investigates how the statistical properties of sequence space influence the effectiveness of various algorithms in predicting protein interaction energies and native contacts from coevolution data, using synthetic alignments.

## Contribution

The paper introduces a controlled approach using synthetic alignments to evaluate and compare the performance of different inverse Potts model algorithms in protein contact prediction.

## Key findings

- Boltzmann-learning algorithm is computationally feasible and effective.
- All algorithms exhibit similar false positive rates.
- Prediction quality varies significantly depending on the system.

## Abstract

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 suffer of false positives quite equally, making the quality of the prediction of native contacts with the different algorithm much system--dependent.