adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis

In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package \texttt{adabmDCA 2.0} is available in different programming languages (C++, Julia, Python) usable on different architectures (single-core and multi-core CPU, GPU) using a common front-end interface. In addition to several learning protocols for dense and sparse generative DCA models, it allows to directly address common downstream tasks like residue-residue contact prediction, mutational-effect prediction, scoring of sequence libraries and generation of artificial sequences for sequence design. It is readily applicable to protein and RNA sequence data.

Sparse generative modeling of protein-sequence families

Pairwise Potts models (PM) provide accurate statistical models of families of evolutionarily related protein sequences. Their parameters are the local fields, which describe site-specific patterns of amino-acid conservation, and the two-site couplings, which mirror the coevolution between pairs of distinct sites. This coevolution reflects structural and functional constraints acting on protein sequences during evolution, and couplings can a priori connect any pairs of sites, even those being distant along the protein chain, or distant in the three-dimensional protein fold. The most conservative choice to describe all of the coevolution signal is to include all possible two-site couplings into the PM. This choice, typically made by what is known as Direct Coupling Analysis, has been highly successful in using sequences for predicting residue contacts in the three-dimensional structure, mutational effects, and in generating new functional sequences. However, the resulting PM suffers from important over-fitting effects: many couplings are small, noisy and hardly interpretable, and the PM is close to a critical point, meaning that it is highly sensitive to small parameter perturbations. In this work, we introduce a parameter-reduction procedure via iterative decimation of the less statistically significant couplings. We propose an information-based criterion that identifies couplings that are either weak, or statistically unsupported. We show that our procedure allows one to remove more than 90% of the PM couplings, while preserving the predictive and generative properties of the original dense PM. The resulting model is far away from criticality, meaning that it is more robust to noise, and its couplings are more easily interpretable.