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adabmDCA: Adaptive Boltzmann machine learning for biological sequences
Anna Paola Muntoni, Andrea Pagnani, Martin Weigt +1
Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are…
Aligning biological sequences by exploiting residue conservation and coevolution
Anna Paola Muntoni, Andrea Pagnani, Martin Weigt +1
Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among the most important computational problems is that of sequence alignme…
How pairwise coevolutionary models capture the collective residue variability in proteins
Matteo Figliuzzi, Pierre Barrat-Charlaix, Martin Weigt
Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity t…
Context-Aware Prediction of Pathogenicity of Missense Mutations Involved in Human Disease
Christoph Feinauer, Martin Weigt
Amino-acid substitutions are implicated in a wide range of human diseases, many of which are lethal. Distinguishing such mutations from polymorphisms without significant effect on…
Improving landscape inference by integrating heterogeneous data in the inverse Ising problem
Pierre Barrat-Charlaix, Matteo Figliuzzi, Martin Weigt
The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statisti…
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Thomas Gueudré, Carlo Baldassi, Marco Zamparo +2
Understanding protein-protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databa…