no code implementations • 14 Mar 2024 • Leonardo Di Bari, Matteo Bisardi, Sabrina Cotogno, Martin Weigt, Francesco Zamponi
Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations.
1 code implementation • 19 Oct 2023 • Francesco Calvanese, Camille N. Lambert, Philippe Nghe, Francesco Zamponi, Martin Weigt
Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences.
no code implementations • 24 Aug 2022 • Carlos A. Gandarilla-Perez, Sergio Pinilla, Anne-Florence Bitbol, Martin Weigt
We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs.
no code implementations • 19 Dec 2021 • Juan Rodriguez-Rivas, Giancarlo Croce, Maureen Muscat, Martin Weigt
The emergence of new variants of SARS-CoV-2 is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions.
1 code implementation • 9 Sep 2021 • Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi
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.
no code implementations • 4 Jun 2021 • Matteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, Martin Weigt
During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection.
no code implementations • 4 Mar 2021 • Jeanne Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin Weigt
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases.
no code implementations • 11 Feb 2021 • Edwin Rodriguez Horta, Alejandro Lage, Martin Weigt, Pierre Barrat-Charlaix
The naive application of inverse statistical physics techniques therefore leads to systematic biases and an effective reduction of the sample size.
Disordered Systems and Neural Networks Statistical Mechanics Quantitative Methods
no code implementations • 23 Nov 2020 • Pierre Barrat-Charlaix, Anna Paola Muntoni, Kai Shimagaki, Martin Weigt, Francesco Zamponi
For example, pairwise Potts models (PM), which are instances of the BM class, provide accurate statistical models of families of evolutionarily related protein sequences.
no code implementations • 18 May 2020 • Anna Paola Muntoni, Andrea Pagnani, Martin Weigt, Francesco Zamponi
Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information.
1 code implementation • 3 Mar 2017 • Simona Cocco, Christoph Feinauer, Matteo Figliuzzi, Remi Monasson, Martin Weigt
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved.