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Found 11 papers, 3 papers with code
Emergent time scales of epistasis in protein evolution
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.
Towards Parsimonious Generative Modeling of RNA Families
Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences.
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs.
Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes
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.
adabmDCA: Adaptive Boltzmann machine learning for biological sequences
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.
Modeling sequence-space exploration and emergence of epistatic signals in protein evolution
During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection.
Efficient generative modeling of protein sequences using simple autoregressive models
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.
Global multivariate model learning from hierarchically correlated data
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
Sparse generative modeling via parameter-reduction of Boltzmann machines: application to protein-sequence families
For example, pairwise Potts models (PM), which are instances of the BM class, provide accurate statistical models of families of evolutionarily related protein sequences.
Aligning biological sequences by exploiting residue conservation and coevolution
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.
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
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.
