Cargando…
Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan
The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable...
Autores principales: | Fernandez-de-Cossio-Diaz, Jorge, Uguzzoni, Guido, Pagnani, Andrea |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7783173/ https://www.ncbi.nlm.nih.gov/pubmed/32770229 http://dx.doi.org/10.1093/molbev/msaa204 |
Ejemplares similares
-
AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape
por: Sesta, Luca, et al.
Publicado: (2021) -
Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes
por: Melnikov, Alexandre, et al.
Publicado: (2014) -
Inferring Genome-Wide Correlations of Mutation Fitness Effects between Populations
por: Huang, Xin, et al.
Publicado: (2021) -
Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1
por: Figliuzzi, Matteo, et al.
Publicado: (2016) -
Efficient generative modeling of protein sequences using simple autoregressive models
por: Trinquier, Jeanne, et al.
Publicado: (2021)