Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Más filtros

Banco de datos
Tipo del documento
País de afiliación
Intervalo de año de publicación
1.
Proc Natl Acad Sci U S A ; 121(26): e2312335121, 2024 Jun 25.
Artículo en Inglés | MEDLINE | ID: mdl-38889151

RESUMEN

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution.


Asunto(s)
Mutación , Proteínas , Proteínas/genética , Proteínas/metabolismo , Proteínas/química , Epistasis Genética , Evolución Molecular , Biología Computacional/métodos
2.
Phys Rev E ; 108(6-1): 064301, 2023 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-38243526

RESUMEN

Continuous attractor neural networks (CANN) form an appealing conceptual model for the storage of information in the brain. However a drawback of CANN is that they require finely tuned interactions. We here study the effect of quenched noise in the interactions on the coding of positional information within CANN. Using the replica method we compute the Fisher information for a network with position-dependent input and recurrent connections composed of a short-range (in space) and a disordered component. We find that the loss in positional information is small for not too large disorder strength, indicating that CANN have a regime in which the advantageous effects of local connectivity on information storage outweigh the detrimental ones. Furthermore, a substantial part of this information can be extracted with a simple linear readout.

SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA