Top-Down Machine Learning of Coarse-Grained Protein Force Fields-Reference-Cited by-同舟云学术

Top-Down Machine Learning of Coarse-Grained Protein Force Fields

Published:2023-10-24 Issue:21 Volume:19 Page:7518-7526
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Navarro Carles¹^ORCID,Majewski Maciej¹^ORCID,De Fabritiis Gianni²³⁴^ORCID

Affiliation:

1. Acellera Labs, Doctor Trueta 183, 08005 Barcelona, Spain

2. Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003 Barcelona, Spain

3. Acellera Ltd., Devonshire House 582, Middlesex HA7 1JS, United Kingdom

4. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain

Funder

Agencia Estatal de Investigaci?n

Ministerio de Ciencia e Innovaci?n

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c00638

Reference63 articles.

1. Conformational changes associated with protein–protein interactions

2. Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations

3. Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations

4. The Native Ensemble and Folding of a Protein Molten-Globule: Functional Consequence of Downhill Folding

5. Ligand-induced dynamical regulation of NO conversion in Mycobacterium tuberculosis truncated hemoglobin-N

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