CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences-Reference-Cited by-同舟云学术

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

Published:2015-12-28 Issue:24 Volume:143 Page:243123
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:The Journal of Chemical Physics

Author:

Ruff Kiersten M.¹,Harmon Tyler S.²,Pappu Rohit V.³

Affiliation:

1. Computational and Systems Biology Program and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130-4899, USA

2. Department of Physics and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130-4899, USA

3. Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis, CB 1097, St. Louis, Missouri 63130-4899, USA

Funder

Foundation for the National Institutes of Health (FNIH)

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/1.4935066

Reference128 articles.

1. Nup98 FG domains from diverse species spontaneously phase-separate into particles with nuclear pore-like permselectivity

2. From Micelles to Fibers: Balancing Self-Assembling and Random Coiling Domains in pH-Responsive Silk-Collagen-Like Protein-Based Polymers

3. Nuclear Pore Complex Protein Sequences Determine Overall Copolymer Brush Structure and Function