DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet-Reference-Cited by-同舟云学术

DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet

Published:2020-03-03 Issue:3 Volume:60 Page:1245-1252
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Qi Yifei¹²^ORCID,Zhang John Z. H.¹²³^ORCID

Affiliation:

1. Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China

2. NYU−ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China

3. Department of Chemistry, New York University, New York, New York 10003, United States

Funder

Natural Science Foundation of Shanghai

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00043

Reference60 articles.

1. Design of a Novel Globular Protein Fold with Atomic-Level Accuracy

2. De Novo Computational Design of Retro-Aldol Enzymes

3. Kemp elimination catalysts by computational enzyme design

4. Computational Design of Epitope-Scaffolds Allows Induction of Antibodies Specific for a Poorly Immunogenic HIV Vaccine Epitope

5. Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus

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