Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint-Reference-Cited by-同舟云学术

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Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint

Published:2024-04-03 Issue:4 Volume:13 Page:1085-1092
ISSN:2161-5063
Container-title:ACS Synthetic Biology
language:en
Short-container-title:ACS Synth. Biol.

Author:

Ertelt Moritz¹²^ORCID,Meiler Jens¹²³⁴,Schoeder Clara T.¹²

Affiliation:

1. Institute for Drug Discovery, University Leipzig Medicine Faculty, Liebigstr. 19, D-04103 Leipzig, Germany

2. Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI, D-04105 Leipzig, Germany

3. Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States

4. Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States

Funder

Bundesministerium f?r Bildung und Forschung

Alexander von Humboldt-Stiftung

S?chsisches Staatsministerium f?r Wissenschaft, Kultur und Tourismus

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssynbio.3c00753

Reference35 articles.

2. Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus

3. De Novo Computational Design of Retro-Aldol Enzymes

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

5. Highly accurate protein structure prediction with AlphaFold

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PEL-PVP: Application of plant vacuolar protein discriminator based on PEFT ESM-2 and bilayer LSTM in an unbalanced dataset;International Journal of Biological Macromolecules;2024-10

2. Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants;2024-06-24

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