https://github.com/johnnytam100/awesome-protein-design

A curated list of awesome protein design research, software and resources.
https://github.com/johnnytam100/awesome-protein-design

List: awesome-protein-design

alphafold alphafold-multimer alphafold2 bioinformatics bioinformatics-tool molecular-biology protein-design protein-engineering protein-folding protein-function protein-protein-interaction protein-sequence protein-structure protein-structure-prediction structural-bioinformatics structural-biology

Last synced: 15 days ago
JSON representation

A curated list of awesome protein design research, software and resources.

• Host: GitHub
• URL: https://github.com/johnnytam100/awesome-protein-design
• Owner: johnnytam100
• Created: 2022-05-10T04:32:44.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-05-10T09:00:16.000Z (about 2 years ago)
• Last Synced: 2024-05-21T03:12:14.707Z (about 1 month ago)
• Topics: alphafold, alphafold-multimer, alphafold2, bioinformatics, bioinformatics-tool, molecular-biology, protein-design, protein-engineering, protein-folding, protein-function, protein-protein-interaction, protein-sequence, protein-structure, protein-structure-prediction, structural-bioinformatics, structural-biology
• Homepage:
• Size: 29.3 KB
• Stars: 11
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome-protein-design-software - awesome-protein-design (johnnytam100)

README

        
# awesome-protein-design

A curated list of awesome protein design research, software and resources.

# Research articles

  

  [Low-N protein engineering with data-efficient deep learning](https://www.nature.com/articles/s41592-021-01100-y)

  

  

  

  [Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design](https://pubs.acs.org/doi/pdf/10.1021/acs.biochem.1c00757)  

  

  

  

  

  [Protein design via deep learning](https://www.researchgate.net/profile/Haipeng-Gong/publication/359594457_Protein_design_via_deep_learning/links/62626176bca601538b5dfd47/Protein-design-via-deep-learning.pdf)

  

  

  

  

  [Generating functional protein variants with variational autoencoders](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008736)

  

  

  

  

  [How Deep Learning Tools Can Help Protein Engineers Find Good Sequences](https://pubs.acs.org/doi/10.1021/acs.jpcb.1c02449?ref=PDF#)

  

  

  

  

  [Efficient generative modeling of protein sequences using simple autoregressive models](https://www.nature.com/articles/s41467-021-25756-4/figures/1)

  

  

  

  

  [Machine learning to navigate fitness landscapes for protein engineering](https://www.sciencedirect.com/science/article/pii/S0958166922000465)

  

  

  [Advances in machine learning for directed evolution](https://www.sciencedirect.com/science/article/pii/S0959440X21000154#bbib0275)

  

  

  

  

  [Deep Learning in Protein Structural Modeling and Design](https://www.sciencedirect.com/science/article/pii/S2666389920301902#fig3)