The coming of age of de novo protein design

By Po-Ssu Huang, Scott E. Boyken, David Baker. 2016

1,345 citations recorded by the source corpus.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. David Baker
2. Kevin Kaichuang Yang
3. Yang Zhang
4. Jeffrey J. Gray
5. Tanja Kortemme
6. Xingjie Pan
7. Po-Ssu Huang
8. Mohammed Alquraishi

In Radiology, Nuclear Medicine and Imaging

1. Sarel Jacob Fleishman
2. Frances H. Arnold
3. Enrique Marcos
4. Tanja Kortemme
5. Zachary Wu
6. Kevin Kaichuang Yang
7. William F. DeGrado
8. Xingjie Pan

In Computational Biology & Bioinformatics

1. Kevin Kaichuang Yang
2. David Baker
3. Mohammed Alquraishi
4. Yang Zhang
5. Debora Marks
6. Po-Ssu Huang
7. Jeffrey J. Gray
8. Chengxin Zhang

Citations in the corpus

• BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020, background): “Protein science, and especially protein engineering, has historically been driven by experimental, wet-lab methodologies along with biophysical, structure-based intuitions and computational techniques [6, 33, 69].”
• Generating functional protein variants with variational autoencoders (2020, background): “Recent progress has also been made in the rational design of proteins 43 with artificial folds from scratch [7, 8, 9].”
• ProGen: Language Modeling for Protein Generation (2020, background): “This performance improves in settings with larger amino acid contexts and when ProGen is provided a larger number of conditioning tags, which highlights its potential for applications in providing viable, starting sequences for directed evolution or de novo protein design (Huang et al., 2016).”
• Learning inverse folding from millions of predicted structures (2022, background): “Designing novel amino acid sequences that encode proteins with desired properties, known as de novo protein design, is a central challenge in bioengineering (Huang et al., 2016).”
• Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus (2019, background): “computational protein design (Huang et al., 2016), continued development of this structure-based approach to designing self-assembling immunogens could be useful for improving the potency, durability, and breadth of vaccines against a number of important pathogens.”
• Energy-based models for atomic-resolution protein conformations (2020, background): “Huang et al. (2016) have argued that since the physical principles that govern protein conformation apply to all proteins, encoding knowledge of these physical and biochemical principles into an energy function will make it possible to design de novo new protein structures and functions that have…”