Tertiary alphabet for the observable protein structural universe

By Craig O. Mackenzie, Jianfu Zhou, Gevorg Grigoryan. 2016

72 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Materials Chemistry

1. Xingjie Pan
2. A. Panaitiu
3. Gevorg Grigoryan
4. Jianfu Zhou
5. Tanja Kortemme
6. William F. DeGrado
7. I. Korendovych
8. John Ingraham

In Molecular Biology

1. Xingjie Pan
2. A. Panaitiu
3. Tanja Kortemme
4. Gevorg Grigoryan
5. Jianfu Zhou
6. Zak Costello
7. William F. DeGrado
8. John Ingraham

Citations in the corpus

• Illuminating protein space with a programmable generative model (2022, background): “Natural protein structures exhibit considerable degeneracy in their use of local tertiary backbone geometries, such that completely unrelated proteins tend to use very similar tertiary motifs 37,38 .”
• De novo protein design, a retrospective (2020, background): “In parallel, the power of bioinformatics will increase dramatically with the inclusion of machine learning (Mackenzie et al., 2016; Mackenzie and Grigoryan, 2017; Eguchi and Huang, 2019).”
• Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “C , the TERMs-based scoring function (133) breaks proteins into tertiary structure motifs and evaluates the fi tness of the sequence for any local structure using the sequence pro fi les of the tertiary motifs.”
• A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures (2018, methodology): “For example, we found that only ~600 TERMs are sufficient to describe over 50% of the observable structural universe, and that TERMs tend to repeat many times, across proteins apparently unrelated by homology (42).”