The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach

By S. Romero-Romero, M. Costas, Daniel-Adriano Silva Manzano, S. Kordes, Erendira Rojas-Ortega, Cinthya Tapia, Y. Guerra, S. Shanmugaratnam, A. Rodríguez‐Romero, David Baker and 2 more. 2021

23 citations recorded by the source corpus.

Graphical abstract

Subfields: Materials Chemistry, Molecular Biology, Oncology

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. Alexander Derry
2. Po-Ssu Huang
3. Namrata Anand
4. Raphael R. Eguchi
5. Russ B. Altman
6. Carla P. Perez
7. Birte Höcker
8. I. Mathews

In Molecular Biology

1. Namrata Anand
2. Raphael R. Eguchi
3. Alexander Derry
4. Po-Ssu Huang
5. Russ B. Altman
6. Steffen Schmidt
7. Carla P. Perez
8. Noelia Ferruz

In Oncology

1. Raphael R. Eguchi
2. Po-Ssu Huang

Citations in the corpus

• Protein sequence design with a learned potential (2022, background): “Of particular interest are mutations in the interfaces between the helices surrounding the barrel where, remarkably, the design algorithm is able to fi nd novel structural features that have not been discovered by Rosetta and human-guided design methodologies 44,48 (Fig.”
• ProtGPT2 is a deep unsupervised language model for protein design (2022, background): “In particular, 1068 shows a TIM-barrel fold, a topology which to date has met impressive success in de novo design 62 – 64 , but whose idealized structure has nevertheless proven challenging to extend via additional secondary elements and longer loops 65,66 .”