A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins.

By G. Dantas, Brian Kuhlman, David R. Callender, Michelle Wong, David Baker. 2003

317 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. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Jens Meiler
5. I. Samish
6. David E. Kim
7. Rhiju Das
8. Po-Ssu Huang

In Molecular Biology

1. David Baker
2. Tanja Kortemme
3. Jeffrey J. Gray
4. Jens Meiler
5. I. Samish
6. Rhiju Das
7. Po-Ssu Huang
8. Yang Zhang

Citations in the corpus

• ProDCoNN: Protein design using a convolutional neural network (2019, background): “18 Hu and coworkers developed a nonlinear scoring function for selecting sequences comparable with a given structure.”
• Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, positive): “…et al. , 2008; Rothlisberger et al. , 2008; Zanghellini et al. , 2006) FixedBBProteinDesign Redesign the amino acids on a fixed protein backbone (Dantas et al. , 2003; Kortemme et al. , 2004; Kuhlman and Baker, 2000) protein_docking Predict the docked conformation of two proteins with a known…”
• High-resolution Structural and Thermodynamic Analysis of Extreme Stabilization of Human Procarboxypeptidase by Computational Protein Design (2007, methodology): “RosettaDesign was previously used to redesign completely the sequence of the activation domain of human procarboxypeptidase A2.”
• A de novo protein binding pair by computational design and directed evolution (2011, background): “In contrast to standard RosettaDesign, which has proven useful in designing hydrophobic cores for monomeric proteins (Dantas et al., 2007; Dantas et al., 2003; Kuhlman et al., 2003), our method focuses on the unique requirements and characteristics of a dissociable protein-protein interface.”
• A Generic Program for Multistate Protein Design (2011, background): “The last fifteen years have produced remarkable advances in the field of protein design [1–3], with the redesign and stabilization of existing proteins [4–10], the de novo design of protein structures [11,12], the altering of existing protein functionality [13,14], the redesign of existing protein/p”
• The highly cooperative folding of small naturally occurring proteins is likely the result of natural selection. (2007, background): “…phagedisplay technology (Gu et al., 1995; Riddle et al., 1997), while computational protein design has allowed for the complete redesign of sequences capable of folding to naturally occurring structures (Dahiyat and Mayo, 1997; Dantas et al., 2003; Dobson et al., 2006; Gillespie et al., 2003).”
• Achievements and Challenges in Computational Protein Design. (2017, methodology): “In 2003 the Baker lab applied RosettaDesign for the redesign of nine different globular folds achieving, on average 65 % deviation in sequence space with biochemical characteristics comparable with their natural templates [45].”
• Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles (2014, methodology): “In our previous work, we found that incorporation of the fragment-derived profile into the RosettaDesign energy function 42 can increase the sequence identity by 4–8% 17 .”
• RosettaDesign server for protein design (2006, methodology): “It has been used to stabilize naturally occurring proteins (9), enhance protein binding affinities (10), design a protein that can switch between 2-folds (11) and create a protein with a novel structure (12).”