Native protein sequences are close to optimal for their structures.

By Brian Kuhlman, David Baker. 2000

819 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, 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 Materials Chemistry

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Jens Meiler
5. Yang Zhang
6. Frank DiMaio
7. I. Samish
8. Brian Kuhlman

In Molecular Biology

1. David Baker
2. Tanja Kortemme
3. Jeffrey J. Gray
4. Jens Meiler
5. I. Samish
6. Yang Zhang
7. Xingjie Pan
8. Frank DiMaio

In Computational Biology & Bioinformatics

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Frank DiMaio
5. Jens Meiler
6. David E. Kim
7. Sergey Ovchinnikov
8. Po-Ssu Huang

Citations in the corpus

• Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models (2022, background): “However, for the task of structure generation, stochastic search algorithms based on handcrafted energy functions and heuristic sampling approaches are still in wide use [25, 34, 2, 26].”
• Protein sequence design with a learned potential (2022, background): “14 were seeded with native fragments. trRosetta structure prediction .”
• Learning from Protein Structure with Geometric Vector Perceptrons (2020, background): “Both metrics rest on the implicit assumption that native sequences are optimized for their structures (Kuhlman & Baker, 2000) and should be assigned high probabilities.”
• ProDCoNN: Protein design using a convolutional neural network (2019, background): “17,24 The average sequence recovery rates achieved by the current top-performing protein-design programs are around 35% on a small number of proteins.”
• Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “…Rosetta community, we have observed that improvements to the energy function, independently conceived to fix a particular aspect of Rosetta’s behavior, have produced improvements in sequence recovery when redesigning naturally occurring proteins (Kuhlman & Baker, 2000; Morozov & Kortemme, 2005).”
• Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, neutral): “ROSETTA was initially written in FORTRAN 77 as two separate programs for protein structure prediction (Simons et al. , 1997) and for protein design (Kuhlman and Baker, 2000), merged, mechanically ported to C þþ , and refactored for several years thereafter.”
• Perturbing the energy landscape for improved packing during computational protein design (2020, positive): “Side chain optimization is performed using Monte Carlo sampling, in which each Monte Carlo move involves replacing a side chain rotamer at a random sequence position with a different rotamer.(18) The change is accepted or rejected based on the calculated change in the energy of the protein and the M”
• Identifying the amylome, proteins capable of forming amyloid-like fibrils (2010, methodology): “Segments that can form a similar, self-complementary steric zipper structure with a low energy, as assessed by the RosettaDesign potential energy function (20), are judged as being capable of fibril formation.”
• 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): “The quality of predicted sequence profiles is comparable to the profiles generated from the protein design program RosettaDesign 3.5 29 based on several measures.”
• Computational Design of Ligand Binding Proteins with High Affinity and Selectivity (2013, methodology): “Design calculations were performed using RosettaDesign 29 .”
• Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors (2011, methodology): “For energy scoring, we use a modified version of the Rosetta full-atom energy function (Rosetta score12 [38,45], see below) and the coarsegrained energy function, which employs a unified spheres sidechains model (Rosetta centroid score4 [38]).”
• Energy functions in de novo protein design: current challenges and future prospects. (2013, methodology): “The RosettaDesign energy function (49, 57, 94) is made of 14 terms as shown in Equation 1 below: 1. where E ref and W are the optimized reference energy and weight factors for different energy terms, respectively.”