De novo protein design: fully automated sequence selection.

By B. Dahiyat, Stephen L. Mayo. 1997

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Citations in the corpus

Generating functional protein variants with variational autoencoders (2020, background): “Beyond the identification of sequence variants, computational approaches have 39 enabled the generation of small synthetic protein domains that mimic natural folds while using 40 sequences that are distant from what is seen in nature [4, 5, 6].”
Computational design of receptor and sensor proteins with novel functions (2003, background): “Difficulties in structure-based computational design arise from limitations in the description of the molecular interactions and the combinatorial complexity of the proble”
De novo protein design, a retrospective (2020, background): “As mentioned above, Dahiyat and Mayo had also succeeded in the fully automated redesign of the sequence of a zinc finger peptide, resulting in a peptide that folded into a structure consisting of an α-helix packed against an antiparallel β-hairpin (Richardson and Richardson, 2002).”
A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins. (2003, background): “1–8 A rigorous test for these models is the complete redesign of naturally occurring proteins.”
Experimental illumination of a fitness landscape (2011, background): “Indeed, although tremendous strides have been made in understanding the relationship between protein structure and stability (17, 21), the ability to predict from structure the most important stabilizing contacts remains an unmet challenge.”
Protein sequence design with a learned potential (2020, methodology): “Methods for optimizing over residues and rotamers include variations on the dead-end elimination (DEE) algorithm [32, 33, 34, 35], integer linear programming [36, 37], belief propagation [38, 39], and Markov Chain Monte Carlo (MCMC) with simulated annealing [40, 41, 42].”
Side-chain Packing Using SE(3)-Transformer (2021, background): “Introduction The protein side-chain packing problem is important for both protein structure prediction (Kaden, Koch, and Selbig 1990) and protein design (Dahiyat and Mayo 1997).”
Protein design: a perspective from simple tractable models (1998, methodology): “Interesting methods to account for sidechain stereochemistry in sequence selections have been developed [6,88,89] that use the dead-end elimination theorem or Monte-Carlo design, which takes into account the degrees of freedom of the sidechains [90].”
An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. (2003, methodology): “Additional rotamers were included with small deviations (10–208) of the x1 and x2 angles for buried residues, and extra angles for x3 and x4 angles as described by Mayo & co-workers.(58) For serine, threonine and tyrosine, hydrogen conformations were chosen according to those observed in neutron dif”
Positive and Negative Design in Stability and Thermal Adaptation of Natural Proteins (2006, background): “successful [1,3], rational design of proteins with desired thermal properties is still an elusive goal.”
Protein Engineering by Combined Computational and In Vitro Evolution Approaches. (2016, methodology): “Computational protein design The CPD methodology is implemented in several programs [84–86] that all use the same basic principles to compute the amino acid sequences most compatible with a given 3D protein structure.”