De novo design of the hydrophobic cores of proteins

By J. Desjarlais, Tracy M. Handel. 1995

How researchers see this paper

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

Citations in the corpus

ProDCoNN: Protein design using a convolutional neural network (2019, background): “In 1995, Desjarlais and Handel designed a computational framework for the de novo design of hydrophobic cores.(15) Raha and coworkers developed the sequence Received: 19 August 2019 Revised: 13 December 2019 Accepted: 14 December 2019”
Protein design is NP-hard. (2002, background): “The protein design problem may be formulated in many different ways; here, we focus on a simple definition that has gained significant attention (Desjarlais and Handel, 1995; Dahiyat and Mayo, 1996, 1997; Gordon and Mayo, 1998; Malakauskas and Mayo, 1998; Koehl and Levitt, 1999; Pierce et al.,…”
Computational redesign of protein-protein interaction specificity (2004, background): “There have been great successes in the computational design of monomeric proteins such as protein core”
Emergence of Protein Fold Families through Rational Design (2006, methodology): “However, because such a study is extremely time-consuming using a highresolution protein model [23], previous investigations were limited to generating thermodynamically stable sequences through successive mutations in a protein for a given structure [24–28].”
Achievements and Challenges in Computational Protein Design. (2017, background): “The method was exemplified on the phage 434 Cro helical protein with five to eight amino acid changes in the hydrophobic core.”
The penultimate rotamer library (2000, background): “The concept of rotamers and the availability of rotamer libraries has changed the handling of side chains in homology modeling,(12) Monte Carlo and combinatorial calculations,(13) and protein design.(14,15) Side-chain rotamer libraries are incorporated into crystallographic model-to-map fitting prog”
De novo protein design, a retrospective (2020, methodology): “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).”
Prediction of amino acid sequence from structure (2000, methodology): “This is not surprising, given the considerable success reported for computational design of hydrophobic cores ~ Hellinga & Richards, 1994; Kono & Doi, 1994; Desjarlais & Handel, 1995; Dahiyat & Mayo, 1996, 1997b; Lazar et al., 1997; Kono et al., 1998 !”
Computational design of antibody-affinity improvement beyond in vivo maturation (2007, background): “Previous work has addressed many problems related to the design of improved protein-protein binding affinity, such as the design of stable protein fold”
De novo protein design: towards fully automated sequence selection. (1997, methodology): “…applied and experimentally tested systematic, quantitative methods to protein design with the goal of developing general design algorithms (Hellinga et al., 1991; Hurley et al., 1992; Desjarlais & Handel, 1995; Harbury et al., 1995; Klemba et al., 1995; Betz & Degrado, 1996; Dahiyat & Mayo, 1996).”
Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms. (1999, background): “Some of the most successful recent protein redesigns (e.g. Desjarlais & Handel, 1995; Struthers et al., 1996; Dahiyat & Mayo, 1997) were modelled using explicit H contacts between (although not within) residues; some cases have achieved well-ordered structures in which only local regions depart…”