Engineering of stable and fast-folding sequences of model proteins.

By Eugene I. Shakhnovich, A. Gutin. 1993

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Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “, for the engineering of stable and fast-folding proteins [39], for the analysis of correlated network states in neural populations [40], regulatory gene network modeling from transcript profiles [41], to extract residue-residue interactions from nucleotide sequences [42–43]), as well as derivation ”
Power law tails in phylogenetic systems (2018, background): “In general a set of homologous protein sequences is constrained by protein structure and function, and with sufﬁcient data it is possible to tease out the nature of these constraints and make biologically relevant predictions (12, 13, 16, 28–32).”
The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. (2005, background): “The native structure of folded proteins corresponds to a pronounced energy minimum, with no other conformations having comparable energy.(44,45) Ensuring this energetic separation demands the native structure to efficiently use the interactions compatible with the given sequence.”
Patterns of coevolving amino acids unveil structural and dynamical domains (2017, background): “…have also followed different routes: from the development of increasingly accurate force ﬁelds to be used in unbiased folding simulations (8, 9) to higher-level approaches where structural features are inferred from the sole physicochemical or statistical proﬁling of the primary sequence (32–36).”
3D Protein Structure Predicted from Sequence (2011, unknown): “, for the engineering of stable and fast-‐folding proteins [37], for the analysis of correlated network states in neural populations [38], regulatory gene network modeling from transcript profiles [39], t”
Protein design: a perspective from simple tractable models (1998, methodology): “The simplest approach of this kind was proposed in 1993 in [9].”
Developing optimal non-linear scoring function for protein design (2004, methodology): “sN: H(f(sN,aN)) < H(f(sN,aD)) for all aD ∈ D. Equivalently, the native sequence will have the highest probability to ﬁt into the speciﬁed native structure. This is the same principle described in (Shakhnovich and Gutin, 1993; Deutsch and Kurosky, 1996; Li et al., 1996). Sometimes we can further r”
Long- and short-range interactions in native protein structures are consistent/minimally frustrated in sequence space. (2003, methodology): “Similar methods were previously used for optimizing protein sequences for a given structure.(23,24)”
Positive and Negative Design in Stability and Thermal Adaptation of Natural Proteins (2006, background): “el of protein design. Two factors may play a role. The first effect is due to fundamental statistical mechanics of proteins that posits that stable and foldable proteins should have an ‘’energy gap’’ [9, 10, 11, 12]. Specifically, the stability of the native state of a protein is determined by the B”
How to derive a protein folding potential? A new approach to an old problem. (1996, methodology): “A sequence design algorithm has been recently developed which generates sequences with specified relative energy (Z-score, Bowie et al., 1991) in a given conformation (Shakhnovich & Gutin, 1993a,b; Abkevich et al., 1995a).”
Protein flexibility and intrinsic disorder (2004, methodology): “To compensate for the large variability of averages over proteins, CB-factors were normalized using the method of Smith et al. (2003) prior to quantization.”