Accurate de novo design of hyperstable constrained peptides

By G. Bhardwaj, Vikram Khipple Mulligan, Christopher D. Bahl, Jason M. Gilmore, P. Harvey, O. Cheneval, G. W. Buchko, S. Pulavarti, Q. Kaas, A. Eletsky and 17 more. 2016

389 citations recorded by the source corpus.

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18–47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N–C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

Subfields: Microbiology, Molecular Biology

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Citations in the corpus

Computational Protein Design with Deep Learning Neural Networks (2018, background): “…are subject to further filtering by considering various factors such as shape complementarity 9 and in silico folding free energy landscape, 27,28 where human experience and familiarity with the designed protein play an important role, indicating that there is a gap between current design…”
Protein sequence design by conformational landscape optimization (2021, methodology): “The quality of the energy landscape was quantified with Pnear (28), which approximates the Boltzmann-weighted probability of the structure adapting the target conformation (fuzzy cutoff):”
The coming of age of de novo protein design (2016, methodology): “An example of this is the design of hyperstable peptides , which are constrained by disulfide crosslinks and cyclic peptide linkages that connect the N and C termini 84 .”
De novo protein design, a retrospective (2020, neutral): “Moreover, repeat proteins (discussed below), mini-proteins, and cyclic peptides including ones with D-amino acids or unnatural crosslinks have been prepared and structurally verified (Bhardwaj et al., 2016; Chevalier et al., 2017; Dang et al., 2017; Marcos et al., 2018) (Fig.”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Notably, the structure prediction neural network from TR-Rosetta (42) can be repurposed for sequence optimization (109).”
Protein sequence design with a learned potential (2020, background): “Computational protein design has emerged as powerful tool for studying the conformational space of proteins, and has allowed us to expand the space of known protein folds [1, 2, 3, 4, 5, 6], create variations on existing topologies [7, 8, 9, 10, 11], and access the expansive space of sequences yet t”
A computational method for the design of nested proteins by loop‐directed domain insertion (2018, background): “…protein structures of each Insert Pair undergo iterative loop closure cycles of a generalized kinematic loop closure (KIC)-based algorithm, 36,42,43 which is a computationally inexpensive calculation that idealizes the peptide bonds at the four chain breakpoints (two at each end of each linker…”
De novo design of covalently constrained mesosize protein scaffolds with unique tertiary structures (2017, methodology): “Rosetta’s generalized kinematic closure module was next used to sample loop conformations connecting the helices (42).”
Reconfigurable asymmetric protein assemblies through implicit negative design (2021, positive): “The interface residues of the docked hetero-dimer complexes were optimized using Rosetta combinatorial sequence ( 37 – 40 ) design using “ ref2015, ” “ beta_nov16, ” or “ beta_genpot ” as score functions ( 41 ).”
Essentials of de novo protein design: Methods and applications (2018, methodology): “Nevertheless, this method has been widely studied and successfully applied to build folds of various degrees of complexity, ranging from miniproteins 38,39 to large folds of highly nonlocal character.”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, background): “23 Rosetta is maintained by the RosettaCommons, a collaborative association of more than 45 principal investigators and collaborators at 55 institutions.”