Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design

By Jack B. Maguire, Scott E. Boyken, David Baker, Brian Kuhlman. 2018

51 citations recorded by the source corpus.

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems, but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements, but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Protein sequence design with a learned potential (2022, background): “…elds, capturing these effects require terms that accurately describe Van der Waals, solvation, hydrogen bonding, as well as many other interactions; it also likely requires an independent hydrogen-bonding network search algorithm 49 and discrete side-chain representations from rotamer libraries.”
Energy-based models for atomic-resolution protein conformations (2020, background): “Incorporation of higher order terms has been an active research area for energy function design (Maguire et al., 2018).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “A Monte Carlo version of the HBNet method uses a stochastic algorithm to traverse the HBNet graph (101).”
Modular repeat protein sculpting using rigid helical junctions (2020, unknown): “We also filtered out models with buried unsatisfied hydrogen bonds (13) and then used Rosetta de novo structure prediction calculations to identify sequences strongly specifying the designed structures in silico (Fig.”