ArticleJournal of chemical theory and computation2018
Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.
Article in Journal of chemical theory and computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Deep learning based design of buried hydrogen bond networks with HBDesigner.bioRxiv : the preprint server for biology · 2026Article
- Self-assembling protein cages: from coiled-coil module to machine learning-drivenMaterials advances · 2026Review
- Article
- The open-source Masala software suite: Facilitating rapid methods development for synthetic heteropolymer design.bioRxiv : the preprint server for biology · 2025Article
- Computational design of pH-sensitive binders.bioRxiv : the preprint server for biology · 2025Article
- Design of nanobody targeting SARS-CoV-2 spike glycoprotein using CDR-grafting assisted by molecular simulation and machine learning.PLoS computational biology · 2025Article
- Design of pseudosymmetric protein hetero-oligomers.Nature communications · 2024Article
- A suite of designed protein cages using machine learning and protein fragment-based protocols.Structure (London, England : 1993) · 2024Article
- Article
- De novo design of monomeric helical bundles for pH-controlled membrane lysis.Protein science : a publication of the Protein Society · 2023Article
- A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols.bioRxiv : the preprint server for biology · 2023Article
- Structural Quantification of the Surface-Confined Metal-Organic Precursors Simulated with the Lattice Monte Carlo Method.Molecules (Basel, Switzerland) · 2023Article
- Protein Design: From the Aspect of Water Solubility and Stability.Chemical reviews · 2022Review
- Massively parallel, computationally guided design of a proenzyme.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- From Protein Design to the Energy Landscape of a Cold Unfolding Protein.The journal of physical chemistry. B · 2022Article
- Protein sequence design with a learned potential.Nature communications · 2022Article
- Hexaconazole exposure ravages biosynthesis pathway of steroid hormones: revealed by molecular dynamics and interaction.Toxicology research · 2022Article
- Interpreting Neural Networks for Biological Sequences by Learning Stochastic Masks.Nature machine intelligence · 2022Article
- XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.PLoS computational biology · 2021Article
- De novo design of tyrosine and serine kinase-driven protein switches.Nature structural & molecular biology · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.