ArticleProteins2020
Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions.
Article in Proteins, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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Who cites it
21 citing papers in PubMed.
- Computational design of 3C-like protease substrate peptide for modular detection of protease activity of coronavirus.Engineering in life sciences · 2026Article
- Applying Computational Protein Design to Engineer Affibodies for Affinity-controlled Delivery of Vascular Endothelial Growth Factor and Platelet-Derived Growth Factor.Biomacromolecules · 2025Article
- Computational optimization of a pan-coronavirus fusion inhibitory peptide targeting spike's heptapeptide repeat region.Biosafety and health · 2025Article
- Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.Cold Spring Harbor perspectives in biology · 2024Review
- An end-to-end deep learning method for protein side-chain packing and inverse folding.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Protein Design Using Physics Informed Neural Networks.Biomolecules · 2023Article
- Designing peptides predicted to bind to the omicron variant better than ACE2 via computational protein design and molecular dynamics.PloS one · 2023Article
- Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin.ACS catalysis · 2022Article
- Protein design via deep learning.Briefings in bioinformatics · 2022Review
- The stability and dynamics of computationally designed proteins.Protein engineering, design & selection : PEDS · 2022Review
- Computational design of Lactobacillus Acidophilus α-L-rhamnosidase to increase its structural stability.PloS one · 2022Article
- Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.Nature communications · 2021Article
- Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy.Peptides · 2021Article
- Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor.Scientific reports · 2021Article
- Molecular basis of the new COVID-19 target neuropilin-1 in complex with SARS-CoV-2 S1 C-end rule peptide and small-molecule antagonists.Journal of molecular liquids · 2021Article
- Perturbing the energy landscape for improved packing during computational protein design.Proteins · 2021Article
- Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.PloS one · 2021Article
- An Expanded Survey of the Moth PBP/GOBP Clade inFrontiers in physiology · 2021Article
- An automated protocol for modelling peptide substrates to proteases.BMC bioinformatics · 2020Article
- Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.
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Registered trials
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