ReviewAnnual review of biophysics2013
Energy functions in de novo protein design: current challenges and future prospects.
Review in Annual review of biophysics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- High-throughput system for the thermostability analysis of proteins.Protein science : a publication of the Protein Society · 2024Article
- Graphormer supervised de novo protein design method and function validation.Briefings in bioinformatics · 2024Article
- SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.PLoS computational biology · 2023Article
- Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding.Molecules (Basel, Switzerland) · 2023Review
- Propensities of Some Amino Acid Pairings in α-Helices Vary with Length.The protein journal · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Discovering the Ultimate Limits of Protein Secondary Structure Prediction.Biomolecules · 2021Article
- Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps.Journal of chemical information and modeling · 2021Article
- Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map.Journal of cheminformatics · 2021Article
- Computational Design of PDZ-Peptide Binding.Methods in molecular biology (Clifton, N.J.) · 2021Article
- A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.PloS one · 2021Article
- A physics-based energy function allows the computational redesign of a PDZ domain.Scientific reports · 2020Article
- Article
- Consensus sequence design as a general strategy to create hyperstable, biologically active proteins.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- A Synthetic Derivative of Antimicrobial Peptide Holothuroidin 2 from Mediterranean Sea Cucumber (Marine drugs · 2019Article
- In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences.iScience · 2019Article
- Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.Journal of chemical theory and computation · 2018Article
- Computational Protein Design with Deep Learning Neural Networks.Scientific reports · 2018Article
- Environment-transformable sequence-structure relationship: a general mechanism for proteotoxicity.Biophysical reviews · 2018Review
- Computational tools help improve protein stability but with a solubility tradeoff.The Journal of biological chemistry · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.
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