ArticleProteins2013
Dead-end elimination with perturbations (DEEPer): a provable protein design algorithm with continuous sidechain and backbone flexibility.
Article in Proteins, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Entropy quantum computing for fixed-backbone protein design.Scientific reports · 2026Article
- Protocol for DesigningJournal of computational biology : a journal of computational molecular cell biology · 2024Article
- Refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data.Methods in enzymology · 2023Article
- Knowledge-Based Unfolded State Model for Protein Design.Methods in molecular biology (Clifton, N.J.) · 2022Article
- qFit 3: Protein and ligand multiconformer modeling for X-ray crystallographic and single-particle cryo-EM density maps.Protein science : a publication of the Protein Society · 2021Article
- New computational protein design methods for de novo small molecule binding sites.PLoS computational biology · 2020Article
- Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface.PLoS computational biology · 2020Article
- Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.International journal of molecular sciences · 2020Review
- Minimization-Aware RecursiveJournal of computational biology : a journal of computational molecular cell biology · 2020Article
- Computational Analysis of Energy Landscapes Reveals Dynamic Features That Contribute to Binding of Inhibitors to CFTR-Associated Ligand.The journal of physical chemistry. B · 2019Article
- Protein Design by Provable Algorithms.Communications of the ACM · 2019Article
- Journey to the center of the protein: allostery from multitemperature multiconformer X-ray crystallography.Acta crystallographica. Section D, Structural biology · 2019Article
- OSPREY 3.0: Open-source protein redesign for you, with powerful new features.Journal of computational chemistry · 2018Article
- Continuous Interdomain Orientation Distributions Reveal Components of Binding Thermodynamics.Journal of molecular biology · 2018Article
- iCFN: an efficient exact algorithm for multistate protein design.Bioinformatics (Oxford, England) · 2018Article
- BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Data driven flexible backbone protein design.PLoS computational biology · 2017Article
- CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions.Bioinformatics (Oxford, England) · 2017Article
- LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid Rotamer-Like Efficiency.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- A critical analysis of computational protein design with sparse residue interaction graphs.PLoS computational biology · 2017Article
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3 authors.
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Abstract
Computational protein and drug design generally require accurate modeling of protein conformations. This modeling typically starts with an experimentally determined protein structure and considers possible conformational changes due to mutations or new ligands. The DEE/A* algorithm provably finds the global minimum-energy conformation (GMEC) of a protein assuming that the backbone does not move and the sidechains take on conformations from a set of discrete, experimentally observed conformations called rotamers. DEE/A* can efficiently find the overall GMEC for exponentially many mutant sequences. Previous improvements to DEE/A* include modeling ensembles of sidechain conformations and either continuous sidechain or backbone flexibility. We present a new algorithm, DEEPer (Dead-End Elimination with Perturbations), that combines these advantages and can also handle much more extensive backbone flexibility and backbone ensembles. DEEPer provably finds the GMEC or, if desired by the user, all conformations and sequences within a specified energy window of the GMEC. It includes the new abilities to handle arbitrarily large backbone perturbations and to generate ensembles of backbone conformations. It also incorporates the shear, an experimentally observed local backbone motion never before used in design. Additionally, we derive a new method to accelerate DEE/A*-based calculations, indirect pruning, that is particularly useful for DEEPer. In 67 benchmark tests on 64 proteins, DEEPer consistently identified lower-energy conformations than previous methods did, indicating more accurate modeling. Additional tests demonstrated its ability to incorporate larger, experimentally observed backbone conformational changes and to model realistic conformational ensembles. These capabilities provide significant advantages for modeling protein mutations and protein-ligand interactions.
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