Jonathan D. Jou

Orcid: 0000-0001-7172-9032

Affiliations:
  • Duke University, Durham, NC, USA


According to our database1, Jonathan D. Jou authored at least 10 papers between 2016 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2020
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 Comput. Biol., 2020

Minimization-Aware Recursive K*: A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape.
J. Comput. Biol., 2020

2019
Minimization-Aware Recursive K^* K ∗ ( MARK^* MARK ∗ ): A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape.
Proceedings of the Research in Computational Molecular Biology, 2019

2018
Novel Computational Protein Design Algorithms with Sparse Residue Interaction Graphs, Ensembles, and Mathematical Guarantees, and their Application to Antibody Design.
PhD thesis, 2018

OSPREY 3.0: Open-source protein redesign for you, with powerful new features.
J. Comput. Chem., 2018

<i>BBK* (Branch and Bound Over K*): </i> A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces.
J. Comput. Biol., 2018

2017
A critical analysis of computational protein design with sparse residue interaction graphs.
PLoS Comput. Biol., 2017

LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid Rotamer-Like Efficiency.
J. Comput. Biol., 2017

BBK<sup>*</sup> (Branch and Bound over K<sup>*</sup>): A Provable and Efficient Ensemble-Based Algorithm to Optimize Stability and Binding Affinity over Large Sequence Spaces.
Proceedings of the Research in Computational Molecular Biology, 2017

2016
BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.
J. Comput. Biol., 2016


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