An Atomistic Statistically Effective Energy Function for Computational Protein Design.

By C. Topham, S. Barbe, I. André. 2016

11 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Gevorg Grigoryan
2. Huali Cao
3. A. Panaitiu
4. Yifei Qi
5. Jingxue Wang
6. Jianfu Zhou
7. J. H. Zhang

In Molecular Biology

1. A. Panaitiu
2. Gevorg Grigoryan
3. Jianfu Zhou
4. Huali Cao
5. Yifei Qi
6. Jingxue Wang
7. J. H. Zhang
8. Xingjie Pan

Citations in the corpus

• Computational Protein Design with Deep Learning Neural Networks (2018, methodology): “30,31 Recently, two statistical potentials for protein design have been developed, 32,33 and the ABACUS potential 34 has been successfully used in designing proteins.”
• A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures (2018, methodology): “PANENERGY has been shown to perform well on predicting mutational protein stability changes and protein-ligand binding free energies, decoy discrimination, and native sequence recovery (49).”
• Protein sequence design with a learned potential (2020, unknown): “Design methods that account for flexibility of the protein backbone often use “softer” potentials [49, 54], including statistical potentials derived from data [55, 56, 55, 57, 58].”