Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions

Medicine & Life Sciences

1. Amino Acid Substitution 100%
2. Ligands 37%
3. Proteins 33%
4. Molecular Evolution 33%
5. Missense Mutation 27%
6. Cyclic AMP Receptor Protein 20%
7. Molecular Medicine 18%
8. Repressor Proteins 18%
9. Protein Interaction Maps 16%
10. Human Genetics 16%
11. Lactose 15%
12. Molecular Dynamics Simulation 14%
13. Preliminary Data 14%
14. Protein Binding 13%
15. Genetic Variation 12%
16. Open Reading Frames 12%
17. Peptide Hydrolases 11%
18. Molecular Biology 10%
19. Binding Sites 10%
20. Escherichia coli 9%
21. Amino Acids 9%
22. Technology 8%
23. Phenotype 7%