Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

By Noah Ollikainen, René M. de Jong, Tanja Kortemme. 2015

76 citations recorded by the source corpus.

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.

Subfields: Materials Chemistry, Molecular Biology

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

In Materials Chemistry

  1. Xingjie Pan
  2. Jinbo Xu
  3. Tanja Kortemme
  4. Matthew McPartlon
  5. David Baker
  6. Sheng Wang
  7. Jeffrey J. Gray
  8. I. Samish

In Molecular Biology

  1. Xingjie Pan
  2. Tanja Kortemme
  3. Jinbo Xu
  4. Matthew McPartlon
  5. Sheng Wang
  6. David Baker
  7. Jeffrey J. Gray
  8. Yang Zhang

Citations in the corpus