Graphical models of protein–protein interaction specificity from correlated mutations and interaction data

By John Thomas, Naren Ramakrishnan, C. Bailey‐Kellogg. 2009

34 citations recorded by the source corpus.

Subfields: 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 Molecular Biology

1. Hetunandan Kamisetty
2. Sivaraman Balakrishnan
3. Jaime G. Carbonell
4. Christopher James Langmead
5. Su-In Lee
6. Sergey Ovchinnikov
7. David Baker
8. Martin Weigt

Citations in the corpus

• Learning generative models for protein fold families (2011, methodology): “They then extended this approach to incorporate interaction data to learn models over pairs of interacting proteins [13] and also develop a sampling algorithm for protein design using such models [14].”
• Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “This approach has been tested on some of the same examples as SCA, namely GPCRs [71], and PDZ domains [72].”
• Finding and Leveraging Structure in Learning Problems (2012, methodology): “They then extended this approach to incorporate interaction data to learn models over pairs of interacting proteins [187] and also develop a sampling algorithm for protein design using such models [189].”