Graphical Models of Residue Coupling in Protein Families

By John Thomas, Naren Ramakrishnan, C. Bailey‐Kellogg. 2008

15 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. Alexander Rives
2. Tom Sercu
3. Roshan Rao
4. C. Bailey‐Kellogg
5. Sergey Ovchinnikov
6. C. Lawrence Zitnick
7. Rob Fergus
8. Jerry Ma

In Molecular Biology

1. Sergey Ovchinnikov
2. Hetunandan Kamisetty
3. Alexander Rives
4. Tom Sercu
5. Roshan Rao
6. Joshua Meier
7. Christopher James Langmead
8. Bornika Ghosh

Citations in the corpus

• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “Direct coupling analysis (Lapedes et al., 1999; Thomas et al., 2008; Weigt et al., 2009) infers constraints on the structure of a protein by fitting a generative model in the form of a Markov Random Field (MRF) to the sequences in the protein’s MSA.”
• Transformer protein language models are unsupervised structure learners (2020, background): “This was proposed by Lapedes et al. (1999) and reintroduced by Thomas et al. (2008) and Weigt et al. (2009).”
• MODELING AND INFERENCE OF SEQUENCE-STRUCTURE SPECIFICITY (2009, methodology): “We have developed an algorithm for learning the edges of a graphical model of residue coupling, and demonstrated its effectiveness in a variety of proteins, including GPCRs and PDZs [23].”