Name
Abstract | ||
|---|---|---|
Atomic level changes to a ligand can affect significantly how a drug interacts with a protein, which in turn can determine whether a pharmaceutical agent is effective in treating a disease. Designing and testing the effects of a new ligand is prohibitively expensive, and can take months -- or even years -- of laborious wet-lab work. To help guide researchers in understanding the effects of ligand variations, we have developed a computational pipeline that for a protein-ligand resolved PDB structure file, generates all possible ligand variants in silico, and analyze all of the protein-ligand complexes using a combinatorial rigidity analysis approach. We present several visualizations, and show that some atoms of ligands have an especially pronounced effect on the stability of the protein-ligand complex.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307339.3343862 | BCB |
Field | DocType | ISBN |
Rigidity (psychology),Protein–ligand complex,Computer science,Artificial intelligence,Computational biology,Machine learning | Conference | 978-1-4503-6666-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hunter Read | 1 | 0 | 0.34 |
| Dylan Carpenter | 2 | 0 | 0.34 |
| Sam Herr | 3 | 0 | 0.34 |
| Filip Jagodzinski | 4 | 71 | 14.83 |