Name
Title | ||
|---|---|---|
A residual level potential of mean force based approach to predict protein-protein interaction affinity |
Abstract | ||
|---|---|---|
We develop a knowledge-based statistical energy function on residual level for quantitatively predicting the affinity of protein-protein complexes by using 20 residue types and a distance-free reference state. The correlation coefficients between experimentally measured protein-protein binding affinities (PPIA) and the predicted affinities by our approach are 0.74 for 82 proteinprotein (peptide) complexes. Compared to the published results of two other volume corrected knowledge-based scoring functions on atomic level, the proposed approach not only is the simplest but also yields the comparable correlation between theoretical and experimental binding affinities of the test sets with the reported best methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14922-1_85 | ICIC (1) |
Keywords | Field | DocType |
atomic level,comparable correlation,experimental binding affinity,knowledge-based statistical energy function,residual level potential,mean force,protein-protein complex,residual level,best method,correlation coefficient,protein-protein interaction affinity,protein-protein binding affinity,potential of mean force,knowledge base,score function,protein complex,protein binding,binding affinity,protein protein interaction | Residual,Protein–protein interaction,Potential of mean force,Pattern recognition,Residue (complex analysis),Biological system,Computer science,Peptide,Correlation,Artificial intelligence,Protein quaternary structure,Affinities | Conference |
Volume | ISSN | ISBN |
6215 | 0302-9743 | 3-642-14921-9 |
Citations | PageRank | References |
3 | 0.49 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueling Li | 1 | 10 | 1.64 |
| Mei-Ling Hou | 2 | 3 | 0.49 |
| Shulin Wang | 3 | 27 | 7.13 |