@inproceedings{89b567c4384746238380f29d8a3a6ac7,
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 protein-protein (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.",
keywords = "Protein-protein interaction, affinity, mean force of potential, protein complex, quaternary structure, residue level",
author = "Li, {Xue Ling} and Hou, {Mei Ling} and Wang, {Shu Lin}",
year = "2010",
doi = "10.1007/978-3-642-14922-1_85",
language = "English (US)",
isbn = "3642149219",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "680--686",
booktitle = "Advanced Intelligent Computing Theories and Applications - 6th International Conference on Intelligent Computing, ICIC 2010, Proceedings",
note = "6th International Conference on Intelligent Computing, ICIC 2010 ; Conference date: 18-08-2010 Through 21-08-2010",
}