TY - GEN
T1 - A Gaussian evolutionary method for predicting protein-protein interaction sites
AU - Liu, Kang Ping
AU - Yang, Jinn-Moon
PY - 2007/4
Y1 - 2007/4
N2 - Protein-protein interactions play a pivotal role in modern molecular biology. Identifying the protein-protein interaction sites is great scientific and practical interest for predicting protein-protein interactions. In this study, we proposed a Gaussian Evolutionary Method (GEM) to optimize 18 features, including ten atomic solvent and eight protein 2nd structure features, for predicting protein-protein interaction sites. The training set consists of 104 unbound proteins selected from PDB and the predicted successful rate is 65.4% (68/104) proteins in the training dataset. These 18 parameters were then applied to a test set with 50 unbound proteins. Based on the threshold obtained from the training set, our method is able to predict the binding sites for 98% (49/50) proteins and yield 46% successful prediction and 42.3% average specificity. Here, a binding-site prediction is considered successful if 50% predicted area is indeed located in protein-protein interface (i.e. the specificity is more than 0.5). We believe that the optimized parameters of our method are useful for analyzing protein-protein interfaces and for interfaces prediction methods and protein-protein docking methods.
AB - Protein-protein interactions play a pivotal role in modern molecular biology. Identifying the protein-protein interaction sites is great scientific and practical interest for predicting protein-protein interactions. In this study, we proposed a Gaussian Evolutionary Method (GEM) to optimize 18 features, including ten atomic solvent and eight protein 2nd structure features, for predicting protein-protein interaction sites. The training set consists of 104 unbound proteins selected from PDB and the predicted successful rate is 65.4% (68/104) proteins in the training dataset. These 18 parameters were then applied to a test set with 50 unbound proteins. Based on the threshold obtained from the training set, our method is able to predict the binding sites for 98% (49/50) proteins and yield 46% successful prediction and 42.3% average specificity. Here, a binding-site prediction is considered successful if 50% predicted area is indeed located in protein-protein interface (i.e. the specificity is more than 0.5). We believe that the optimized parameters of our method are useful for analyzing protein-protein interfaces and for interfaces prediction methods and protein-protein docking methods.
KW - Atomic solvation parameter
KW - Gaussian evolutionary method
KW - Protein-protein binding site
KW - Protein-protein interactions
UR - http://www.scopus.com/inward/record.url?scp=38049095109&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:38049095109
SN - 9783540717829
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 143
EP - 154
BT - Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 5th European Conference, EvoBIO 2007, Proceedings
T2 - 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007
Y2 - 11 April 2007 through 13 April 2007
ER -