StruLocPred: structure-based protein subcellular localisation prediction using multi-class support vector machine.
Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.
Bioinformatics and Computational Biology Program, Electrical and Computer Engineering Department, Virtual Reality Applications Center, Iowa State University, Ames, IA 50011, USA. wgzhou@iastate.edu
Wengang
W
Zhou
Julie A
JA
Dickerson
D022082:Cellular Structures / Q000378:metabolism; D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism; D012984:Software; D060388:Support Vector Machine
D011506:Proteins