Prediction of protein structural class using a complexity-based distance measure

• Published in: Amino acids 2010-03, Vol.38 (3), p.721-728
• Main Authors: Liu, Taigang, Zheng, Xiaoqi, Wang, Jun
• Format: Article
• Language: English
• Subjects: Algorithms; Amino Acid Motifs; Amino acids; Analytical Chemistry; Animals; Benchmarking; Biochemical Engineering; Biochemistry; Biomedical and Life Sciences; Classifiers; Computation; Databases, Protein; Engines; Folding; Humans; Life Sciences; Models, Molecular; Neurobiology; Original Article; Protein folding; Protein Structure, Secondary; Proteins; Proteins - chemistry; Proteins - classification; Proteomics; Proteomics - methods; Studies; Symbols
• ISSN: 0939-4451; 1438-2199
• DOI: 10.1007/s00726-009-0276-1

Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.