The number and arrangement of subunits that form a protein are referred to as quaternary structure.Knowing the quaternary structure of an uncharacterized protein provides clues to finding its biological function and i...The number and arrangement of subunits that form a protein are referred to as quaternary structure.Knowing the quaternary structure of an uncharacterized protein provides clues to finding its biological function and interaction process with other molecules in a biological system.With the explosion of protein sequences generated in the Post-Genomic Age,it is vital to develop an automated method to deal with such a challenge.To explore this prob-lem,we adopted an approach based on the pseudo position-specific score matrix(Pse-PSSM)descriptor,proposed by Chou and Shen,representing a protein sample.The Pse-PSSM descriptor is advantageous in that it can combine the evolution information and sequence-correlated informa-tion.However,incorporating all these effects into a descriptor may cause‘high dimension disaster’.To over-come such a problem,the fusion approach was adopted by Chou and Shen.A completely different approach,linear dimensionality reduction algorithm principal component analysis(PCA)is introduced to extract key features from the high-dimensional Pse-PSSM space.The obtained dimension-reduced descriptor vector is a compact repre-sentation of the original high dimensional vector.The jack-knife test results indicate that the dimensionality reduction approach is efficient in coping with complicated problems in biological systems,such as predicting the quaternary struc-ture of proteins.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.60704047).
文摘The number and arrangement of subunits that form a protein are referred to as quaternary structure.Knowing the quaternary structure of an uncharacterized protein provides clues to finding its biological function and interaction process with other molecules in a biological system.With the explosion of protein sequences generated in the Post-Genomic Age,it is vital to develop an automated method to deal with such a challenge.To explore this prob-lem,we adopted an approach based on the pseudo position-specific score matrix(Pse-PSSM)descriptor,proposed by Chou and Shen,representing a protein sample.The Pse-PSSM descriptor is advantageous in that it can combine the evolution information and sequence-correlated informa-tion.However,incorporating all these effects into a descriptor may cause‘high dimension disaster’.To over-come such a problem,the fusion approach was adopted by Chou and Shen.A completely different approach,linear dimensionality reduction algorithm principal component analysis(PCA)is introduced to extract key features from the high-dimensional Pse-PSSM space.The obtained dimension-reduced descriptor vector is a compact repre-sentation of the original high dimensional vector.The jack-knife test results indicate that the dimensionality reduction approach is efficient in coping with complicated problems in biological systems,such as predicting the quaternary struc-ture of proteins.
