a rapid method for characterization of protein relatedness using feature vectors快速亲缘鉴定蛋白质的利用特征向量的方法.pdfVIP

A Rapid Method for Characterization of Protein Relatedness Using Feature Vectors 1 2 1 3 1 Kareem Carr , Eleanor Murray , Ebenezer Armah , Rong L. He , Stephen S.-T. Yau * 1 Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, Illinois, United States of America, 2 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America, 3 Department of Biological Sciences, Chicago State University, Chicago, Illinois, United States of America Abstract We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the sequence. The feature vector characterizes the distance between the actual sequence and a model of a theoretical sequence based on the binomial and uniform distributions. This method is distinctive in that it does not rely on sequence alignment for determining protein relatedness, allowing the user to visualize the relationships within a set of proteins without making a priori assumptions about those proteins. We apply our method to two large families of proteins: protein kinase C, and globins, including hemoglobins and myoglobins. We interpret the high-dimensional feature vectors using principal components analysis and agglomerative hierarchical clustering. We find that the feature vector retains much of the information about the original sequence. By using principal component analysis to extract information from collections of feature vectors, we are able to quickly identify t