Multiconformational Investigations of Polypeptidic Structures, Using Clustering Methods and Principal Components Analysis

Abstract Recently, we have applied random-search and energy minimization procedures in the framework of molecular mechanics in order to investigate the structural properties of polypeptides. Consequently, populations of conformers were generated and analyzed. The problem of the identification of conformation domains was addressed using factorial analysis such as clustering methods and principal components analysis. A strategy to apply such techniques to conformational populations is presented here. The results obtained for proline-containing peptides demonstrate the existence of five families of conformations, describing completely the generated population. The homogeneity and variability within families are discussed in terms of their characteristic molecular structures.

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