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Principal component analysis of dipeptides
Author(s) -
Susnow Roberta,
Schutt Clarence,
Rabitz Herschel
Publication year - 1994
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540150906
Subject(s) - principal component analysis , component (thermodynamics) , chemistry , computer science , artificial intelligence , physics , thermodynamics
Principal component analysis applied to a set of dipeptides illustrates how changes in families of parameters act in concert to produce overall molecular structural changes. Principal component analysis is an eigenvalue–eigenvector analysis whereby the parametric sensitivity coefficient matrix is manipulated to produce weighted principal components, which reveal the variant and invariant directions in the parameter space. This analysis summarizes the sensitivity results by revealing interdependence among the parameter values with regard to their role in controlling the molecular structure. An analysis of the principal components reveals hidden relationships among the parameters. Thus, those parameters, which were thought to be of controlling significance with respect to the molecular structure, may, in fact, not be (or vice versa) due to cooperative parametric interactions; as a result, the parameters of significance in a sequence of dipeptides are identified. In general, for the dipeptides studied, there is mutual exclusion of dominant parameters between the sets of invariant and variant eigenvectors. © 1994 by John Wiley & Sons, Inc.