Principal component analysis of dipeptides

Roberta Susnow, Clarence Schutt, Herschel Rabitz

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)963-980
Number of pages18
JournalJournal of Computational Chemistry
Issue number9
StatePublished - Sep 1994

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Computational Mathematics


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