New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical-chemical properties

Mathura Venkatarajan, Werner Braun

Research output: Contribution to journalReview article

115 Scopus citations


We derive new quantitative descriptors for the 20 naturally occurring amino acids based on multidimensional scaling of 237 physical-chemical properties. We show that a five-dimensional property space can be constructed such that the amino acids are in a similar spatial distribution to that in the original high-dimensional property space. Properties that correlate well with the five major components were hydrophobicity, size, preferences for amino acids to occur in α-helices, number of degenerate triplet codons and the frequency of occurrence of amino acid residues in β-strands. Distances computed for pairs of amino acids in the five-dimensional property space are highly correlated with corresponding scores from similarity matrices derived from sequence and 3D structure comparison. We used the five-dimensional property distances to cluster the amino acids in groups depending on a cutoff distance. These groups define a reduced amino acid alphabet for protein folding studies. Our descriptors should provide a quantitative means to identify property motifs in sequences of protein families. Electronic supplementary material to this paper can be obtained by using the Springer Link server located at

Original languageEnglish (US)
Pages (from-to)445-453
Number of pages9
JournalJournal of Molecular Modeling
Issue number12
StatePublished - Jan 1 2001



  • Amino acid
  • Cluster analysis
  • Multidimensional scaling
  • PAM
  • Physical-chemical properties
  • Substitution matrices

ASJC Scopus subject areas

  • Catalysis
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Computational Theory and Mathematics
  • Inorganic Chemistry

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