Numerical Representation of Amino Acid Sequences
Abstract
Amino acid substitution matrices attempt to quantify the rates at which the different amino acid residues in proteins are functionally substituted by other amino acid residues with similar biochemical properties. Currently, the most widely utilized substitution matrices comprise BLOSUM-62, PAM-120 and PAM250 among others. Percent recognized mutation (PAM) matrices list the probability of transition from one amino acid to another during evolution of homologous protein sequences and are therefore based on monitoring protein evolutionary origins. In comparison, the amino acid substitution matrices of the blocks (BLOSUM) are based on scoring substitutions observed over a number of cycles of evolution. Traditionally, amino acids have been represented by single letter alphabetic codes but in this study, we will be using Principal Component Analysis (PCA) applied to amino acid physiochemical properties in order to assign numerical values to the various amino acid residues. Then we will compare the result with existing substitution matrices to learn how this method may quantify the physiochemical basis for functional substitution in proteins.
Faculty Sponsors
Dr. Radleigh Santos
Project Type
Event
Location
Alvin Sherman Library
Start Date
4-6-2021 12:00 PM
End Date
4-9-2021 12:00 PM
Numerical Representation of Amino Acid Sequences
Alvin Sherman Library
Amino acid substitution matrices attempt to quantify the rates at which the different amino acid residues in proteins are functionally substituted by other amino acid residues with similar biochemical properties. Currently, the most widely utilized substitution matrices comprise BLOSUM-62, PAM-120 and PAM250 among others. Percent recognized mutation (PAM) matrices list the probability of transition from one amino acid to another during evolution of homologous protein sequences and are therefore based on monitoring protein evolutionary origins. In comparison, the amino acid substitution matrices of the blocks (BLOSUM) are based on scoring substitutions observed over a number of cycles of evolution. Traditionally, amino acids have been represented by single letter alphabetic codes but in this study, we will be using Principal Component Analysis (PCA) applied to amino acid physiochemical properties in order to assign numerical values to the various amino acid residues. Then we will compare the result with existing substitution matrices to learn how this method may quantify the physiochemical basis for functional substitution in proteins.
