Amino acid encoding schemes from protein structure alignments: multi-dimensional vectors to describe residue types.
Bioinformatic software has used various numerical encoding schemes to describe amino acid sequences. Orthogonal encoding, employing 20 numbers to describe the amino acid type of one protein residue, is often used with artificial neural network (ANN) models. However, this can increase the model complexity, thus leading to difficulty in implementation and poor performance. Here, we use ANNs to derive encoding schemes for the amino acid types from protein three-dimensional structure alignments. Each of the 20 amino acid types is characterized with a few real numbers. Our schemes are tested on the simulation of amino acid substitution matrices. These simplified schemes outperform the orthogonal encoding on small data sets. Using one of these encoding schemes, we generate a colouring scheme for the amino acids in which comparable amino acids are in similar colours. We expect it to be useful for visual inspection and manual editing of protein multiple sequence alignments.
Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill, NW, 7 1AA, UK.
Kuang
K
Lin
Alex C W
AC
May
William R
WR
Taylor
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D003116:Color; D006801:Humans; D016571:Neural Networks, Computer; D016415:Sequence Alignment