Transmembrane segment prediction from protein sequence data.
We consider the automated identification of transmembrane domains in membrane protein sequences. 324 proteins (containing 1585 segments) were examined, representing every protein in the PIR database having the transmembrane domain feature annotation. Machine learning techniques were used to evaluate the efficacy of alternative hydrophobicity measures and windowing techniques. We describe a simpler measure of hydrophobicity and a new variable window size concept. We demonstrate that these techniques are superior to some previous techniques in minimizing the segment error rate. Using these new techniques, we describe an algorithm that has a 7.9% segment error rate on the sampled proteins, while classifying 16.7% of the amino acid residues as transmembrane.
Weiss
S M
SM
Department of Computer Science, Rutgers University, New Brunswick, New Jersey 08903, USA.
Cohen
D M
DM
Indurkhya
N
N
D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D005544:Forecasting; D008565:Membrane Proteins; D008969:Molecular Sequence Data; D015203:Reproducibility of Results; D017421:Sequence Analysis / Q000379:methods*
D000596:Amino Acids; D008565:Membrane Proteins