Gapped sequence alignment using artificial neural networks: application to the MHC class I system

MOTIVATION: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. RESULTS: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of…

• UD
  U.S. Department of Health and Human Services

  Award: HHSN272201200010C
• CN
  Consejo Nacional de Investigaciones Científicas y Técnicas
• NI
  National Institutes of Health

  Award: HHSN272201200010C
• AN
  Agencia Nacional de Promoción Científica y Tecnológica

  Awards: PICT-2012-0115, PICT-2012-0115
• NI
  National Institute of Allergy and Infectious Diseases

  Award: HHSN272201200010C