title: Parameterization studies for the SAM and HMMER methods of hidden Markov model generation.
abstract: Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generation of multiple sequence alignments. The results of studies attempting to infer appropriate parameter constraints for the generation of de novo HMMs for globin, kinase, aspartic acid protease, and ribonuclease H sequences by both the SAM and HMMER methods are described.
authors.affiliation: Department of Biological Sciences, UNLV 89129, USA.
authors.forename: M A
authors.initials: MA
authors.lastname: McClure
authors.forename: C
authors.initials: C
authors.lastname: Smith
authors.forename: P
authors.initials: P
authors.lastname: Elton
mesh_terms: D016282:Aspartic Acid Endopeptidases / Q000737:chemistry; D005914:Globins / Q000737:chemistry; D008390:Markov Chains; D011494:Protein Kinases / Q000737:chemistry; D016914:Ribonuclease H / Q000737:chemistry; D016415:Sequence Alignment; D012984:Software; D014764:Viral Proteins / Q000737:chemistry*
chemical_list: D014764:Viral Proteins; D005914:Globins; D011494:Protein Kinases; D016914:Ribonuclease H; D016282:Aspartic Acid Endopeptidases