An information theoretic approach to macromolecular modeling: I. Sequence alignments.
We are interested in applying the principles of information theory to structural biology calculations. In this article, we explore the information content of an important computational procedure: sequence alignment. Using a reference state developed from exhaustive sequences, we measure alignment statistics and evaluate gap penalties based on first-principle considerations and gap distributions. We show that there are different gap penalties for different alphabet sizes and that the gap penalties can depend on the length of the sequences being aligned. In a companion article, we examine the information content of molecular force fields.
Aynechi
Tiba
T
Graduate Group in Biophysics, and Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA 94143, USA.
Kuntz
Irwin D
ID
D000465:Algorithms; D001703:Biophysics / Q000379:methods; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D046911:Macromolecular Substances; D008958:Models, Molecular; D015233:Models, Statistical; D008962:Models, Theoretical; D008968:Molecular Conformation; D009696:Nucleic Acids / Q000737:chemistry; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software; D013816:Thermodynamics
D046911:Macromolecular Substances; D009696:Nucleic Acids; D011506:Proteins