Minimum Message Length Inference of Protein Alignments and Statistical Models of Amino Acid Evolution

Abstract

Protein comparison via alignment is an indispensable first step for many biological studies. It forms a lynchpin to understand how proteins come about, function and evolve. The classical problem of protein sequence alignment is a broadly investigated topic, yet carries several limitations in the current state of the art. This thesis approaches the problem using the Bayesian criterion of Minimum Message Length that combines inductive inference with information theory and lossless data compression. It also develops a complete set of statistical models for sequence alignment, and at the end proposes a method to combine sequence with structure when searching for alignment relationships.