Defensins are small cysteine-rich, cationic peptides that play an essential role in the innate immune system of virtually all life forms, from insects and plants to amphibians and mammals. Defensins are mainly an innate immunity element, exhibiting antibacterial activities by disrupting the cell membrane of a wide range of organisms (Cole et al. 2002). Defensins also affect certain adaptive immune responses, including enhancing phagocytosis, promoting neutrophil recruitment, and enhancing the production of proinflammatory cytokines. The aim of this thesis is to make a comprehensive and accurate subfamily classification of the defensin gene family, primarily by using a library of Hidden Markov Models (HMMs). In this project the subfamily classification of the defensin gene family is primarily based on a constructed library of HMMs. Results: Sets of known defensins were organized in placed in 84 clusters using the clustering and alignment tool, FlowerPower. The clusters were further classified as mammalian alpha- or beta-defensins, plant defensin, insect defensin and defensin MGD. This classification was based on significant cluster hits against the Structural Classification of Proteins (SCOP) database and species distribution. Based on the relative positions of disulfide bonds and constructed Multiple Sequence Alignments (MSAs) some sequences were classified as belonging to the sperm– and theta-defensin subfamilies. Compared to PFAM’s classification of defensins, the subfamily classification presented here is more informative. The library of HMMs has been made public via a web server that was used to automatically score and analyze input sequences against the created database of HMMs. This database and web server are expected to be useful to researchers working on various aspects of defensin action.

<p>Defensins are small cysteine-rich, cationic peptides that play an essential role in the innate immune system of virtually all life forms, from insects and plants to amphibians and mammals. Defensins are mainly an innate immunity element, exhibiting antibacterial activities by disrupting the cell membrane of a wide range of organisms (Cole et al. 2002). Defensins also affect certain adaptive immune responses, including enhancing phagocytosis, promoting neutrophil recruitment, and enhancing the production of proinflammatory cytokines.</p><p>The aim of this thesis is to make a comprehensive and accurate subfamily classification of the defensin gene family, primarily by using a library of Hidden Markov Models (HMMs). In this project the subfamily classification of the defensin gene family is primarily based on a constructed library of HMMs. Results: Sets of known defensins were organized in placed in 84 clusters using the clustering and alignment tool, FlowerPower. The clusters were further classified as mammalian alpha- or beta-defensins, plant defensin, insect defensin and defensin MGD. This classification was based on significant cluster hits against the Structural Classification of Proteins (SCOP) database and species distribution. Based on the relative positions of disulfide bonds and constructed Multiple Sequence Alignments (MSAs) some sequences were classified as belonging to the sperm– and theta-defensin subfamilies. Compared to PFAM’s classification of defensins, the subfamily classification presented here is more informative. The library of HMMs has been made public via a web server that was used to automatically score and analyze input sequences against the created database of HMMs. This database and web server are expected to be useful to researchers working on various aspects of defensin action.</p>