In silico sequence analysis, homology modeling and function annotation of leishmanolysin from Leishmania donovani

详细信息    查看全文

• 作者：Somnath Waghmare ; Abhishek Buxi ; Yogesh Nandurkar…
• 关键词：Leishmania ; Homology modeling ; Leishmanolysin ; 3D structure
• 刊名：Journal of Parasitic Diseases
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：40
• 期：4
• 页码：1266-1269
• 全文大小：
• 刊物主题：Infectious Diseases; Health Promotion and Disease Prevention; Medicine/Public Health, general;
• 出版者：Springer India
• ISSN：0975-0703
• 卷排序：40

文摘

Leishmaniases are a complex of diseases that range from the deadly visceral disease and some self-curing lesions to gross disfigurations. About 12 million peoples from 88 different countries get infected by this protozoan parasite through the sand flies. Visceral leishmaniasis is a potentially fatal disease endemic to large parts of Asia and Africa, primarily caused by the protozoan parasite Leishmania donovani. L. donovani is a species of Leishmania, a hemoflagellate parasite and causative agent of visceral leishmaniasis. Leishmanolysin is the major surface protein of the parasitic Leishmania. Leishmanolysin has been described as a parasite virulence factor and is involved in the direct interaction of promastigotes and host macrophage receptors and interaction with the complement cascade. In the current study we predicted the 3D structure of leishmanolysin using homology modeling as 3D structure prediction approach. Leishmanolysin is a stable extracellular stable protein of 561 amino acid residues. 3D structure of the leishmanolysin was determined using Protein Structure Prediction Server (PS₂ Server) selecting MODELLER as 3D structure prediction method. Quality analysis of the model through its Ramchandran Plot and ERRAT value (94.25) indicated that it is a reliable model. Functional annotation showed that this protein is a member of the superfamily cl18220. The information thus discussed provides insight to the molecular understanding of structure and function of leishmanolysin from L. donovani. The predicted 3-D model may be further used in characterizing the protein in wet laboratory.