A comparative protein function analysis database of different Leishmania strains

Abstract

A complete understanding of different protein functional families and template information opens new avenues for novel drug development. Protein identification and analysis software performs a central role in the investigation of proteins and leads to the development of refined database for description of proteins of different Leishmania strains. There are certain databases for different strains that lack template information and functional family annotation. Rajendra Memorial Research Institute of Medical Sciences (RMRIMS) has developed a web-based unique database to provide information about functional families of different proteins and its template information in different Leishmania species. Based on the template information users can model the tertiary structure of protein. The database facilitates significant relationship between template information and possible protein functional families assigned to different proteins by SVMProt. This database is designed to provide comprehensive descriptions of certain important proteins found in four different species of Leishmania i.e. L. donovani, L. infantum, L. major and L. braziliensis. A specific characterization information table provides information related to species and specific functional families. This database aims to be a resource for scientists working on proteomics. The database is freely available at http://biomedinformri.org/calp/. Background: Kala-azar or Leishmaniasis is identified by clinical syndromes caused by obligate intracellular protozoa of the genus Leishmania and transmitted from one host to another by the bite of blood sucking sand fly vectors [1]. The genomes of three species have been sequenced. There are relatively few species-specific differences in gene content between the sequenced genomes, but nearly 8% of the genes appear to be evolving at different rates [2]. Knowledge about protein function is essential in the understanding of biological processes [3]. No computational functional analysis of different proteins of Leishmania is available till date. As the gap between the amount of sequence information and functional characterization widens, increasing efforts are being intended for the construction of databases. For scientist, it is therefore helpful to have a single data collection point, which integrates research interrelated data from diverse domains. Large scale of protein sequences is available at the National Center for Biotechnology Information (NCBI) protein database [4] and supplementary data in the published literature. In silico analysis gives us an idea on the role of different proteins in replication, survival and spread in the host [5]. Computational proteomics of Leishmania (CPL) involves the general tasks related to analysis of any novel sequences, such as functional annotation and template information of the sequences. Support vector machine (SVM) is a useful classifier for predicting the functional classes of distantly related proteins [6, 7]. The function of a protein depends on its tertiary structure. The structure and function of a protein gives much more insight of the protein than its sequence [8]. Structural genomics are yielding many protein structures that have unknown function. Nevertheless, successive experimental investigation is costly and time-consuming, which makes computational methods for predicting protein function very attractive [9].