FluMap is an online resource developed specifically to provide a rapid, comprehensive analysis of variations within a user-defined subset of influenza proteins. FluMap combines entropy methods, evolutionary information, and sequence-identity percentile algorithms to identify conserved and divergent elements between influenza proteins. FluMap then maps these elements onto a user-defined three-dimensional influenza protein structure. These methods are implemented to enable powerful computational analysis to be accessible to a broad spectrum of the influenza research community.
Analyses of residue divergence and conservation amongst the surface proteins of emerging influenza strains provide insight into functional changes during host and environmental adaptation of the virus, which is of critical value to the design of novel drugs and vaccines. FluMap, which is developed specifically as a tool to map mutations in influenza proteins, provides a reliable representation of sequence conservation within a user-defined population, which is independent of sequence size and controls for variable factors such as chain identity. FluMap is capable of conducting conservation analyses for more than a single polypeptide chain and this is convenient for studying influenza surface proteins, which form multiple-chain complexes.
Six scoring approaches are implemented in FluMap for measuring conservation scores among influenza proteins. These methods can be categorized into three groups (entropy method, evolutionary information, and sequence homology) as described below.
Shannon information theory has been widely used to measure residue conservation for aligned biological sequences. These aligned residues can be assigned into different numbers of groups: 6, 9, or 20 groups for amino acids (CHEM6, CHEM9, AA20). The residues in the same group will have the same entropy value.
Evolutionary mutation matrices, such as Block Substitution Matrix (BLOSUM62) and Point Accepted Mutation (PAM250), have been widely used in sequence comparison. FluMap adapted the scoring scheme by Karlin and Brocchieri (Karlin and Brocchieri, 1996).
FluMap also implements a simple score approach based on maximum residue identity percentile (MRIP) at each position. FluMap categorizes positions with a MRIP not less than an upper boundary (e.g. 90%) as a single group and the one with a MRIP less than a lower boundary (e.g. 50%) as another single group. The option of “HOMOLOGY” is implemented in FluMap.