Analysis and prediction of functionally important sites in proteins

Abstract

The rapidly increasing volume of sequence and structure information available for proteins poses the daunting task of determining their functional importance. Computational methods can prove to be very useful in understanding and characterizing the biochemical and evolutionary information contained in this wealth of data, particularly at functionally important sites. Therefore, we perform a detailed survey of compositional and evolutionary constraints at the molecular and biological function level for a large set of known functionally important sites extracted from a wide range of protein families. We compare the degree of conservation across different functional categories and provide detailed statistical insight to decipher the varying evolutionary constraints at functionally important sites. The compositional and evolutionary information at functionally important sites has been compiled into a library of functional templates. We developed a module that predicts functionally important columns (FIC) of an alignment based on the detection of a significant "template match score" to a library template. Our template match score measures an alignment column's similarity to a library template and combines a term explicitly representing a column's residue composition with various evolutionary conservation scores (information content and position-specific scoring matrix-derived statistics). Our benchmarking studies show good sensitivity/specificity for the prediction of functional sites and high accuracy in attributing correct molecular function type to the predicted sites. This prediction method is based on information derived from homologous sequences and no structural information is required. Therefore, this method could be extremely useful for large-scale functional annotation.

Figure 1.

Degree of conservation across molecular functional categories. Degree of conservation of FICs is compared across six molecular functional categories (see Table 1 and text for details) based on its median conservation score m_c (a), rate of substitution R_c (b), and information content I_c (c). The central line in each box shows the median value, the upper and lower boundaries of an individual box show the upper and lower quartiles, and the vertical lines extend to a value of 1.5 times the interquartile range. Outlier values are shown outside the whiskers.

Figure 2.

Degree of conservation across biological functional categories. Degree of conservation of FICs is compared across 16 biological functional categories (see Table 2 and the text for details) based on its median conservation score m_c (upper panel), rate of substitution R_c (middle panel), and information content I_c (lower panel).

Figure 3.

Examples of functionally important site prediction. (a) Cartoon representation of response regulator protein PleD (PDB code 1W25). Correctly predicted active sites and inhibitor binding sites are colored in cyan; functional sites missed by our prediction module are marked either by purple (active sites) or orange (inhibitor binding sites). (b) Structure of DNA (ligand) binding Phob effector Domain (PDB code 1GXP, chain A). (c) Structure of a Ran-binding protein Mog1p. Correctly predicted functional sites for ligand binding (b) and protein binding (c) are colored in cyan, whereas functional sites that are missed by our prediction module are marked by purple. (d) Structure of Human Long-[Arg3] insulin-like growth factor 1 (PDB code 3LRI, chain A, region 17–74). All predicted functionally important sites are colored in cyan, whereas correctly predicted sites are marked in stick representation.