Cluster and information entropy patterns in immunoglobulin complementarity determining regions

Previous studies of antibody binding domains have established many crucial features that include important structural positions, canonical formations, and the geometric correlations with the binding site nature and topography. In this work, position-specific frequency and hierarchical clustering analysis are used to explore the statistical pattern of the residues in the complementarity determining regions of human antibodies. In addition, Shannon’s information entropy is computed for the entire heavy and light chains and compared with germline patterns to seek variability due to antibody clonal selection. Results are compared with reported analyses based on structural data and ligand-protein contact point computations based on Protein Data Bank records. Observations derived from the present sequence analysis are consistent with previous structural based methods. In the absence of structural data, methods used in this work can be effective and efficient computational tools used for identifying residues that are important for antigen targeting and predicting the probable amino acid distribution expected at these positions. The results in turn can be applied to help design or plan mutagenesis experiments to improve the binding properties of antibodies.