Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the interaction energy of different amino acid residues with the ligand and predicts the ones where binding energy is at a minimum to be potential binding sites. However, examining conserved sequences is often used in conjunction with other methodologies to enhance this prediction further. Structurally conserved residues can be used to distinguish between binding sites and exposed protein surfaces. The amino acids, Trp, Phe, and Met, are highly conserved in binding sites, and no such conservation is observed in the case of exposed protein surfaces.
Various computational tools can predict binding sites using a mix of structural, energetic, and conserved binding site methodologies. ConCavity is a tool that can be used to predict 3D ligand-binding pockets and individual ligand-binding residues. The algorithm used directly integrates evolutionary sequence conservation estimates with structure-based prediction. Another tool, MONKEY, is used to identify conserved transcription-factor binding sites in multispecies alignments. It employs factor specificity and binding-site evolution models to compute the likelihood that putative sites are conserved and assign statistical significance to each prediction.
