title: Mapping the mutual information network of enzymatic families in the protein structure to unveil functional features.
abstract: Amino acids committed to a particular function correlate tightly along evolution and tend to form clusters in the 3D structure of the protein. Consequently, a protein can be seen as a network of co-evolving clusters of residues. The goal of this work is two-fold: first, we have combined mutual information and structural data to describe the amino acid networks within a protein and their interactions. Second, we have investigated how this information can be used to improve methods of prediction of functional residues by reducing the search space. As a main result, we found that clusters of co-evolving residues related to the catalytic site of an enzyme have distinguishable topological properties in the network. We also observed that these clusters usually evolve independently, which could be related to a fail-safe mechanism. Finally, we discovered a significant enrichment of functional residues (e.g. metal binding, susceptibility to detrimental mutations) in the clusters, which could be the foundation of new prediction tools.
authors: Structural Bioinformatics Group, Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain. daniel.aguilar@upf.edu
authors: Daniel
authors: D
authors: Aguilar
authors: Baldo
authors: B
authors: Oliva
authors: Cristina
authors: C
authors: Marino Buslje
mesh_terms: D020134:Catalytic Domain; D004798:Enzymes / Q000737:chemistry* / Q000235:genetics*; D019143:Evolution, Molecular; D009154:Mutation; D017510:Protein Folding; D020539:Sequence Analysis, Protein / Q000295:instrumentation / Q000379:methods*
chemical_list: D004798:Enzymes