ConSurf: An algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information

Aharon Armon, Dan Graur, Nir Ben-Tal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Experimental approaches for the identification of functionally important regions on the surface of a protein involve mutagenesis, in which exposed residues are replaced one after another while the change in binding to other proteins or changes in activity are recorded. However, practical considerations limit the use of these methods to small-scale studies, precluding a full mapping of all the functionally important residues on the surface of a protein. We present here an alternative approach involving the use of evolutionary data in the form of multiple-sequence alignment for a protein family to identify hot spots and surface patches that are likely to be in contact with other proteins, domains, peptides, DNA, RNA or ligands. The underlying assumption in this approach is that key residues that are important for binding should be conserved throughout evolution, just like residues that are crucial for maintaining the protein fold, i.e. buried residues. A main limitation in the implementation of this approach is that the sequence space of a protein family may be unevenly sampled, e.g. mammals may be overly represented. Thus, a seemingly conserved position in the alignment may reflect a taxonomically uneven sampling, rather than being indicative of structural or functional importance. To avoid this problem, we present here a novel methodology based on evolutionary relations among proteins as revealed by inferred phylogenetic trees, and demonstrate its capabilities for mapping binding sites in SH2 and PTB signaling domains. A computer program that implements these ideas is available freely at:∼rony

Original languageEnglish
Pages (from-to)447-463
Number of pages17
JournalJournal of Molecular Biology
Issue number1
StatePublished - 16 Mar 2001


  • Molecular recognition
  • Phylogenetic trees
  • Protein modeling
  • Protein-protein interactions


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