Resumo:
Metals are present in more than 30% of proteins found in nature and perform important
biological functions, in addition they act in the maintenance of protein structure. Metal
ions in proteins are bounded to groups of atoms and this set is called a metal-binding
site. Metal-binding sites can perform catalytic, structural, transport and electron transfer
functions in a protein.
Traditional and experimental techniques for metal-binding site prediction usually find
obstacles related to time and cost of execution, making computational tools that can
assist in predictions become even more important. Several methods in the literature have
made efforts to predict metal-binding sites and have shown great results, but they still
encounter barriers due to issues related to protein size, type of ions and ligands, ability
to find inter-domain residues and even when obtaining not good accuracy rates.
The main goal of this master thesis is to adapt GASS algorithm (Genetic Active
Site Search), initially proposed for the prediction of catalytic sites, to search for metalbinding
sites. The method developed, GASS-Metal, divides residues of a protein in threedimensional
space and uses parallelism of genetic algorithms to find candidate sites that
are close in relation to the distance of cured templates from M-CSA and MetalPDB.
The results of the sanity and homologous protein tests showed that GASS-Metal is a
robust method, capable of finding metal-binding sites in different types of ions and does
not restrict its search to a single chain. In addition, when using conservative mutations,
the prediction accuracy rate improves even more, helping to find sites in situations where
it was previously impossible, due to the lack of residues in certain proteins.
In comparison to state-of-the-art predictors, GASS-Metal achieved satisfactory performance
in predicting metal-binding sites of different ions. The results showed that the
method was superior in the prediction in 5 of the 12 metal ions evaluated and still obtained
equivalent performance in other 6 different metal-binding sites.
