GASS: identifying enzyme active sites with genetic algorithms.
BACKGROUND
Currently, 25% of proteins annotated in Pfam have their function unknown. One way of predicting proteins function is by looking at their active site, which has two main parts: the catalytic site and the substrate binding site. The active site is more conserved than the other residues of the protein and can be a rich source of information for protein function prediction. This article presents a new heuristic method, named genetic active site search (GASS), which searches for given active site 3D templates in unknown proteins. The method can perform non-exact amino acid matches (conservative mutations), is able to find amino acids in different chains and does not impose any restrictions on the active site size.


RESULTS
GASS results were compared with those catalogued in the catalytic site atlas (CSA) in four different datasets and compared with two other methods: amino acid pattern search for substructures and motif and catalytic site identification. The results show GASS can correctly identify >90% of the templates searched. Experiments were also run using data from the substrate binding sites prediction competition CASP 10, and GASS is ranked fourth among the 18 methods considered.
Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil.
Sandro C
SC
Izidoro
Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil.
Raquel C
RC
de Melo-Minardi
Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil.
Gisele L
GL
Pappa
D000465:Algorithms; D001665:Binding Sites; D020134:Catalytic Domain; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins