title: Prediction of residues involved in inhibitor specificity in the dihydrofolate reductase family.
abstract: Dihydrofolate reductase (DHFR) is of significant recent interest as a target for drugs against parasitic and opportunistic infections. Understanding factors which influence DHFR homolog inhibitor specificity is critical for the design of compounds that selectively target DHFRs from pathogenic organisms over the human homolog. This paper presents a novel approach for predicting residues involved in ligand discrimination in a protein family using DHFR as a model system. In this approach, the relationship between inhibitor specificity and amino acid composition for sets of protein homolog pairs is examined. Similar inhibitor specificity profiles correlate with increased sequence homology at specific alignment positions. Residue positions that exhibit the strongest correlations are predicted as specificity determinants. Correlation analysis requires a quantitative measure of similarity in inhibitor specificity (S(lig)) for a pair of homologs. To this end, a method of calculating S(lig) values using K(I) values for the two homologs against a set of inhibitors as input was developed. Correlation analysis of S(lig) values to amino acid sequence similarity scores - obtained via multiple sequence alignments - was performed for individual residue alignment positions and sets of residues on 13 DHFRs. Eighteen alignment positions were identified with a strong correlation of S(lig) to sequence similarity. Of these, three lie in the active site; four are located proximal to the active site, four are clustered together in the adenosine binding domain and five on the βFβG loop. The validity of the method is supported by agreement between experimental findings and current predictions involving active site residues.
authors.affiliation: Montclair State University, Department of Chemistry and Biochemistry, Montclair, NJ 07043, USA. goodeyn@montclair.edu
authors.forename: N M
authors.initials: NM
authors.lastname: Goodey
authors.forename: K G
authors.initials: KG
authors.lastname: Herbert
authors.forename: S M
authors.initials: SM
authors.lastname: Hall
authors.forename: K C
authors.initials: KC
authors.lastname: Bagley
mesh_terms: D000818:Animals; D019295:Computational Biology; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000378:metabolism*; D004926:Escherichia coli / Q000737:chemistry / Q000201:enzymology / Q000235:genetics / Q000378:metabolism; D005493:Folic Acid Antagonists / Q000737:chemistry / Q000378:metabolism; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D008958:Models, Molecular; D005810:Multigene Family; D009161:Mycobacterium / Q000737:chemistry / Q000201:enzymology / Q000235:genetics / Q000378:metabolism; D010802:Phylogeny; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D013379:Substrate Specificity; D013762:Tetrahydrofolate Dehydrogenase / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D014345:Trypanosoma / Q000737:chemistry / Q000201:enzymology / Q000235:genetics / Q000378:metabolism
chemical_list: D004791:Enzyme Inhibitors; D005493:Folic Acid Antagonists; D013762:Tetrahydrofolate Dehydrogenase