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Human thymidylate synthase (hTS), a target for antiproliferative drugs, is an obligate homodimer. Single-point mutations to alanine at the monomer-monomer interface may enable the identification of specific residues that delineate sites for drugs aimed at perturbing the protein-protein interactions critical for activity. We computationally identified putative hotspot residues at the interface and designed mutants to perturb the intersubunit interaction. Dimer dissociation constants measured by a FRET-based assay range from 60 nM for wild-type hTS up to about 1 mM for single-point mutants and agree with computational predictions of the effects of these mutations. Mutations that are remote from the active site retain full or partial activity, although the substrate KM values were generally higher and the dimer was less stable. The lower dimer stability of the mutants can facilitate access to the dimer interface by small molecules and thereby aid the design of inhibitors that bind at the dimer interface.

Human thymidylate synthase is a homodimeric enzyme that plays a key role in DNA synthesis and is a target for several clinically important anticancer drugs that bind to its active site. We have designed peptides to specifically target its dimer interface. Here we show through X-ray diffraction, spectroscopic, kinetic, and calorimetric evidence that the peptides do indeed bind at the interface of the dimeric protein and stabilize its di-inactive form. The "LR" peptide binds at a previously unknown binding site and shows a previously undescribed mechanism for the allosteric inhibition of a homodimeric enzyme. It inhibits the intracellular enzyme in ovarian cancer cells and reduces cellular growth at low micromolar concentrations in both cisplatin-sensitive and -resistant cells without causing protein overexpression. This peptide demonstrates the potential of allosteric inhibition of hTS for overcoming platinum drug resistance in ovarian cancer.

Many enzymes and proteins are regulated by their quaternary structure and/or by their association in homo- and/or hetero-oligomer complexes. Thus, these protein-protein interactions can be good targets for blocking or modulating protein function therapeutically. The large number of oligomeric structures in the Protein Data Bank (http://www.rcsb.org/) reflects growing interest in proteins that function as multimeric complexes. In this review, we consider the particular case of homodimeric enzymes as drug targets. There is intense interest in drugs that inhibit dimerization of a functionally obligate homodimeric enzyme. Because amino acid conservation within enzyme interfaces is often low compared to conservation in active sites, it may be easier to achieve drugs that target protein interfaces selectively and specifically. Two main types of dimerization inhibitors have been developed: peptides or peptidomimetics based on sequences involved in protein-protein interactions, and small molecules that act at hot spots in protein-protein interfaces. Examples include inhibitors of HIV protease and HIV integrase. Studying the mechanisms of action and locating the binding sites of such inhibitors requires different techniques for different proteins. For some enzymes, ligand binding is only detectable in vivo or after unfolding of the complexes. Here, we review the structural features of dimeric enzymes and give examples of inhibition through interference in dimer stability. Several techniques for studying these complex phenomena will be presented.

Structure-based drug design of species-specific inhibitors generally exploits structural differences in proteins from different organisms. Here, we demonstrate how achieving specificity can be aided by targeting differences in the dynamics of proteins. Thymidylate synthase (TS) is a good target for anticancer agents and a potential target for antibacterial agents. Most inhibitors are folate-analogs that bind at the folate binding site and are not species specific. In contrast, alpha156 is not a folate-analog and is specific for bacterial TS; it has been shown crystallographically to bind in a nonconserved binding site. Docking calculations and crystal structure-based estimation of the essential dynamics of TSs from five different species show that differences in the dynamics of TSs make the active site more accessible to alpha156 in the prokaryotic than in the eukaryotic TSs and thereby enhance the specificity of alpha156.

Neuraminidase is a surface glycoprotein of influenza viruses that cleaves terminal sialic acids from carbohydrates. It is critical for viral release from infected cells and facilitates viral spread in the respiratory tract. The catalytic active site of neuraminidase is highly conserved in all type A and B influenza viruses, making it an excellent target for antiinfluenza drug design. Indeed, neuraminidase inhibitors have recently become available in the clinic for the treatment of influenza. Here, we describe the use of 3D structures of neuraminidase-inhibitor complexes to derive quantitative structure-activity relationships (QSARs) to aid understanding of the mechanism of inhibition and the discovery of new inhibitors. Crystal structures of neuraminidase-inhibitor complexes were used alongside modeled complexes to derive QSAR models by COMparative BINding Energy (COMBINE) analysis (Ortiz, A. R.; Pisabarro, M. T.; Gago, F.; Wade, R. C. J. Med. Chem. 1995, 38, 2681-2691). The neuraminidase proteins studied include type A subtypes N2 and N9 (which have ca. 50% sequence identity) and an active site mutant of the N9 subtype. The inhibitors include sialic acid and benzoic acid analogues with diverse frameworks and substitution groups. By considering the contributions of the protein residues and a key water molecule to the electrostatic and van der Waals intermolecular interaction energies, a predictive and robust QSAR model for binding to type A neuraminidase was obtained. In this QSAR model, 12 protein residues and 1 bound water molecule are highlighted as particularly important for inhibitory activity. This QSAR model provides guidelines for structural modification of current inhibitors and the design of novel inhibitors in order to optimize inhibitory activity.

The effects of data pretreatment, data scaling, and variable selection on three-dimensional quantitative structure-activity relationships derived by comparative molecular field analysis (CoMFA) using the GRID energy function were studied in detail for a set of inhibitors of the human synovial fluid phospholipase A2 (HSF-PLA2). The quality of the models was evaluated for predictive power and ability to map the receptor binding site by (a) comparison of predicted and experimental activities using cross-validation and external validation sets and (b) comparison of the regions selected in space in the CoMFA models with a crystal structure of a HSF-PLA2-inhibitor complex, with optimized comparative binding energy analysis (COMBINE) models (Ortiz et al., 1995) and with structure-activity relationships derived previously for different sets of compounds. It is found that (1) data scaling and dielectric modeling strongly influence CoMFA results. Unscaled data and a uniform dielectric constant of 4 are well suited to GRID-CoMFA studies for the present compound set. (2) The GOLPE and Q2-GRS variable selection methods select variables in roughly the same regions in Cartesian space, but they produce different models in chemometric space and differ in their sensitivity to data scaling and pretreatment and their tendency to overfitting. (3) CoMFA models are consistent with COMBINE models in that they identify approximately the same intermolecular interactions as relevant for activity. Our study provides support for the qualitative receptor-mapping properties of CoMFA models and for the validity of variable selection when applied with care and also provides guidelines for how to evaluate the quality of CoMFA models.