Laurie E. Grove

Structural conservation of druggable hot spots in protein–protein interfaces

Despite the growing number of examples of small-molecule inhibitors that disrupt protein–protein interactions (PPIs), the origin of druggability of such targets is poorly understood. To identify druggable sites in protein–protein interfaces we combine computational solvent mapping, which explores the protein surface using a variety of small “probe” molecules, with a conformer generator to account for side-chain flexibility. Applications to unliganded structures of 15 PPI target proteins show that the druggable sites comprise a cluster of binding hot spots, distinguishable from other regions of the protein due to their concave topology combined with a pattern of hydrophobic and polar functionality. This combination of properties confers on the hot spots a tendency to bind organic species possessing some polar groups decorating largely hydrophobic scaffolds. Thus, druggable sites at PPI are not simply sites that are complementary to particular organic functionality, but rather possess a general tendency to bind organic compounds with a variety of structures, including key side chains of the partner protein. Results also highlight the importance of conformational adaptivity at the binding site to allow the hot spots to expand to accommodate a ligand of drug-like dimensions. The critical components of this adaptivity are largely local, involving primarily low energy side-chain motions within 6 Å of a hot spot. The structural and physicochemical signature of druggable sites at PPI interfaces is sufficiently robust to be detectable from the structure of the unliganded protein, even when substantial conformational adaptation is required for optimal ligand binding.

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins.

FTMap is a computational mapping server that identifies binding hot spots of macromolecules—i.e., regions of the surface with major contributions to the ligand-binding free energy. To use FTMap, users submit a protein, DNA or RNA structure in PDB (Protein Data Bank) format. FTMap samples billions of positions of small organic molecules used as probes, and it scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots in good agreement with experimental data. FTMap serves as the basis for other servers, namely FTSite, which is used to predict ligand-binding sites, FTFlex, which is used to account for side chain flexibility, FTMap/param, used to parameterize additional probes and FTDyn, for mapping ensembles of protein structures. Applications include determining the druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures and providing input for fragment-based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and it is much faster than the more-recent approaches to protein mapping based on mixed molecular dynamics. By using 16 probe molecules, the FTMap server finds the hot spots of an average-size protein in <1 h. As FTFlex performs mapping for all low-energy conformers of side chains in the binding site, its completion time is proportionately longer.

FTSite: high accuracy detection of ligand binding sites on unbound protein structures

MOTIVATION Binding site identification is a classical problem that is important for a range of applications, including the structure-based prediction of function, the elucidation of functional relationships among proteins, protein engineering and drug design. We describe an accurate method of binding site identification, namely FTSite. This method is based on experimental evidence that ligand binding sites also bind small organic molecules of various shapes and polarity. The FTSite algorithm does not rely on any evolutionary or statistical information, but achieves near experimental accuracy: it is capable of identifying the binding sites in over 94% of apo proteins from established test sets that have been used to evaluate many other binding site prediction methods. AVAILABILITY FTSite is freely available as a web-based server at http://ftsite.bu.edu.

Spectroscopic and Computational Investigation of Second-Sphere Contributions to Redox Tuning in Escherichia coli Iron Superoxide Dismutase

In Fe- and Mn-dependent superoxide dismutases (SODs), second-sphere residues have been implicated in precisely tuning the metal ion reduction potential to maximize catalytic activity (Vance, C. K.; Miller, A.-F. J. Am. Chem. Soc. 1998, 120, 461-467). In the present study, spectroscopic and computational methods were used to characterize three distinct Fe-bound SOD species that possess different second-coordination spheres and, consequently, Fe(3+/2+)reduction potentials that vary by approximately 1 V, namely, FeSOD, Fe-substituted MnSOD (Fe(Mn)SOD), and the Q69E FeSOD mutant. Despite having markedly different metal ion reduction potentials, FeSOD, Fe(Mn)SOD, and Q69E FeSOD exhibit virtually identical electronic absorption, circular dichroism, and magnetic circular dichroism (MCD) spectra in both their oxidized and reduced states. Likewise, variable-temperature, variable-field MCD data obtained for the oxidized and reduced species do not reveal any significant electronic, and thus geometric, variations within the Fe ligand environment. To gain insight into the mechanism of metal ion redox tuning, complete enzyme models for the oxidized and reduced states of all three Fe-bound SOD species were generated using combined quantum mechanics/molecular mechanics (QM/MM) geometry optimizations. Consistent with our spectroscopic data, density functional theory computations performed on the corresponding active-site models predict that the three SOD species share similar active-site electronic structures in both their oxidized and reduced states. By using the QM/MM-optimized active-site models in conjunction with the conductor-like screening model to calculate the proton-coupled Fe(3+/2+) reduction potentials, we found that different hydrogen-bonding interactions with the conserved second-sphere Gln (changed to Glu in Q69E FeSOD) greatly perturb the p K of the Fe-bound solvent ligand and, thus, drastically affect the proton-coupled metal ion reduction potential.

FTFlex: accounting for binding site flexibility to improve fragment-based identification of druggable hot spots

UNLABELLED Computational solvent mapping finds binding hot spots, determines their druggability and provides information for drug design. While mapping of a ligand-bound structure yields more accurate results, usually the apo structure serves as the starting point in design. The FTFlex algorithm, implemented as a server, can modify an apo structure to yield mapping results that are similar to those of the respective bound structure. Thus, FTFlex is an extension of our FTMap server, which only considers rigid structures. FTFlex identifies flexible residues within the binding site and determines alternative conformations using a rotamer library. In cases where the mapping results of the apo structure were in poor agreement with those of the bound structure, FTFlex was able to yield a modified apo structure, which lead to improved FTMap results. In cases where the mapping results of the apo and bound structures were in good agreement, no new structure was predicted. AVAILABILITY FTFlex is freely available as a web-based server at http://ftflex.bu.edu/.

Robust identification of binding hot spots using continuum electrostatics: application to hen egg-white lysozyme.

Binding hot spots, protein regions with high binding affinity, can be identified by using X-ray crystallography or NMR spectroscopy to screen libraries of small organic molecules that tend to cluster at such hot spots. FTMap, a direct computational analogue of the experimental screening approaches, uses 16 different probe molecules for global sampling of the surface of a target protein on a dense grid and evaluates the energy of interaction using an empirical energy function that includes a continuum electrostatic term. Energy evaluation is based on the fast Fourier transform correlation approach, which allows for the sampling of billions of probe positions. The grid sampling is followed by off-grid minimization that uses a more detailed energy expression with a continuum electrostatics term. FTMap identifies the hot spots as consensus clusters formed by overlapping clusters of several probes. The hot spots are ranked on the basis of the number of probe clusters, which predicts their binding propensity. We applied FTMap to nine structures of hen egg-white lysozyme (HEWL), whose hot spots have been extensively studied by both experimental and computational methods. FTMap found the primary hot spot in site C of all nine structures, in spite of conformational differences. In addition, secondary hot spots in sites B and D that are known to be important for the binding of polysaccharide substrates were found. The predicted probe–protein interactions agree well with those seen in the complexes of HEWL with various ligands and also agree with an NMR-based study of HEWL in aqueous solutions of eight organic solvents. We argue that FTMap provides more complete information on the HEWL binding site than previous computational methods and yields fewer false-positive binding locations than the X-ray structures of HEWL from crystals soaked in organic solvents.

Spectroscopic and computational insights into second-sphere amino-acid tuning of substrate analogue/active-site interactions in iron(III) superoxide dismutase.

In this study, the mechanism by which second-sphere residues modulate the structural and electronic properties of substrate-analogue complexes of the Fe-dependent superoxide dismutase (FeSOD) has been explored. Both spectroscopic and computational methods were used to investigate the azide (N3(-)) adducts of Fe(3+)SOD (N3-Fe(3+)SOD) and its Q69E mutant, as well as Fe(3+)-substituted MnSOD (N3-Fe(3+)(Mn)SOD) and its Y34F mutant. Electronic absorption, circular dichroism, and magnetic circular dichroism spectroscopic data reveal that the energy of the dominant N3(-)-->Fe(3+) ligand-to-metal charge transfer (LMCT) transition decreases in the order N3-Fe(3+)(Mn)SOD>N3-Fe(3+)SOD>Q69E N3-Fe(3+)SOD. Intriguingly, the LMCT transition energies correlate almost linearly with the Fe(3+/2+) reduction potentials of the corresponding Fe(3+)-bound SOD species in the absence of azide, which span a range of approximately 1 V (see the preceding paper). To explore the origin of this correlation, combined quantum mechanics/molecular mechanics (QM/MM) geometry optimizations were performed on complete enzyme models. The INDO/S-CI computed electronic transition energies satisfactorily reproduce the experimental trend in LMCT transition energies, indicating that the QM/MM optimized active-site models are reasonable. Density functional theory calculations on these experimentally validated active-site models reveal that the differences in spectral and electronic properties among the four N 3(-) adducts arise primarily from differences in the hydrogen-bond network involving the conserved second-sphere Gln (mutated to Glu in Q69E FeSOD) and the solvent ligand. The implications of our findings with respect to the mechanism by which the second-coordination sphere modulates substrate-analogue binding as well as the catalytic properties of FeSOD are discussed.

Synthesis, X-ray crystallographic characterization, and electronic structure studies of a di-azide iron(III) complex: implications for the azide adducts of iron(III) superoxide dismutase.

We have synthesized and characterized, using X-ray crystallographic, spectroscopic, and computational techniques, a six-coordinate diazide Fe (3+) complex, LFe(N 3) 2 (where L is the tetradentate ligand 7-diisopropyl-1,4,7-triazacyclononane-1-acetic acid), that serves as a model of the azide adducts of Fe (3+) superoxide dismutase (Fe (3+)SOD). While previous spectroscopic studies revealed that two distinct azide-bound Fe (3+)SOD species can be obtained at cryogenic temperatures depending on protein and azide concentrations, the number of azide ligands coordinated to the Fe (3+) ion in each species has been the subject of some controversy. In the case of LFe(N 3) 2, the electronic absorption and magnetic circular dichroism spectra are dominated by two broad features centered at 21 500 cm (-1) (approximately 4000 M (-1) cm (-1)) and approximately 30 300 cm (-1) (approximately 7400 M (-1) cm (-1)) attributed to N3 (-) --> Fe (3+) charge transfer (CT) transitions. A normal coordinate analysis of resonance Raman (RR) data obtained for LFe(N 3) 2 indicates that the vibrational features at 363 and 403 cm (-1) correspond to the Fe-N 3 stretching modes (nu Fe-N3) associated with the two different azide ligands and yields Fe-N 3 force constants of 1.170 and 1.275 mdyne/A, respectively. RR excitation profile data obtained with laser excitation between 16,000 and 22,000 cm (-1) reveal that the nu Fe-N3 modes at 363 and 403 cm (-1) are preferentially enhanced upon excitation in resonance with the N 3 (-) --> Fe (3+) CT transitions at lower and higher energies, respectively. Consistent with this result, density functional theory electronic structure calculations predict a larger stabilization of the molecular orbitals of the more strongly bound azide due to increased sigma-symmetry orbital overlap with the Fe 3d orbitals, thus yielding higher N 3 (-) --> Fe (3+) CT transition energies. Comparison of our data obtained for LFe(N 3) 2 with those reported previously for the two azide adducts of Fe (3+)SOD provides compelling evidence that a single azide is coordinated to the Fe (3+) center in each protein species.

Focused grid-based resampling for protein docking and mapping.

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein‐protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near‐native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands.