Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands.

Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.

Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches.

Accurate and rapid estimation of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estimated for one or two non-hydrogen atom changes in compounds targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include standard FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive saturation (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with experimental results. This is supported by additional comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calculating the relative affinities of ligand modifications once those pre-computations are complete. An illustrative example of the potential application of these methods in the context of screening large numbers of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.

Small-molecule inhibitors of ERK-mediated immediate early gene expression and proliferation of melanoma cells expressing mutated BRaf.

Constitutive activation of the extracellular-signal-regulated kinases 1 and 2 (ERK1/2) are central to regulating the proliferation and survival of many cancer cells. The current inhibitors of ERK1/2 target ATP binding or the catalytic site and are therefore limited in their utility for elucidating the complex biological roles of ERK1/2 through its phosphorylation and regulation of over 100 substrate proteins. To overcome this limitation, a combination of computational and experimental methods was used to identify low-molecular-mass inhibitors that are intended to target ERK1/2 substrate-docking domains and selectively interfere with ERK1/2 regulation of substrate proteins. In the present study, we report the identification and characterization of compounds with a thienyl benzenesulfonate scaffold that were designed to inhibit ERK1/2 substrates containing an F-site or DEF (docking site for ERK, FXF) motif. Experimental evidence shows the compounds inhibit the expression of F-site containing immediate early genes (IEGs) of the Fos family, including c-Fos and Fra1, and transcriptional regulation of the activator protein-1 (AP-1) complex. Moreover, this class of compounds selectively induces apoptosis in melanoma cells containing mutated BRaf and constitutively active ERK1/2 signalling, including melanoma cells that are inherently resistant to clinically relevant kinase inhibitors. These findings represent the identification and initial characterization of a novel class of compounds that inhibit ERK1/2 signalling functions and their potential utility for elucidating ERK1/2 and other signalling events that control the growth and survival of cancer cells containing elevated ERK1/2 activity.

Spatial analysis and quantification of the thermodynamic driving forces in protein-ligand binding: binding site variability.

The thermodynamic driving forces behind small molecule-protein binding are still not well-understood, including the variability of those forces associated with different types of ligands in different binding pockets. To better understand these phenomena we calculate spatially resolved thermodynamic contributions of the different molecular degrees of freedom for the binding of propane and methanol to multiple pockets on the proteins Factor Xa and p38 MAP kinase. Binding thermodynamics are computed using a statistical thermodynamics based end-point method applied on a canonical ensemble comprising the protein-ligand complexes and the corresponding free states in an explicit solvent environment. Energetic and entropic contributions of water and ligand degrees of freedom computed from the configurational ensemble provide an unprecedented level of detail into the mechanisms of binding. Direct protein-ligand interaction energies play a significant role in both nonpolar and polar binding, which is comparable to water reorganization energy. Loss of interactions with water upon binding strongly compensates these contributions leading to relatively small binding enthalpies. For both solutes, the entropy of water reorganization is found to favor binding in agreement with the classical view of the "hydrophobic effect". Depending on the specifics of the binding pocket, both energy-entropy compensation and reinforcement mechanisms are observed. It is notable to have the ability to visualize the spatial distribution of the thermodynamic contributions to binding at atomic resolution showing significant differences in the thermodynamic contributions of water to the binding of propane versus methanol.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules.

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

Mapping functional group free energy patterns at protein occluded sites: nuclear receptors and G-protein coupled receptors.

Occluded ligand-binding pockets (LBP) such as those found in nuclear receptors (NR) and G-protein coupled receptors (GPCR) represent a significant opportunity and challenge for computer-aided drug design. To determine free energies maps of functional groups of these LBPs, a Grand-Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) strategy is combined with the Site Identification by Ligand Competitive Saturation (SILCS) methodology. SILCS-GCMC/MD is shown to map functional group affinity patterns that recapitulate locations of functional groups across diverse classes of ligands in the LBPs of the androgen (AR) and peroxisome proliferator-activated-γ (PPARγ) NRs and the metabotropic glutamate (mGluR) and ß2-adreneric (ß2AR) GPCRs. Inclusion of protein flexibility identifies regions of the binding pockets not accessible in crystal conformations and allows for better quantitative estimates of relative ligand binding affinities in all the proteins tested. Differences in functional group requirements of the active and inactive states of the ß2AR LBP were used in virtual screening to identify high efficacy agonists targeting ß2AR in Airway Smooth Muscle (ASM) cells. Seven of the 15 selected ligands were found to effect ASM relaxation representing a 46% hit rate. Hence, the method will be of use for the rational design of ligands in the context of chemical biology and the development of therapeutic agents.

Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling.

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach.

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Rapid estimation of hydration thermodynamics of macromolecular regions.

This work describes a novel protocol to efficiently calculate the local free energy of hydration of specific regions in macromolecules. The method employs Monte Carlo simulations in the grand canonical ensemble to generate water configurations in a selected spherical region in the macromolecule. Excess energy and entropy of hydration are calculated by analyzing the water configurational distributions following the recently published grid inhomogeneous solvation theory method [C. N. Nguyen, T. K. Young, and M. K. Gilson, J. Chem. Phys. 137, 044101 (2012)]. Our method involves the approximations of treating the macromolecule and distant solvent as rigid and performing calculations on multiple such conformations to account for conformational diversity. These approximations are tested against water configurations obtained from a molecular dynamics simulation. The method is validated by predicting the number and location of water molecules in 5 pockets in the protein Interleukin-1ß for which experimental water occupancy data are available. Free energy values are validated against decoupling free energy perturbation calculations. The results indicate that the approximations used in the method enable efficient prediction of free energies of water displacement.

Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges.

Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug candidates interacting with biological systems. In these simulations, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters, and partial atomic charges is required. In the present article, algorithms for the assignment of parameters and charges for the CHARMM General Force Field (CGenFF) are presented. These algorithms rely on the existing parameters and charges that were determined as part of the parametrization of the force field. Bonded parameters are assigned based on the similarity between the atom types that define said parameters, while charges are determined using an extended bond-charge increment scheme. Charge increments were optimized to reproduce the charges on model compounds that were part of the parametrization of the force field. A "penalty score" is returned for every bonded parameter and charge, allowing the user to quickly and conveniently assess the quality of the force field representation of different parts of the compound of interest. Case studies are presented to clarify the functioning of the algorithms and the significance of their output data.

Reproducing crystal binding modes of ligand functional groups using Site-Identification by Ligand Competitive Saturation (SILCS) simulations.

The applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS), to identify regions on a protein surface with which different types of functional groups on low-molecular weight inhibitors interact is demonstrated. The method involves molecular dynamics (MD) simulations of a protein in an aqueous solution of chemically diverse small molecules from which probability distributions of fragments types, termed FragMaps, are obtained. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogen and oxygen atoms serve as probes for hydrogen-bond donor and acceptor affinity, respectively. The method is tested using a set of 7 proteins for which crystal structures of complexes with several high affinity inhibitors are known. Good agreement is obtained between FragMaps and the positions of chemically similar functional groups in inhibitors as observed in the X-ray crystallographic structures. Quantitative capabilities of the SILCS approach are demonstrated by converting FragMaps to free energies, termed Grid Free Energies (GFE), and showing correlation between the GFE values and experimental binding affinities. For proteins for which ligand decoy sets are available, GFE values are shown to typically score the crystal conformation and conformations similar to it more favorable than decoys. Additionally, SILCS is tested for its ability to capture the subtle differences in ligand affinity across homologous proteins, information which may be of utility toward specificity-guided drug design. Taken together, our results show that SILCS can recapitulate the known location of functional groups of bound inhibitors for a number of proteins, suggesting that the method may be of utility for rational drug design.

Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness.

Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW) ≤ 500 or the logarithm of the octanol-water partition coefficient, log P(o/w) < 5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this, we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness based on compounds in the World Drug Index database. In the study four properties, MW, log P(o/w), number of hydrogen bond donors, and number of hydrogen acceptors, were combined into a 4-dimensional (4D) histogram, from which a scoring function was defined on the basis of a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral druglike characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System, and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.

Molecular dynamics simulations of anti-aggregation effect of ibuprofen.

Using implicit solvent molecular dynamics and replica exchange simulations, we study the impact of ibuprofen on the growth of wild-type Abeta fibrils. We show that binding of ibuprofen to Abeta destabilizes the interactions between incoming peptides and the fibril. As a result, ibuprofen interference modifies the free energy landscape of fibril growth and reduces the free energy gain of Abeta peptide binding to the fibril by approximately 2.5 RT at 360 K. Furthermore, ibuprofen interactions shift the thermodynamic equilibrium from fibril-like locked states to disordered docked states. Ibuprofen's anti-aggregation effect is explained by its competition with incoming Abeta peptides for the same binding site located on the fibril edge. Although ibuprofen impedes fibril growth, it does not significantly change the mechanism of fibril elongation or the structure of Abeta peptides bound to the fibril.

Binding of nonsteroidal anti-inflammatory drugs to Abeta fibril.

Nonsteroidal anti-inflammatory drugs are considered as potential therapeutic agents against Alzheimer's disease. Using replica exchange molecular dynamics and atomistic implicit solvent model, we studied the mechanisms of binding of naproxen and ibuprofen to the Abeta fibril derived from solid-state NMR measurements. The binding temperature of naproxen is found to be almost 40 K higher than of ibuprofen implicating higher binding affinity of naproxen. The key factor, which enhances naproxen binding, is strong interactions between ligands bound to the surface of the fibril. The naphthalene ring in naproxen appears to provide a dominant contribution to ligand-ligand interactions. In contrast, ligand-fibril interactions cannot explain differences in the binding affinities of naproxen and ibuprofen. The concave fibril edge with the groove is identified as the primary binding location for both ligands. We show that confinement of the ligands to the groove facilitates ligand-ligand interactions that lowers the energy of the ligands bound to the concave edge compared with those bound to the convex edge. Our simulations appear to provide microscopic rationale for the differing binding affinities of naproxen and ibuprofen observed experimentally.

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free-Energy Calculations Using Lambda Dynamics.

Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small-molecule lead optimization. Relative free-energy perturbation (FEP) approaches are one of the most widely utilized for this goal but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to set up, execute, and analyze multisite lambda dynamics (MSLD) calculations run on GPUs with CHARMM implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse data set of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free-energy landscape of any MSLD system is developed, which enhances sampling and allows for efficient estimation of free-energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than 100 ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150 ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multisite systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore the chemical space around a lead compound and thus are of utility in lead optimization.

Molecular dynamics simulations of Ibuprofen binding to Abeta peptides.

Using replica exchange molecular dynamics simulations and the implicit solvent model we probed binding of ibuprofen to Abeta(10-40) monomers and amyloid fibrils. We found that the concave (CV) fibril edge has significantly higher binding affinity for ibuprofen than the convex edge. Furthermore, binding of ibuprofen to Abeta monomers, as compared to fibrils, results in a smaller free energy gain. The difference in binding free energies is likely to be related to the presence of the groove on the CV fibril edge, in which ibuprofen tends to accumulate. The confinement effect of the groove promotes the formation of large low-energy ibuprofen clusters, which rarely occur on the surface of Abeta monomers. These observations led us to suggest that the ibuprofen binding mechanism for Abeta fibrils is different from that for monomers. In general, ibuprofen shows a preference to bind to those regions of Abeta monomers (amino terminal) and fibrils (the CV edge) that are also the primary aggregation interfaces. Based on our findings and on available experimental data, we propose a rationale for the ibuprofen antiaggregation effect.

Mechanical unbinding of abeta peptides from amyloid fibrils.

Using the experimental structures of Abeta amyloid fibrils and all-atom molecular dynamics, we study the force-induced unbinding of Abeta peptides from the fibril. We show that the mechanical dissociation of Abeta peptides is highly anisotropic and proceeds via different pathways when force is applied in parallel or perpendicular direction with respect to the fibril axis. The threshold forces associated with lateral unbinding of Abeta peptides exceed those observed during the mechanical dissociation along the fibril axis. In addition, Abeta fibrils are found to be brittle in the lateral direction of unbinding and soft along the fibril axis. Lateral mechanical unbinding and the unbinding along the fibril axis load different types of fibril interactions. Lateral unbinding is primarily determined by the cooperative rupture of fibril backbone hydrogen bonds. The unbinding along the fibril axis largely depends on the interpeptide Lys-Asp electrostatic contacts and the hydrophobic interactions formed by the Abeta C terminal. Due to universality of the amyloid beta structure, the anisotropic mechanical dissociation observed for Abeta fibrils is likely to be applicable to other amyloid assemblies. The estimates of equilibrium forces required to dissociate Abeta peptide from the amyloid fibril suggest that these supramolecular structures are mechanically stronger than most protein domains.

Folding of tandem-linked domains.

One of the factors, which influences protein folding in vivo, is a linkage of protein domains into multidomain tandems. However, relatively little is known about the impact of domain connectivity on protein folding mechanisms. In this article, we use coarse grained models of proteins to explore folding of tandem-linked domains (TLD). We found TLD folding to follow two scenarios. In the first, the tandem connectivity produces relatively minor impact on folding and the mechanisms of folding of tandem-linked and single domains remain similar. The second scenario involves qualitative changes in folding mechanism because of tandem linkage. As a result, protein domains, which fold via two-state mechanism as single isolated domains, may form new stable intermediates when inserted into tandems. The new intermediates are created by topological constraints imposed by the linkers between domains. In both cases tandem linkage slows down folding. We propose that the impact of tandem connectivity can be minimized, if the terminal secondary structure elements (SSEs) are flexible. In particular, two factors appear to facilitate TLD folding: (1) the interactions between terminal SSE are poorly ordered in the folding transition state, whereas nonterminal SSE are better structured, (2) the interactions between terminal SSE are weak in the native state. We apply these findings to wild-type proteins by examining experimental phi-value data and by performing all-atom molecular dynamics simulations. We show that immunoglobulin-like domains appear to utilize the factors, which minimize the impact of tandem connectivity on their folding. Several single domain proteins, which are likely to misfold in tandems, are also identified.

Novel protein-inhibitor interactions in site 3 of Ca(2+)-bound S100B as discovered by X-ray crystallography.

Structure-based drug discovery is under way to identify and develop small-molecule S100B inhibitors (SBiXs). Such inhibitors have therapeutic potential for treating malignant melanoma, since high levels of S100B downregulate wild-type p53 tumor suppressor function in this cancer. Computational and X-ray crystallographic studies of two S100B-SBiX complexes are described, and both compounds (apomorphine hydrochloride and ethidium bromide) occupy an area of the S100B hydrophobic cleft which is termed site 3. These data also reveal novel protein-inhibitor interactions which can be used in future drug-design studies to improve SBiX affinity and specificity. Of particular interest, apomorphine hydrochloride showed S100B-dependent killing in melanoma cell assays, although the efficacy exceeds its affinity for S100B and implicates possible off-target contributions. Because there are no structural data available for compounds occupying site 3 alone, these studies contribute towards the structure-based approach to targeting S100B by including interactions with residues in site 3 of S100B.

Small Molecule Inhibitors of Ca(2+)-S100B Reveal Two Protein Conformations.

The drug pentamidine inhibits calcium-dependent complex formation with p53 ((Ca)S100B·p53) in malignant melanoma (MM) and restores p53 tumor suppressor activity in vivo. However, off-target effects associated with this drug were problematic in MM patients. Structure-activity relationship (SAR) studies were therefore completed here with 23 pentamidine analogues, and X-ray structures of (Ca)S100B·inhibitor complexes revealed that the C-terminus of S100B adopts two different conformations, with location of Phe87 and Phe88 being the distinguishing feature and termed the "FF-gate". For symmetric pentamidine analogues ((Ca)S100B·5a, (Ca)S100B·6b) a channel between sites 1 and 2 on S100B was occluded by residue Phe88, but for an asymmetric pentamidine analogue ((Ca)S100B·17), this same channel was open. The (Ca)S100B·17 structure illustrates, for the first time, a pentamidine analog capable of binding the "open" form of the "FF-gate" and provides a means to block all three "hot spots" on (Ca)S100B, which will impact next generation (Ca)S100B·p53 inhibitor design.