Molecular Recognition in a Diverse Set of Protein-Ligand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations

End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal•mol−1 highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10). But larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the non-polar and entropy components, rather than a better representation of the electrostatics. SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by pre-scoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the non-polar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy

Small-Molecule Binding Sites to Explore New Targets in the Cancer Proteome

The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeutic agents (druggable). These binding sites were classified as enzyme active sites (ENZ), protein-protein interaction sites (PPI), or other sites whose function is unknown (OTH). Interestingly, the overwhelming majority of binding sites were classified as OTH. We find that ENZ, PPI, and OTH binding sites often occurred on the same structure suggesting that many of these OTH cavities can be used for allosteric modulation of enzyme activity or protein-protein interactions with small molecules. We discovered several ENZ (PYCR1, QPRT, and HSPA6) and PPI (CASC5, ZBTB32, and CSAD) binding sites on proteins that have been seldom explored in cancer. We also found proteins that have been extensively studied in cancer that have not been previously explored with small molecules that harbor ENZ (PKMYT1, STEAP3, and NNMT) and PPI (HNF4A, MEF2B, and CBX2) binding sites. All binding sites were classified by the signaling pathways to which the protein that harbors them belongs using KEGG. In addition, binding sites were mapped onto structural protein-protein interaction networks to identify promising sites for drug discovery. Finally, we identify pockets that harbor missense mutations previously identified from analysis of the TCGA data. The occurrence of mutations in these binding sites provides new opportunities to develop small-molecule probes to explore their function in cancer.

Small molecules inhibit STAT3 activation, autophagy, and cancer cell anchorage-independent growth

Triple-negative breast cancers (TNBCs) lack the signature targets of other breast tumors, such as HER2, estrogen receptor, and progesterone receptor. These aggressive basal-like tumors are driven by a complex array of signaling pathways that are activated by multiple driver mutations. Here we report the discovery of 6 (KIN-281), a small molecule that inhibits multiple kinases including maternal leucine zipper kinase (MELK) and the non-receptor tyrosine kinase bone marrow X-linked (BMX) with single-digit micromolar IC50s. Several derivatives of 6 were synthesized to gain insight into the binding mode of the compound to the ATP binding pocket. Compound 6 was tested for its effect on anchorage-dependent and independent growth of MDA-MB-231 and MDA-MB-468 breast cancer cells. The effect of 6 on BMX prompted us to evaluate its effect on STAT3 phosphorylation and DNA binding. The compound’s inhibition of cell growth led to measurements of survivin, Bcl-XL, p21WAF1/CIP1, and cyclin A2 levels. Finally, LC3B-II levels were quantified following treatment of cells with 6 to determine whether the compound affected autophagy, a process that is known to be activated by STAT3. Compound 6 provides a starting point for the development of small molecules with polypharmacology that can suppress TNBC growth and metastasis

Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR·uPA, TEAD4·Yap1, and CaVα·CaVβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR·uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4·Yap1. Virtual screening of conformationally restricted compounds targeting uPAR·uPA and TEAD4·Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions

Small Molecules Engage Hot Spots through Cooperative Binding To Inhibit a Tight Protein–Protein Interaction

Protein–protein interactions drive every aspect of cell signaling, yet only a few small-molecule inhibitors of these interactions exist. Despite our ability to identify critical residues known as hot spots, little is known about how to effectively engage them to disrupt protein–protein interactions. Here, we take advantage of the ease of preparation and stability of pyrrolinone 1, a small-molecule inhibitor of the tight interaction between the urokinase receptor (uPAR) and its binding partner, the urokinase-type plasminogen activator uPA, to synthesize more than 40 derivatives and explore their effect on the protein–protein interaction. We report the crystal structure of uPAR bound to previously discovered pyrazole 3 and to pyrrolinone 12. While both 3 and 12 bind to uPAR and compete with a fluorescently labeled peptide probe, only 12 and its derivatives inhibit the full uPAR·uPA interaction. Compounds 3 and 12 mimic and engage different hot-spot residues on uPA and uPAR, respectively. Interestingly, 12 is involved in a π–cation interaction with Arg-53, which is not considered a hot spot. Explicit-solvent molecular dynamics simulations reveal that 3 and 12 exhibit dramatically different correlations of motion with residues on uPAR. Free energy calculations for the wild-type and mutant uPAR bound to uPA or 12 show that Arg-53 interacts with uPA or with 12 in a highly cooperative manner, thereby altering the contributions of hot spots to uPAR binding. The direct engagement of peripheral residues not considered hot spots through π–cation or salt-bridge interactions could provide new opportunities for enhanced small-molecule engagement of hot spots to disrupt challenging protein–protein interactions

Small-molecule CaVα1⋅CaVβ antagonist suppresses neuronal voltage-gated calcium-channel trafficking

Extracellular calcium flow through neuronal voltage-gated CaV2.2 calcium channels converts action potential-encoded information to the release of pronociceptive neurotransmitters in the dorsal horn of the spinal cord, culminating in excitation of the postsynaptic central nociceptive neurons. The CaV2.2 channel is composed of a pore-forming α1 subunit (CaVα1) that is engaged in protein-protein interactions with auxiliary α2/δ and β subunits. The high-affinity CaV2.2α1⋅CaVβ3 protein-protein interaction is essential for proper trafficking of CaV2.2 channels to the plasma membrane. Here, structure-based computational screening led to small molecules that disrupt the CaV2.2α1⋅CaVβ3 protein-protein interaction. The binding mode of these compounds reveals that three substituents closely mimic the side chains of hot-spot residues located on the α-helix of CaV2.2α1 Site-directed mutagenesis confirmed the critical nature of a salt-bridge interaction between the compounds and CaVβ3 Arg-307. In cells, compounds decreased trafficking of CaV2.2 channels to the plasma membrane and modulated the functions of the channel. In a rodent neuropathic pain model, the compounds suppressed pain responses. Small-molecule α-helical mimetics targeting ion channel protein-protein interactions may represent a strategy for developing nonopioid analgesia and for treatment of other neurological disorders associated with calcium-channel trafficking

BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome

BioDrugScreen is a resource for ranking molecules docked against a large number of targets in the human proteome. Nearly 1600 molecules from the freely available NCI diversity set were docked onto 1926 cavities identified on 1589 human targets resulting in >3 million receptor–ligand complexes requiring >200 000 cpu-hours on the TeraGrid. The targets in BioDrugScreen originated from Human Cancer Protein Interaction Network, which we have updated, as well as the Human Druggable Proteome, which we have created for the purpose of this effort. This makes the BioDrugScreen resource highly valuable in drug discovery. The receptor–ligand complexes within the database can be ranked using standard and well-established scoring functions like AutoDock, DockScore, ChemScore, X-Score, GoldScore, DFIRE and PMF. In addition, we have scored the complexes with more intensive GBSA and PBSA approaches requiring an additional 120 000 cpu-hours on the TeraGrid. We constructed a simple interface to enable users to view top-ranking molecules and access purchasing and other information for further experimental exploration

Small molecules inhibit ex vivo tumor growth in bone

Bone is a common site of metastasis for breast, prostate, lung, kidney and other cancers. Bone metastases are incurable, and substantially reduce patient quality of life. To date, there exists no small-molecule therapeutic agent that can reduce tumor burden in bone. This is partly attributed to the lack of suitable in vitro assays that are good models of tumor growth in bone. Here, we take advantage of a novel ex vivo model of bone colonization to report a series of pyrrolopyrazolone small molecules that inhibit cancer cell invasion and ex vivo tumor growth in bone at single-digit micromolar concentration. We find that the compounds modulated the expression levels of genes associated with bone-forming osteoblasts, bone-destroying osteoclasts, cancer cell viability and metastasis. Our compounds provide chemical tools to uncover novel targets and pathways associated with bone metastasis, as well as for the development of compounds to prevent and reverse bone tumor growth in vivo

Discovery and characterization of small molecules that target the Ral GTPase

The Ras-like GTPases RalA and B are important drivers of tumor growth and metastasis. Chemicals that block Ral function would be valuable as research tools and for cancer therapeutics. Here, we used protein structure analysis and virtual screening to identify drug-like molecules that bind a site on the GDP-form of Ral. Compounds RBC6, RBC8 and RBC10 inhibited Ral binding to its effector RalBP1, Ral-mediated cell spreading in murine fibroblasts and anchorage-independent growth of human cancer cell lines. Binding of RBC8 derivative BQU57 to RalB was confirmed by isothermal titration calorimetry, surface plasma resonance and 15N-HSQC NMR. RBC8 and BQU57 show selectivity for Ral relative to Ras or Rho and inhibit xenograft tumor growth similar to depletion of Ral by siRNA. Our results show the utility of structure-based discovery for development of therapeutics for Ral-dependent cancers