Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries

Virtual screening consists of docking libraries of small molecules to a target protein followed by rank-ordering of the resulting structures using scoring functions. The ability of scoring methods to distinguish between actives and inactives depends on several factors that include the accuracy of the binding pose during the docking step and the quality of the three-dimensional structure of the target. Here, we build on our previous work to introduce a new scoring approach (SVMGen) that uses machine learning trained with features from statistical pair potentials obtained from three-dimensional crystal structures. We use SVMGen and GlideScore to explore how enrichment or rank-ordering is affected by binding pose accuracy. To that end, we create a validation set that consists strictly of proteins whose crystal structure was solved in complex with their inhibitors. For the rank-ordering studies, we use crystal structures from PDBbind along with corresponding binding affinity data provided in the database. In addition to binding pose, we investigate the effect of using modeled structures for the target on the enrichment performance of SVMGen and GlideScore. To accomplish this, we generated homology models for protein kinases in DUD-E for which crystal structures are available to enable comparison of enrichment between modeled and crystal structure. We also generate homology models for kinases in SARfari for which there are many known small-molecule inhibitors but no known crystal structure. These models are used to assess the ability of SVMGen and GlideScore to distinguish between actives and decoys. We focus our work on protein kinases considering the wealth of structural and binding affinity data that exists for this family of proteins

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.

A new class of orthosteric uPAR·uPA small-molecule antagonists are allosteric inhibitors of the uPAR·vitronectin interaction

The urokinase receptor (uPAR) is a GPI-anchored cell surface receptor that is at the center of an intricate network of protein-protein interactions. Its immediate binding partners are the serine proteinase urokinase (uPA), and vitronectin (VTN), a component of the extracellular matrix. uPA and VTN bind at distinct sites on uPAR to promote extracellular matrix degradation and integrin signaling, respectively. Here, we report the discovery of a new class of pyrrolone small-molecule inhibitors of the tight ∼1 nM uPAR·uPA protein-protein interaction. These compounds were designed to bind to the uPA pocket on uPAR. The highest affinity compound, namely 7, displaced a fluorescently labeled α-helical peptide (AE147-FAM) with an inhibition constant Ki of 0.7 μM and inhibited the tight uPAR·uPAATF interaction with an IC50 of 18 μM. Biophysical studies with surface plasmon resonance showed that VTN binding is highly dependent on uPA. This cooperative binding was confirmed as 7, which binds at the uPAR·uPA interface, also inhibited the distal VTN·uPAR interaction. In cell culture, 7 blocked the uPAR·uPA interaction in uPAR-expressing human embryonic kidney (HEK-293) cells and impaired cell adhesion to VTN, a process that is mediated by integrins. As a result, 7 inhibited integrin signaling in MDA-MB-231 cancer cells as evidenced by a decrease in focal adhesion kinase (FAK) phosphorylation and Rac1 GTPase activation. Consistent with these results, 7 blocked breast MDA-MB-231 cancer cell invasion with IC50 values similar to those observed in ELISA and surface plasmon resonance competition studies. Explicit-solvent molecular dynamics simulations show that the cooperativity between uPA and VTN is attributed to stabilization of uPAR motion by uPA. In addition, free energy calculations revealed that uPA stabilizes the VTNSMB·uPAR interaction through more favorable electrostatics and entropy. Disruption of the uPAR·VTNSMB interaction by 7 is consistent with the cooperative binding to uPAR by uPA and VTN. Interestingly, the VTNSMB·uPAR interaction was less favorable in the VTNSMB·uPAR·7 complex suggesting potential cooperativity between 7 and VTN. Compound 7 provides an excellent starting point for the development of more potent derivatives to explore uPAR biology

Enrichment of Chemical Libraries Docked to Protein Conformational Ensembles and Application to Aldehyde Dehydrogenase 2

Molecular recognition is a complex process that involves a large ensemble of structures of the receptor and ligand. Yet, most structure-based virtual screening is carried out on a single structure typically from X-ray crystallography. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein. Here we evaluate our recently developed scoring method SVMSP in its ability to enrich chemical libraries docked to MD structures of seven proteins from the Directory of Useful Decoys (DUD). SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We find that enrichment power as measured by the area under the ROC curve (ROC-AUC) is not affected by increasing the number of MD structures. Among individual MD snapshots, many exhibited enrichment that was significantly better than the crystal structure, but no correlation between enrichment and structural deviation from crystal structure was found. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models were applied to two difficult cases (p38 and CDK2) for which enrichment was not better than random. We found remarkable increase in enrichment power, particularly for p38, where the ROC-AUC increased by 0.30 to 0.85. Finally, we explored approaches for a priori identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. We found that the use of randomly selected compounds docked to the target of interest using SVMSP led to notable enrichment for EGFR and Src MD snapshots. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50 000 compounds docked to MD structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors

Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors

Target-specific scoring methods are more commonly used to identify small-molecule inhibitors among compounds docked to a target of interest. Top candidates that emerge from these methods have rarely been tested for activity and specificity across a family of proteins. In this study we docked a chemical library into CaMKIIδ, a member of the Ca2+ /calmodulin (CaM)-dependent protein kinase (CaMK) family, and re-scored the resulting protein-compound structures using Support Vector Machine SPecific (SVMSP), a target-specific method that we developed previously. Among the 35 selected candidates, three hits were identified, such as quinazoline compound 1 (KIN-1; N4-[7-chloro-2-[(E)-styryl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), which was found to inhibit CaMKIIδ kinase activity at single-digit micromolar IC50 . Activity across the kinome was assessed by profiling analogues of 1, namely 6 (KIN-236; N4-[7-chloro-2-[(E)-2-(2-chloro-4,5-dimethoxyphenyl)vinyl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), and an analogue of hit compound 2 (KIN-15; 2-[4-[(E)-[(5-bromobenzofuran-2-carbonyl)hydrazono]methyl]-2-chloro-6-methoxyphenoxy]acetic acid), namely 14 (KIN-332; N-[(E)-[4-(2-anilino-2-oxoethoxy)-3-chlorophenyl]methyleneamino]benzofuran-2-carboxamide), against 337 kinases. Interestingly, for compound 6, CaMKIIδ and homologue CaMKIIγ were among the top ten targets. Among the top 25 targets of 6, IC50 values ranged from 5 to 22 μm. Compound 14 was found to be not specific toward CaMKII kinases, but it does inhibit two kinases with sub-micromolar IC50 values among the top 25. Derivatives of 1 were tested against several kinases including several members of the CaMK family. These data afforded a limited structure-activity relationship study. Molecular dynamics simulations with explicit solvent followed by end-point MM-GBSA free-energy calculations revealed strong engagement of specific residues within the ATP binding pocket, and also changes in the dynamics as a result of binding. This work suggests that target-specific scoring approaches such as SVMSP may hold promise for the identification of small-molecule kinase inhibitors that exhibit some level of specificity toward the target of interest across a large number of proteins

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

Probing binding and cellular activity of pyrrolidinone and piperidinone small molecules targeting the urokinase receptor

The urokinase receptor (uPAR) is a cell-surface protein that is part of an intricate web of transient and tight protein interactions that promote cancer cell invasion and metastasis. Here, we evaluate the binding and biological activity of a new class of pyrrolidinone and piperidinone compounds, along with derivatives of previously-identified pyrazole and propylamine compounds. Competition assays revealed that the compounds displace a fluorescently labeled peptide (AE147-FAM) with inhibition constant (Ki ) values ranging from 6 to 63 μM. Structure-based computational pharmacophore analysis followed by extensive explicit-solvent molecular dynamics (MD) simulations and free energy calculations suggested the pyrazole-based and piperidinone-based compounds adopt different binding modes, despite their similar two-dimensional structures. In cells, pyrazole-based compounds showed significant inhibition of breast adenocarcinoma (MDA-MB-231) and pancreatic ductal adenocarcinoma (PDAC) cell proliferation, but piperidinone-containing compounds exhibited no cytotoxicity even at concentrations of 100 μM. One pyrazole-based compound impaired MDA-MB-231 invasion, adhesion, and migration in a concentration-dependent manner, while the piperidinone inhibited only invasion. The pyrazole derivative inhibited matrix metalloprotease-9 (gelatinase) activity in a concentration-dependent manner, while the piperidinone showed no effect suggesting different mechanisms for inhibition of cell invasion. Signaling studies further highlighted these differences, showing that pyrazole compounds completely inhibited ERK phosphorylation and impaired HIF1α and NF-κB signaling, while pyrrolidinones and piperidinones had no effect. Annexin V staining suggested that the effect of the pyrazole-based compound on proliferation was due to cell killing through an apoptotic mechanism. The compounds identified represent valuable leads in the design of further derivatives with higher affinities and potential probes to unravel the protein-protein interactions of uPAR

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