Structure-based identification of small molecule binding sites using a free energy model

We separately have shown that the maximal druglike affinity of a given binding site on a protein can be calculated on the basis of the binding-site structure alone by using a desolvation-based free energy model along with the notion that druglike ligands fall into certain physiochemical property ranges. Here, we present an approach where we reformulate the calculated druggability affinity as an additive free energy to facilitate the searching of whole protein surfaces for druglike binding sites. The highest-scoring patches in many cases represent known ligand-binding sites for druggable targets, but not for difficult targets. This approach differs from other approaches in that it does not simply identify pockets with the greatest volume but instead identifies pockets that are likely to be amenable to druglike small-molecule binding. Combining the method with a functional residue prediction method called SCA (statistical coupling analysis) results in the prediction of potentially druggable allosteric binding sites on p38alpha kinase.     

DrugPred: a structure-based approach to predict protein druggability developed using an extensive nonredundant data set

Judging if a protein is able to bind orally available molecules with high affinity, i.e. if a protein is druggable, is an important step in target assessment. In order to derive a structure-based method to predict protein druggability, a comprehensive, nonredundant data set containing crystal structures of 71 druggable and 44 less druggable proteins was compiled by literature search and data mining. This data set was subsequently used to train a structure-based druggability predictor (DrugPred) using partial least-squares projection to latent structures discriminant analysis (PLS-DA). DrugPred performed well in discriminating druggable from less druggable binding sites for both internal and external predictions. The method is robust against conformational changes in the binding site and outperforms previously published methods. The superior performance of DrugPred is likely due to the size and composition of the training set which, in contrast to most previously developed methods, only contains cavities that have evolved to bind a natural ligand.     

Molecular Dynamics Approach to Probe the Allosteric Inhibition of PTP1B by Chlorogenic and Cichoric Acid

Protein tyrosine phosphatase 1B (PTP1B), a major negative regulator of the insulin and leptin signaling pathway, is a potential target for therapeutic intervention against diabetes and obesity. The recent discovery of an allosteric site in PTP1B has created an alternate strategy in the development of PTP1B targeted therapy. The current study investigates the molecular interactions between the allosteric site of PTP1B with two caffeoyl derivatives, chlorogenic acid (CGA) and cichoric acid (CHA), using computational strategies. Molecular docking analysis with CGA and CHA at the allosteric site of PTP1B were performed and the resulting protein-ligand complexes used for molecular dynamics simulation studies for a time scale of 10 ns. Results show stable binding of CGA and CHA at the allosteric site of PTP1B. The flexibility of the WPD loop was observed to be constrained by CGA and CHA in the open (inactive), providing molecular mechanism of allosteric inhibition. The allosteric inhibition of CGA and CHA of PTP1B was shown to be favorable due to no restriction by the α-7 helix in the binding of CGA and CHA at the allosteric binding site. In conclusion, our results exhibit an inhibitory pattern of CGA and CHA against PTP1B through potent binding at the allosteric site.     

Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in receptor based pharmacophore modeling

Allosteric inhibition of protein tyrosine phosphatase 1B (PTP1B), has paved a new path to design specific inhibitors for PTP1B, which is an important drug target for the treatment of type II diabetes and obesity. The PTP1B_1–282-allosteric inhibitor complex crystal structure lacks α7 (287–298) and moreover there is no available 3D structure of PTP1B_1–298 in open form. As the interaction between α7 and α6–α3 helices plays a crucial role in allosteric inhibition, α7 was modeled to the PTP1B_1–282 in open form complexed with an allosteric inhibitor (compound-2) and a 5 ns MD simulation was performed to investigate the relative orientation of the α7–α6–α3 helices. The simulation conformational space was statistically sampled by clustering analyses. This approach was helpful to reveal certain clues on PTP1B allosteric inhibition. The simulation was also utilized in the generation of receptor based pharmacophore models to include the conformational flexibility of the protein-inhibitor complex. Three cluster representative structures of the highly populated clusters were selected for pharmacophore model generation. The three pharmacophore models were subsequently utilized for screening databases to retrieve molecules containing the features that complement the allosteric site. The retrieved hits were filtered based on certain drug-like properties and molecular docking simulations were performed in two different conformations of protein. Thus, performing MD simulation with α7 to investigate the changes at the allosteric site, then developing receptor based pharmacophore models and finally docking the retrieved hits into two distinct conformations will be a reliable methodology in identifying PTP1B allosteric inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

How does a drug molecule find its target binding site?

Although the thermodynamic principles that control the binding of drug molecules to their protein targets are well understood, detailed experimental characterization of the process by which such binding occurs has proven challenging. We conducted relatively long, unguided molecular dynamics simulations in which a ligand (the cancer drug dasatinib or the kinase inhibitor PP1) was initially placed at a random location within a box that also contained a protein (Src kinase) to which that ligand was known to bind. In several of these simulations, the ligand correctly identified its target binding site, forming a complex virtually identical to the crystallographically determined bound structure. The simulated trajectories provide a continuous, atomic-level view of the entire binding process, revealing persistent and noteworthy intermediate conformations and shedding light on the role of water molecules. The technique we employed, which does not assume any prior knowledge of the binding site's location, may prove particularly useful in the development of allosteric inhibitors that target previously undiscovered binding sites.     

新型二氟甲基磷酸类酪氨酸蛋白磷酸酯酶1B抑制剂的分子动力学模拟和结合自由能计算

通过分子对接建立了一系列含二氟甲基磷酸基团(DFMP)或二氟甲基硫酸基团(DFMS)的抑制剂与酪氨酸蛋白磷酸酯酶1B(PTP1B)的相互作用模式, 并通过1 ns的分子动力学模拟和molecular mechanics/generalized Born surface area (MM/GBSA)方法计算了其结合自由能. 计算获得的结合自由能排序和抑制剂与靶酶间结合能力排序一致; 通过基于主方程的自由能计算方法, 获得了抑制剂与靶酶残基间相互作用的信息, 这些信息显示DFMP/DFMS基团的负电荷中心与PTP1B的221位精氨酸正电荷中心之间的静电相互作用强弱决定了此类抑制剂的活性, 进一步的分析还显示位于DFMP/DFMS基团中的氟原子或其他具有适当原子半径的氢键供体原子会增进此类抑制剂与PTP1B活性位点的结合能力.     

Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site

Eg5, a mitotic kinesin exclusively involved in the formation and function of the mitotic spindle has attracted interest as an anticancer drug target. Eg5 is co-crystallized with several inhibitors bound to its allosteric binding pocket. Each of these occupies a pocket formed by loop 5/helix α2 (L5/α2). Recently designed inhibitors additionally occupy a hydrophobic pocket of this site. The goal of the present study was to explore this hydrophobic pocket with our MED-SuMo fragment-based protocol, and thus discover novel chemical structures that might bind as inhibitors. The MED-SuMo software is able to compare and superimpose similar interaction surfaces upon the whole protein data bank (PDB). In a fragment-based protocol, MED-SuMo retrieves MED-Portions that encode protein-fragment binding sites and are derived from cross-mining protein-ligand structures with libraries of small molecules. Furthermore we have excluded intra-family MED-Portions derived from Eg5 ligands that occupy the hydrophobic pocket and predicted new potential ligands by hybridization that would fill simultaneously both pockets. Some of the latter having original scaffolds and substituents in the hydrophobic pocket are identified in libraries of synthetically accessible molecules by the MED-Search software. Ksenia Oguievetskaia and Laetitia Martin-Chanas contributed equally to this work.     

Druggable Allosteric Sites in β-Propeller Lectins

Carbohydrate-binding proteins (lectins) are auspicious targets in drug discovery to combat antimicrobial resistance; however, their non-carbohydrate drug-like inhibitors are still unavailable. Here, we present a druggable pocket in a β-propeller lectin BambL from Burkholderia ambifaria as a potential target for allosteric inhibitors. This site was identified employing ¹⁹F NMR fragment screening and a computational pocket prediction algorithm SiteMap. The structure–activity relationship study revealed the most promising fragment with a dissociation constant of 0.3±0.1 mM and a ligand efficiency of 0.3 kcal mol^?1 HA^?1 that affected the orthosteric site. This effect was substantiated by site-directed mutagenesis in the orthosteric and secondary pockets. Future drug-discovery campaigns that aim to develop small molecule inhibitors can benefit from allosteric sites in lectins as a new therapeutic approach against antibiotic-resistant pathogens.     

Druggability of methyl-lysine binding sites

Structural modules that specifically recognize—or read—methylated or acetylated lysine residues on histone peptides are important components of chromatin-mediated signaling and epigenetic regulation of gene expression. Deregulation of epigenetic mechanisms is associated with disease conditions, and antagonists of acetyl-lysine binding bromodomains are efficacious in animal models of cancer and inflammation, but little is known regarding the druggability of methyl-lysine binding modules. We conducted a systematic structural analysis of readers of methyl marks and derived a predictive druggability landscape of methyl-lysine binding modules. We show that these target classes are generally less druggable than bromodomains, but that some proteins stand as notable exceptions.     

Structure-activity analysis of the purine binding site of human liver glycogen phosphorylase

Human liver glycogen phosphorylase (HLGP) catalyzes the breakdown of glycogen to maintain serum glucose levels and is a therapeutic target for diabetes. HLGP is regulated by multiple interacting allosteric sites, each of which is a potential drug binding site. We used surface plasmon resonance (SPR) to screen for compounds that bind to the purine allosteric inhibitor site. We determined the affinities of a series of compounds and solved the crystal structures of three representative ligands with K(D) values from 17-550 microM. The crystal structures reveal that the affinities are partly determined by ligand-specific water-mediated hydrogen bonds and side chain movements. These effects could not be predicted; both crystallographic and SPR studies were required to understand the important features of binding and together provide a basis for the design of new allosteric inhibitors targeting this site.     

A Formylglycine-Peptide for the Site-Directed Identification of Phosphotyrosine-Mimetic Fragments**

Discovery of protein-binding fragments for precisely defined binding sites is an unmet challenge to date. Herein, formylglycine is investigated as a molecular probe for the sensitive detection of fragments binding to a spatially defined protein site . Formylglycine peptide 3 was derived from a phosphotyrosine-containing peptide substrate of protein tyrosine phosphatase PTP1B by replacing the phosphorylated amino acid with the reactive electrophile. Fragment ligation with formylglycine occurred in situ in aqueous physiological buffer. Structures and kinetics were validated by NMR spectroscopy. Screening and hit validation revealed fluorinated and non-fluorinated hit fragments being able to replace the native phosphotyrosine residue. The formylglycine probe identified low-affinity fragments with high spatial resolution as substantiated by molecular modelling. The best fragment hit, 4-amino-phenyl-acetic acid, was converted into a cellularly active, nanomolar inhibitor of the protein tyrosine phosphatase SHP2.     

Proteomimetic surface fragments distinguish targets by function

The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein–protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces.

Foldameric local surface mimetics (LSMs) detect spots at protein surfaces and are promising building blocks in a fragment-centric design of xenobiotic structures and protein–protein interaction inhibitors.     

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein–ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new “Rank Difference Ratio” metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4’s very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the “HIV Interaction and Viral Evolution Center”.     

Identification of the druggable concavity in homology models using the PLB index

Identification of the druggable concavity, in which drug-like molecules are highly inclined to bind, is an important step in structure-based drug design. We previously proposed an index named PLB (propensity for ligand binding), which is based on the amino acid composition characteristically observed at the small molecule binding sites in the X-ray structures of the complexes between proteins and drug-like small molecules. The PLB index was proven to be useful in identifying the druggable concavities in the quality X-ray structures of proteins. Here, we apply the PLB to predicting the druggable concavity in target proteins using the structures of homologous proteins constructed by homology modeling. In this study, we assembled a set of reference proteins that were accurately determined by X-ray analysis in forms of complexes with drug-like small molecules. Homology models for the reference protein were constructed using multiple homologous proteins as templates. The PLB index was then used to predict the druggable concavity. If the template protein in a complex with a drug-like small molecule was used, the druggable concavity was predicted well, with a prediction rate of 78%. When only the apo protein was available as the template, the practical prediction rate was 71%. Interestingly, even when the percent sequence identity between the reference and template proteins was lower than 30, the PLB index could successfully identify the druggable concavity in some cases. This study demonstrates the practical value of applying the PLB index to identifying the drugabble concavity in the homology model.     

Evidence that monastrol is an allosteric inhibitor of the mitotic kinesin Eg5

Monastrol, a cell-permeable inhibitor of the kinesin Eg5, has been used to probe the dynamic organization of the mitotic spindle. The mechanism by which monastrol inhibits Eg5 function is unknown. We found that monastrol inhibits both the basal and the microtubule-stimulated ATPase activity of the Eg5 motor domain. Unlike many ATPase inhibitors, monastrol does not compete with ATP binding to Eg5. Monastrol appears to inhibit microtubule-stimulated ADP release from Eg5 but does not compete with microtubule binding, suggesting that monastrol binds a novel allosteric site in the motor domain. Finally, we established that (S)-monastrol, as compared to the (R)-enantiomer, is a more potent inhibitor of Eg5 activity in vitro and in vivo. Future structural studies should help in designing more potent Eg5 inhibitors for possible use as anticancer drugs and cell biological reagents.     

Multiple Binding Sites Contribute to the Mechanism of Mixed Agonistic and Positive Allosteric Modulators of the Cannabinoid CB1 Receptor

The cannabinoid CB1 receptor (CB1R) is an abundant metabotropic G‐protein‐coupled receptor that has been difficult to address therapeutically because of CNS side effects exerted by orthosteric drug candidates. Recent efforts have focused on developing allosteric modulators that target CB1R. Compounds from the recently discovered class of mixed agonistic and positive allosteric modulators (Ago‐PAMs) based on 2‐phenylindoles have shown promising functional and binding properties as CB1R ligands. Here, we identify binding modes of both the CP 55,940 agonist and GAT228, a 2‐phenylindole allosteric modulator, by using our metadynamics simulation protocol, and quantify their affinity and cooperativity by atomistic simulations. We demonstrate the involvement of multiple adjunct binding sites in the Ago‐PAM characteristics of the 2‐phenylindole modulators and explain their ability to compete with orthosteric agonists at higher concentrations. We validate these results experimentally by showing the contribution of multiple sites on the allosteric binding of ZCZ011, another homologous member of the class, together with the orthosteric agonist.     

Small Molecule Degraders of Protein Tyrosine Phosphatase 1B and T-Cell Protein Tyrosine Phosphatase for Cancer Immunotherapy

Protein tyrosine phosphatase 1B (PTP1B) and T-cell protein tyrosine phosphatase (TC-PTP) play non-redundant negative regulatory roles in T-cell activation, tumor antigen presentation, insulin and leptin signaling, and are potential targets for several therapeutic applications. Here, we report the development of a highly potent and selective small molecule degrader DU-14 for both PTP1B and TC-PTP. DU-14 mediated PTP1B and TC-PTP degradation requires both target protein(s) and VHL E3 ligase engagement and is also ubiquitination- and proteasome-dependent. DU-14 enhances IFN-γ induced JAK1/2-STAT1 pathway activation and promotes MHC-I expression in tumor cells. DU-14 also activates CD8⁺ T-cells and augments STAT1 and STAT5 phosphorylation. Importantly, DU-14 induces PTP1B and TC-PTP degradation in vivo and suppresses MC38 syngeneic tumor growth. The results indicate that DU-14, as the first PTP1B and TC-PTP dual degrader, merits further development for treating cancer and other indications.     

Studies of the mechanism of selectivity of protein tyrosine phosphatase 1B (PTP1B) bidentate inhibitors using molecular dynamics simulations and free energy calculations

Bidentate inhibitors of protein tyrosine phosphatase 1B (PTP1B) are considered as a group of ideal inhibitors with high binding potential and high selectivity in treating type II diabetes. In this paper, the binding models of five bidentate inhibitors to PTP1B, TCPTP, and SHP-2 were investigated and compared by using molecular dynamics (MD) simulations and free energy calculations. The binding free energies were computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodology. The calculation results show that the predicted free energies of the complexes are well consistent with the experimental data. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition analysis indicates that the residues ARG24, ARG254, and GLN262 in the second binding site of PTP1B are essential for the high selectivity of inhibitors. Furthermore, the residue PHE182 close to the active site is also important for the selectivity and the binding affinity of the inhibitors. According to our analysis, it can be concluded that in most cases the polarity of the portion of the inhibitor that binds to the second binding site of the protein is positive to the affinity of the inhibitors while negative to the selectivity of the inhibitors. We expect that the information we obtained here can help to develop potential PTP1B inhibitors with more promising specificity.