Andrew Anighoro

Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations

In the last decades, molecular docking has emerged as an increasingly useful tool in the modern drug discovery process, but it still needs to overcome many hurdles and limitations such as how to account for protein flexibility and poor scoring function performance. For this reason, it has been recognized that in many cases docking results need to be post-processed to achieve a significant agreement with experimental activities. In this study, we have evaluated the performance of MM-PBSA and MM-GBSA scoring functions, implemented in our post-docking procedure BEAR, in rescoring docking solutions. For the first time, the performance of this post-docking procedure has been evaluated on six different biological targets (namely estrogen receptor, thymidine kinase, factor Xa, adenosine deaminase, aldose reductase, and enoyl ACP reductase) by using i) both a single and a multiple protein conformation approach, and ii) two different software, namely AutoDock and LibDock. The assessment has been based on two of the most important criteria for the evaluation of docking methods, i.e., the ability of known ligands to enrich the top positions of a ranked database with respect to molecular decoys, and the consistency of the docking poses with crystallographic binding modes. We found that, in many cases, MM-PBSA and MM-GBSA are able to yield higher enrichment factors compared to those obtained with the docking scoring functions alone. However, for only a minority of the cases, the enrichment factors obtained by using multiple protein conformations were higher than those obtained by using only one protein conformation.

Role of bifidobacteria in the hydrolysis of chlorogenic acid

This study aimed to explore the capability of potentially probiotic bifidobacteria to hydrolyze chlorogenic acid into caffeic acid (CA), and to recognize the enzymes involved in this reaction. Bifidobacterium strains belonging to eight species occurring in the human gut were screened. The hydrolysis seemed peculiar of Bifidobacterium animalis, whereas the other species failed to release CA. Intracellular feruloyl esterase activity capable of hydrolyzing chlorogenic acid was detected only in B. animalis. In silico research among bifidobacteria esterases identified Balat_0669 as the cytosolic enzyme likely responsible of CA release in B. animalis. Comparative modeling of Balat_0669 and molecular docking studies support its role in chlorogenic acid hydrolysis. Expression, purification, and functional characterization of Balat_0669 in Escherichia coli were obtained as further validation. A possible role of B. animalis in the activation of hydroxycinnamic acids was demonstrated and new perspectives were opened in the development of new probiotics, specifically selected for the enhanced bioconversion of phytochemicals into bioactive compounds.

Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes

Molecular docking is the premier approach to structure-based virtual screening. While ligand posing is often successful, compound ranking using force field-based scoring functions remains difficult. Uncertainties associated with scoring often limit the ability to confidently identify new active compounds. In this study, we introduce an alternative approach to compound ranking. Rather than using scoring functions for final ranking, compounds are prioritized on the basis of computed 3D similarity to known crystallographic ligands. For different targets, it is shown that 3D similarity-based ranking consistently improves the enrichment of active compounds compared to ranking obtained using scoring functions, even if only a single crystallographic ligand is used as a reference. While the strategy is not applicable in cases where no cocrystal structure is available, it should be a promising alternative or complement to conventional scoring in many instances. Since ligand similarity calculations are used to rank docking poses, which are independently derived, the approach introduced herein also contributes to the integration of ligand- and structure-based computational screening methods.

Structure-based discovery of the first allosteric inhibitors of cyclin-dependent kinase 2

Allosteric targeting of protein kinases via displacement of the structural αC helix with type III allosteric inhibitors is currently gaining a foothold in drug discovery. Recently, the first crystal structure of CDK2 with an open allosteric pocket adjacent to the αC helix has been described, prospecting new opportunities to design more selective inhibitors, but the structure has not yet been exploited for the structure-based design of type III allosteric inhibitors. In this work we report the results of a virtual screening campaign that resulted in the discovery of the first-in-class type III allosteric ligands of CDK2. Using a combination of docking and post-docking analyses made with our tool BEAR, 7 allosteric ligands (hit rate of 20%) with micromolar affinity for CDK2 were identified, some of them inhibiting the growth of breast cancer cell lines in the micromolar range. Competition experiments performed in the presence of the ATP-competitive inhibitor staurosporine confirmed that the 7 ligands are truly allosteric, in agreement with their design. Of these, compound 2 bound CDK2 with an EC50 value of 3 μM and inhibited the proliferation of MDA-MB231 and ZR-75–1 breast cancer cells with IC50 values of approximately 20 μM, while compound 4 had an EC50 value of 71 μM and IC50 values around 4 μM. Remarkably, the most potent compound 4 was able to selectively inhibit CDK2-mediated Retinoblastoma phosphorylation, confirming that its mechanism of action is fully compatible with a selective inhibition of CDK2 phosphorylation in cells. Finally, hit expansion through analog search of the most potent inhibitor 4 revealed an additional ligand 4g with similar in vitro potency on breast cancer cells.

Enrichment Factor Analyses on G-Protein Coupled Receptors with Known Crystal Structure

G-protein coupled receptors (GPCRs) are highly relevant drug targets. Four GPCRs with known crystal structure were analyzed with docking (AutoDock4) and postdocking (MM-PBSA) in order to evaluate the ability to recognize known antagonists from a larger database of molecular decoys and to predict correct binding modes. Moreover, implications on multitarget drug screening are put forward. The results suggest that these methods may be of interest to the growing field of GPCR structure-based virtual screening.

Computational polypharmacology analysis of the heat shock protein 90 interactome.

The design of a single drug molecule that is able to simultaneously and specifically interact with multiple biological targets is gaining major consideration in drug discovery. However, the rational design of drugs with a desired polypharmacology profile is still a challenging task, especially when these targets are distantly related or unrelated. In this work, we present a computational approach aimed at the identification of suitable target combinations for multitarget drug design within an ensemble of biologically relevant proteins. The target selection relies on the analysis of activity annotations present in molecular databases and on ligand-based virtual screening. A few target combinations were also inspected with structure-based methods to demonstrate that the identified dual-activity compounds are able to bind target combinations characterized by remote binding site similarities. Our approach was applied to the heat shock protein 90 (Hsp90) interactome, which contains several targets of key importance in cancer. Promising target combinations were identified, providing a basis for the computational design of compounds with dual activity. The approach may be used on any ensemble of proteins of interest for which known inhibitors are available.

Insights into the interaction of negative allosteric modulators with the metabotropic glutamate receptor 5: Discovery and computational modeling of a new series of ligands with nanomolar affinity

Metabotropic glutamate receptor 5 (mGlu5) is a biological target implicated in major neurological and psychiatric disorders. In the present study, we have investigated structural determinants of the interaction of negative allosteric modulators (NAMs) with the seven-transmembrane (7TM) domain of mGlu5. A homology model of the 7TM receptor domain built on the crystal structure of the mGlu1 template was obtained, and the binding modes of known NAMs, namely MPEP and fenobam, were investigated by docking and molecular dynamics simulations. The results were validated by comparison with mutagenesis data available in the literature for these two ligands, and subsequently corroborated by the recently described mGlu5 crystal structure. Moreover, a new series of NAMs was synthesized and tested, providing compounds with nanomolar affinity. Several structural modifications were sequentially introduced with the aim of identifying structural features important for receptor binding. The synthesized NAMs were docked in the validated homology model and binding modes were used to interpret and discuss structure-activity relationships within this new series of compounds. Finally, the models of the interaction of NAMs with mGlu5 were extended to include important non-aryl alkyne mGlu5 NAMs taken from the literature. Overall, the results provide useful insights into the molecular interaction of negative allosteric modulators with mGlu5 and may facilitate the design of new modulators for this class of receptors.

Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space

Heat shock protein 90 (Hsp90) and B-Raf are validated targets for anticancer drug discovery. Although there is strong evidence that concomitant inhibition of Hsp90 and B-Raf may provide significant therapeutic benefits, molecules endowed with dual activity against the two targets have not been reported. For the first time, we show that Hsp90 and B-Raf inhibitors have overlapping chemical space and we disclose the first-in-class dual inhibitors. The compounds were identified through a computational strategy especially devised for detecting ligands with dual-target activity. Although the two targets had only remote binding site similarity, we were able to identify dual inhibitors with well-balanced in vitro potencies and relatively low molecular weight. Remarkably, they also inhibited the V600E mutant form of B-Raf with similar potency. This study provides the first direct proof that designing dual ligands of Hsp90 and a kinase is possible, thus opening the way to new interesting possibilities in drug discovery.

Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor

We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of docking poses to crystallographic ligand(s) as well as similarity of receptor–ligand interaction patterns was consistently superior to conventional scoring functions for prioritizing antagonists over decoys. Moreover, the use of crystallographic antagonists and agonists, a core fragment of an antagonist, and a model of an agonist placed into the binding site of an antagonist-bound form of the receptor resulted in a significant early enrichment of antagonists in compound rankings. Taken together, these findings showed that the use of binding modes of agonists and/or antagonists, even if they were only approximate, for similarity assessment of docking poses or comparison of interaction patterns increased the odds of identifying new active compounds over conventional scoring.

Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors

Ligand docking into homology models of G-protein-coupled receptors (GPCRs) is a widely used approach in computational compound screening. The generation of “double-hypothetical” models of ligand–target complexes has intrinsic accuracy limitations that further complicate compound ranking and selection compared to those of X-ray structures. Given these uncertainties, we have explored “fuzzy 3D similarity” between hypothetical binding modes of known ligands in homology models and docking poses of database compounds as an alternative to conventional scoring schemes. Therefore, GPCR homology models at varying accuracy levels were generated and used for docking. Increases in recall performance were observed for fuzzy 3D similarity ranking using single or multiple ligand poses compared to that of conventional scoring functions and interaction fingerprints. Fuzzy similarity ranking was also successfully applied to docking into an external model of a GPCR for which no experimental structure is currently available. Taken together, our results indicate that the use of putative ligand poses, albeit approximate at best, increases the odds of identifying active compounds in docking screens of GPCR homology models.