Discovery and biological characterization of a novel mesoionic insecticide fenmezoditiaz.

BACKGROUND: Many piercing-sucking insects have developed resistance or cross-resistance to many insecticides targeting insect neural nicotinic acetylcholine receptor (nAChR). Here we are aiming to present the discovery of a novel mesoionic insecticide, fenmezoditiaz, by BASF through structure-based drug design (SBDD) approaches. It has recently been added to the Insecticide Resistance Action Committee mode of classification (IRAC 4E). It is being developed for plant protection against piercing-sucking pests, especially rice hopper complex. RESULTS: The soluble acetylcholine binding protein (AChBP) from the sea slug Aplysia californica was modified using site-directed mutagenesis and based on putative aphid nAChR subunit sequences to create soluble insect-like AChBPs. Among them, insect-like ß1 AChBP and native aphid membrane preparation showed the highest correlated biochemical affinity toward structurally diverse ligands. This mutant AChBP was used to understand how insect nAChRs structurally interact with mesoionics, which was then utilized to design novel mesoionics including fenmezoditiaz. It is an excellent systemic insecticide with diverse application methods and has a broad insecticidal spectrum, especially against piercing/sucking insects. It lacks cross-resistance for neonicotinoid resistant plant hoppers. Field-collected brown plant hopper populations from Asian countries showed high susceptibility. CONCLUSIONS: Fenmezoditiaz is a systemic insecticide with a broad spectrum, lack of cross-resistance and it could be an additional tool for integrated pest management and insecticide resistance management, especially for the rice hopper complex. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site.

Helicobacter pylori is associated with inflammatory diseases and can cause gastric cancer and mucosa-associated lymphoma. One of the bacterium's key proteins is high temperature requirement A (HpHtrA) protein, an extracellular serine protease that cleaves E-cadherin of gastric epithelial cells, which leads to loss of cell-cell adhesion. Inhibition of HpHtrA may constitute an intervention strategy against H. pylori infection. Guided by the computational prediction of hypothetical ligand binding sites on the surface of HpHtrA, we performed residue mutation experiments that confirmed the functional relevance of an allosteric region. We virtually screened for potential ligands addressing this surface cleft located between the catalytic and PDZ1 domains. Our receptor-based computational method represents protein surface pockets in terms of graph frameworks and retrieves small molecules that satisfy the constraints given by the pocket framework. A new chemical entity was identified that blocked E-cadherin cleavage in vitro by direct binding to HpHtrA, and efficiently blocked pathogen transmigration across the gastric epithelial barrier. A preliminary crystal structure of HpHtrA confirms the validity of a comparative "homology" model of the enzyme, which we used for the computational study. The results of this study demonstrate that addressing orphan protein surface cavities of target macromolecules can lead to new bioactive ligands.

Optimization of benzodiazepinones as selective inhibitors of the X-linked inhibitor of apoptosis protein (XIAP) second baculovirus IAP repeat (BIR2) domain.

The IAPs are key regulators of the apoptotic pathways and are commonly overexpressed in many cancer cells. IAPs contain one to three BIR domains that are crucial for their inhibitory function. The pro-survival properties of XIAP come from binding of the BIR domains to the pro-apoptotic caspases. The BIR3 domain of XIAP binds and inhibits caspase 9, while the BIR2 domain binds and inhibits the terminal caspases 3 and 7. While XIAP BIR3 inhibitors have previously been reported, they also inhibit cIAP1/2 and promote the release of TNFα, potentially limiting their therapeutic utility. This paper will focus on the optimization of selective XIAP BIR2 inhibitors leading to the discovery of highly potent benzodiazepinone 36 (IC50 = 45 nM), which has high levels of selectivity over XIAP BIR3 and cIAP1 BIR2/3 and shows efficacy in a xenograft pharmacodynamic model monitoring caspase activity while not promoting the release of TNFα in vitro.

Benzazepinones and benzoxazepinones as antagonists of inhibitor of apoptosis proteins (IAPs) selective for the second baculovirus IAP repeat (BIR2) domain.

XIAP is a key regulator of apoptosis, and its overexpression in cancer cells may contribute to their survival. The antiapoptotic function of XIAP derives from its BIR domains, which bind to and inhibit pro-apoptotic caspases. Most known IAP inhibitors are selective for the BIR3 domain and bind to cIAP1 and cIAP2 as well as XIAP. Pathways activated upon cIAP binding contribute to the function of these compounds. Inhibitors selective for XIAP should exert pro-apoptotic effects through competition with the terminal caspases. This paper details our synthetic explorations of a novel XIAP BIR2-selective benzazepinone screening hit with a focus on increasing BIR2 potency and overcoming high in vivo clearance. These efforts led to the discovery of benzoxazepinone 40, a potent BIR2-selective inhibitor with good in vivo pharmacokinetic properties which potentiates apoptotic signaling in a manner mechanistically distinct from that of known pan-IAP inhibitors.

PROLIX: rapid mining of protein-ligand interactions in large crystal structure databases.

A central problem in structure-based drug design is understanding protein-ligand interactions quantitatively and qualitatively. Several recent studies have highlighted from a qualitative perspective the nature of these interactions and their utility in drug discovery. However, a common limitation is a lack of adequate tools to mine these interactions comprehensively, since exhaustive searches of the protein data bank are time-consuming and difficult to perform. Consequently, fundamental questions remain unanswered: How unique or how common are the protein-ligand interactions observed in a given drug design project when compared to all complexed structures in the protein data bank? Which interaction patterns might explain the affinity of a tool compound toward unwanted targets? To answer these questions and to enable the systematic and comprehensive study of protein-ligand interactions, we introduce PROLIX (Protein Ligand Interaction Explorer), a tool that uses sophisticated fingerprint representations of protein-ligand interaction patterns for rapid data mining in large crystal structure databases. Our implementation strategy pursues a branch-and-bound technique that enables mining against thousands of complexes within a few seconds. Key elements of PROLIX include (i) an intuitive interface that enables users to formulate complex queries easily, (ii) exceptional speed for results retrieval, and (iii) a sophisticated results summarization. Herein we describe the algorithms developed to enable complex queries and fast retrieval of search results, as well as the intuitive aspects of the user interface and summarization viewer.

Self-organizing fuzzy graphs for structure-based comparison of protein pockets.

Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology. Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called "orphan" proteins with known three-dimensional structure but unknown function, and predict ligand promiscuity of binding pockets. We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity. Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels. For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms. A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns. In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype. We determined a score threshold that indicates "true" pocket similarity with high reliability, which not only supports structure-based drug design but also allows for sequence-independent studies of the proteome.

Architectural repertoire of ligand-binding pockets on protein surfaces.

Knowledge of the three-dimensional structure of ligand binding sites in proteins provides valuable information for computer-assisted drug design. We present a method for the automated extraction and classification of ligand binding site topologies, in which protein surface cavities are represented as branched frameworks. The procedure employs a growing neural gas approach for pocket topology assignment and pocket framework generation. We assessed the structural diversity of 623 known ligand binding site topologies based on framework cluster analysis. At a resolution of 5 A only 23 structurally distinct topology groups were formed; this suggests an overall limited structural diversity of ligand-accommodating protein cavities. Higher resolution allowed for identification of protein-family specific pocket features. Pocket frameworks highlight potentially preferred modes of ligand-receptor interactions and will help facilitate the identification of druggable subpockets suitable for ligand affinity and selectivity optimization.

Model structure of APOBEC3C reveals a binding pocket modulating ribonucleic acid interaction required for encapsidation.

Human APOBEC3 (A3) proteins form part of the intrinsic immunity to retroviruses. Carrying 1 or 2 copies of a cytidine deaminase motif, A3s act by deamination of retroviral genomes during reverse transcription. HIV-1 overcomes this inhibition by the Vif protein, which prevents incorporation of A3 into virions. In this study we modeled and probed the structure of APOBEC3C (A3C), a single-domain A3 with strong antilentiviral activity. The 3-dimensional protein model was used to predict the effect of mutations on antiviral activity, which was tested in a Deltavif simian immunodeficiency virus (SIV) reporter virus assay. We found that A3C activity requires protein dimerization for antiviral activity against SIV. Furthermore, by using a structure-based algorithm for automated pocket extraction, we detected a putative substrate binding pocket of A3C distal from the zinc-coordinating deaminase motif. Mutations in this region diminished antiviral activity by excluding A3C from virions. We found evidence that the small 5.8S RNA specifically binds to this locus and mediates incorporation of A3C into virus particles.

Form follows function: shape analysis of protein cavities for receptor-based drug design.

Identification of potential ligand-binding pockets is an initial step in receptor-based drug design. While many geometric or energy-based binding-site prediction methods characterize the size and shape of protein cavities, few of them offer an estimate of the pocket's ability to bind small drug-like molecules. Here, we present a shape-based technique to examine binding-site druggability from the crystal structure of a given protein target. The method includes the PocketPicker algorithm to determine putative binding-site volumes for ligand-interaction. Pocket shape descriptors were calculated for both known ligand binding sites and empty pockets and were then subjected to self-organizing map clustering. Descriptors were calculated for structures derived from a database of representative drug-protein complexes with experimentally determined binding affinities to characterize the "druggable pocketome". The new method provides a means for selecting drug targets and potential ligand-binding pockets based on structural considerations and addresses orphan binding sites.

PocketPicker: analysis of ligand binding-sites with shape descriptors.

BACKGROUND: Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. RESULTS: We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITE(cs), PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITE(cs) and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. CONCLUSION: The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.