Search

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package., (© 2022. The Author(s).)

Cloning may seem to be a view from the past. The time before software, computers and AI were invented. It seems to us worth discussing these points in view of our favorite target: the melatoninergic system. In a few stances, it might be important to point out that even in the new era of dry science, there is still a need to experiment and to prove at the bench that our in silico assertions are right. Most of the living animals express to some extend the melatonin receptors. Some of these animal genomes were completely or partially sequenced, and it is tempting to extract from this huge information the sequence(s) of our favorite genes (MLT receptors). Then, why bother cloning, as opposed to simply built the gene and express it in a host cell? Because the genetic boundaries of the expressed sequence(s) are not 100% sure. Because the melatonin receptor gene(s) comprise a first exon 25,000 base pair far from the second one and the limits between this Ex1 and In1-as between In1 and Ex2-are subject to changes that might have a huge impact on the biochemical properties of the receptor, once expressed. Because a receptor is a biochemical entity with characteristics that are important for the functioning of this particular pathway, and more generally, for the functioning of life., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

7-Azaindole has been identified as a novel bidentate anchor point for allosteric glucokinase activators. A systematic investigation around three principal parts of the new small molecule glucokinase activators led to a robust SAR in agreement with structural data that also helped to assess the conformational flexibility of the allosteric activation site. The increase in glucose uptake resulting from glucokinase activation in hepatocytes in vitro translated into the efficient lowering of glucose levels in vivo with the best compounds.

Today, drug discovery routinely uses experimental assays to determine very early if a lead compound can yield certain types of off-target activity. Among such off targets is hERG. The ion channel plays a primordial role in membrane repolarization and altering its activity can cause severe heart arrhythmia and sudden death. Despite routine tests for hERG activity, rather little information is available for helping medicinal chemists and molecular modelers to rationally circumvent hERG activity. In this article novel insights into the dynamics of hERG channel closure are described. Notably, helical pairwise closure movements have been observed. Implications and relations to hERG inactivation are presented. Based on these dynamics novel insights on hERG blocker placement are presented, compared to literature, and discussed. Last, new evidence for horizontal ligand positioning is shown in light of former studies on hERG blockers.

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.