SH2db, an information system for the SH2 domain.

SH2 domains are key mediators of phosphotyrosine-based signalling, and therapeutic targets for diverse, mostly oncological, disease indications. They have a highly conserved structure with a central beta sheet that divides the binding surface of the protein into two main pockets, responsible for phosphotyrosine binding (pY pocket) and substrate specificity (pY + 3 pocket). In recent years, structural databases have proven to be invaluable resources for the drug discovery community, as they contain highly relevant and up-to-date information on important protein classes. Here, we present SH2db, a comprehensive structural database and webserver for SH2 domain structures. To organize these protein structures efficiently, we introduce (i) a generic residue numbering scheme to enhance the comparability of different SH2 domains, (ii) a structure-based multiple sequence alignment of all 120 human wild-type SH2 domain sequences and their PDB and AlphaFold structures. The aligned sequences and structures can be searched, browsed and downloaded from the online interface of SH2db (http://sh2db.ttk.hu), with functions to conveniently prepare multiple structures into a Pymol session, and to export simple charts on the contents of the database. Our hope is that SH2db can assist researchers in their day-to-day work by becoming a one-stop shop for SH2 domain related research.

Allosteric regulation and crystallographic fragment screening of SARS-CoV-2 NSP15 endoribonuclease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). The NSP15 endoribonuclease enzyme, known as NendoU, is highly conserved and plays a critical role in the ability of the virus to evade the immune system. NendoU is a promising target for the development of new antiviral drugs. However, the complexity of the enzyme's structure and kinetics, along with the broad range of recognition sequences and lack of structural complexes, hampers the development of inhibitors. Here, we performed enzymatic characterization of NendoU in its monomeric and hexameric form, showing that hexamers are allosteric enzymes with a positive cooperative index, and with no influence of manganese on enzymatic activity. Through combining cryo-electron microscopy at different pHs, X-ray crystallography and biochemical and structural analysis, we showed that NendoU can shift between open and closed forms, which probably correspond to active and inactive states, respectively. We also explored the possibility of NendoU assembling into larger supramolecular structures and proposed a mechanism for allosteric regulation. In addition, we conducted a large fragment screening campaign against NendoU and identified several new allosteric sites that could be targeted for the development of new inhibitors. Overall, our findings provide insights into the complex structure and function of NendoU and offer new opportunities for the development of inhibitors.

Molecular Mechanism of Labelling Functional Cysteines by Heterocyclic Thiones.

Heterocyclic thiones have recently been identified as reversible covalent warheads, consistent with their mild electrophilic nature. Little is known so far about their mechanism of action in labelling nucleophilic sidechains, especially cysteines. The vast number of tractable cysteines promotes a wide range of target proteins to examine; however, our focus was put on functional cysteines. We chose the main protease of SARS-CoV-2 harboring Cys145 at the active site that is a structurally characterized and clinically validated target of covalent inhibitors. We screened an in-house, cysteine-targeting covalent inhibitor library which resulted in several covalent fragment hits with benzoxazole, benzothiazole and benzimidazole cores. Thione derivatives and Michael acceptors were selected for further investigations with the objective of exploring the mechanism of inhibition of the thiones and using the thoroughly characterized Michael acceptors for benchmarking our studies. Classical and hybrid quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations were carried out that revealed a new mechanism of covalent cysteine labelling by thione derivatives, which was supported by QM and free energy calculations and by a wide range of experimental results. Our study shows that the molecular recognition step plays a crucial role in the overall binding of both sets of molecules.

Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices.

Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

Conservation of Allosteric Ligand Binding Sites in G-Protein Coupled Receptors.

Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.

Extended continuous similarity indices: theory and application for QSAR descriptor selection.

Extended (or n-ary) similarity indices have been recently proposed to extend the comparative analysis of binary strings. Going beyond the traditional notion of pairwise comparisons, these novel indices allow comparing any number of objects at the same time. This results in a remarkable efficiency gain with respect to other approaches, since now we can compare N molecules in O(N) instead of the common quadratic O(N2) timescale. This favorable scaling has motivated the application of these indices to diversity selection, clustering, phylogenetic analysis, chemical space visualization, and post-processing of molecular dynamics simulations. However, the current formulation of the n-ary indices is limited to vectors with binary or categorical inputs. Here, we present the further generalization of this formalism so it can be applied to numerical data, i.e. to vectors with continuous components. We discuss several ways to achieve this extension and present their analytical properties. As a practical example, we apply this formalism to the problem of feature selection in QSAR and prove that the extended continuous similarity indices provide a convenient way to discern between several sets of descriptors.

Machine learning models for classification tasks related to drug safety.

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.

Discovery of a Non-Nucleoside SETD2 Methyltransferase Inhibitor against Acute Myeloid Leukemia.

Histone methyltransferases (HMTs) have attracted considerable attention as potential targets for pharmaceutical intervention in various malignant diseases. These enzymes are known for introducing methyl marks at specific locations of histone proteins, creating a complex system that regulates epigenetic control of gene expression and cell differentiation. Here, we describe the identification of first-generation cell-permeable non-nucleoside type inhibitors of SETD2, the only mammalian HMT that is able to tri-methylate the K36 residue of histone H3. By generating the epigenetic mark H3K36me3, SETD2 is involved in the progression of acute myeloid leukemia. We developed a structure-based virtual screening protocol that was first validated in retrospective studies. Next, prospective screening was performed on a large library of commercially available compounds. Experimental validation of 22 virtual hits led to the discovery of three compounds that showed dose-dependent inhibition of the enzymatic activity of SETD2. Compound C13 effectively blocked the proliferation of two acute myeloid leukemia (AML) cell lines with MLL rearrangements and led to decreased H3K36me3 levels, prioritizing this chemotype as a viable chemical starting point for drug discovery projects.

Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification.

Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary (multiclass) classification. It is also known that the models are ranked differently according to the performance merit(s) used. Here, 25 performance parameters were calculated for each model, then factorial ANOVA was applied to compare the results. The results clearly show the differences not just between the applied machine learning algorithms but also between the dataset sizes and to a lesser extent the train/test split ratios. The XGBoost algorithm could outperform the others, even in multiclass modeling. The performance parameters reacted differently to the change of the sample set size; some of them were much more sensitive to this factor than the others. Moreover, significant differences could be detected between train/test split ratios as well, exerting a great effect on the test validation of our models.

The impact of binding site waters on the activity/selectivity trade-off of Janus kinase 2 (JAK2) inhibitors.

Structure based optimization of B39, an indazole-based low micromolar JAK2 virtual screening hit is reported. Analysing the effect of certain modifications on the activity and selectivity of the analogues suggested that these parameters are influenced by water molecules available in the binding site. Simulation of water networks in combination with docking enabled us to identify the key waters and to optimize our primary hit into a low nanomolar JAK2 lead with promising selectivity over JAK1.

Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics.

Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of ranking differences (SRD) and analysis of variance (ANOVA) were applied for evaluation. The effect of dataset composition (balanced vs. imbalanced) and 2-class vs. multiclass classification scenarios was also studied. Most of the performance metrics are sensitive to dataset composition, especially in 2-class classification problems. The optimal machine learning algorithm also depends significantly on the composition of the dataset.

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.

Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.

Discovery of Immunoproteasome Inhibitors Using Large-Scale Covalent Virtual Screening.

Large-scale virtual screening of boronic acid derivatives was performed to identify nonpeptidic covalent inhibitors of the ß5i subunit of the immunoproteasome. A hierarchical virtual screening cascade including noncovalent and covalent docking steps was applied to a virtual library of over 104,000 compounds. Then, 32 virtual hits were selected, out of which five were experimentally confirmed. Biophysical and biochemical tests showed micromolar binding affinity and time-dependent inhibitory potency for two compounds. These results validate the computational protocol that allows the screening of large compound collections. One of the lead-like boronic acid derivatives identified as a covalent immunoproteasome inhibitor is a suitable starting point for chemical optimization.

Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of D-amino acid oxidase inhibitors.

Optimization of fragment size D-amino acid oxidase (DAAO) inhibitors was investigated using a combination of computational and experimental methods. Retrospective free energy perturbation (FEP) calculations were performed for benzo[d]isoxazole derivatives, a series of known inhibitors with two potential binding modes derived from X-ray structures of other DAAO inhibitors. The good agreement between experimental and computed binding free energies in only one of the hypothesized binding modes strongly support this bioactive conformation. Then, a series of 1-H-indazol-3-ol derivatives formerly not described as DAAO inhibitors was investigated. Binding geometries could be reliably identified by structural similarity to benzo[d]isoxazole and other well characterized series and FEP calculations were performed for several tautomers of the deprotonated and protonated compounds since all these forms are potentially present owing to the experimental pKa values of representative compounds in the series. Deprotonated compounds are proposed to be the most important bound species owing to the significantly better agreement between their calculated and measured affinities compared to the protonated forms. FEP calculations were also used for the prediction of the affinities of compounds not previously tested as DAAO inhibitors and for a comparative structure-activity relationship study of the benzo[d]isoxazole and indazole series. Selected indazole derivatives were synthesized and their measured binding affinity towards DAAO was in good agreement with FEP predictions.

Discovery of Subtype Selective Janus Kinase (JAK) Inhibitors by Structure-Based Virtual Screening.

Janus kinase inhibitors represent a promising opportunity for the pharmaceutical intervention of various inflammatory and oncological indications. Subtype selective inhibition of these enzymes, however, is still a very challenging goal. In this study, a novel, customized virtual screening protocol was developed with the intention of providing an efficient tool for the discovery of subtype selective JAK2 inhibitors. The screening protocol involves protein ensemble-based docking calculations combined with an Interaction Fingerprint (IFP) based scoring scheme for estimating ligand affinities and selectivities, respectively. The methodology was validated in retrospective studies and was applied prospectively to screen a large database of commercially available compounds. Six compounds were identified and confirmed in vitro, with an indazole-based hit exhibiting promising selectivity for JAK2 vs JAK1. Having demonstrated that the described methodology is capable of identifying subtype selective chemical starting points with a favorable hit rate (11%), we believe that the presented screening concept can be useful for other kinase targets with challenging selectivity profiles.

Identification of 8-Hydroxyquinoline Derivatives Active Against Somatic V658F Mutant JAK1-Dependent Cells.

Janus kinases (JAKs) and their gain-of-function mutants have been implicated in a range of oncological, inflammatory, and autoimmune conditions, which has sparked great research interest in the discovery and development of small-molecule JAK inhibitors. Two molecules are currently marketed as JAK inhibitors, but due to the displayed side effects (owing to their suboptimal selectivities among the various JAK subtypes) new JAK inhibitors are still sought after. We present the results of an extensive virtual screening campaign based on a multi-step screening protocol involving ligand docking. The screening yielded five new, experimentally validated inhibitors of JAK1 with 8-hydroxyquinoline as a novel hinge-binding scaffold. The compounds did not only display favorable potencies in a JAK1V658F -driven cell-based assay but were also shown to be non-cytotoxic on rat liver cells.

Electrophilic MiniFrags Revealed Unprecedented Binding Sites for Covalent HDAC8 Inhibitors.

Screening of ultra-low-molecular weight ligands (MiniFrags) successfully identified viable chemical starting points for a variety of drug targets. Here we report the electrophilic analogues of MiniFrags that allow the mapping of potential binding sites for covalent inhibitors by biochemical screening and mass spectrometry. Small electrophilic heterocycles and their N-quaternized analogues were first characterized in the glutathione assay to analyze their electrophilic reactivity. Next, the library was used for systematic mapping of potential covalent binding sites available in human histone deacetylase 8 (HDAC8). The covalent labeling of HDAC8 cysteines has been proven by tandem mass spectrometry measurements, and the observations were explained by mutating HDAC8 cysteines. As a result, screening of electrophilic MiniFrags identified three potential binding sites suitable for the development of allosteric covalent HDAC8 inhibitors. One of the hit fragments was merged with a known HDAC8 inhibitor fragment using different linkers, and the linker length was optimized to result in a lead-like covalent inhibitor.

Maximizing the integration of virtual and experimental screening in hit discovery.

INTRODUCTION: Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches. AREAS COVERED: After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies. EXPERT OPINION: Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.

Consensus Virtual Screening Identified [1,2,4]Triazolo[1,5-b]isoquinolines As MELK Inhibitor Chemotypes.

Maternal Embryonic Leucine-zipper Kinase (MELK) is a current oncotarget involved in a diverse range of human cancers, with the usage of MELK inhibitors being explored clinically. Here, we aimed to discover new MELK inhibitor chemotypes from our in-house compound library with a consensus-based virtual screening workflow, employing three screening concepts. After careful retrospective validation, prospective screening and in vitro enzyme inhibition testing revealed a series of [1,2,4]triazolo[1,5-b]isoquinolines as a new structural class of MELK inhibitors, with the lead compound of the series exhibiting a sub-micromolar inhibitory activity. The structure-activity relationship of the series was explored by testing further analogs based on a structure-guided selection process. Importantly, the present work marks the first disclosure of the synthesis and bioactivity of this class of compounds.

Designed Peptide Inhibitors of STEP Phosphatase-GluA2 AMPA Receptor Interaction Enhance the Cognitive Performance in Rats.

Cognitive impairment and learning ability of the brain are directly linked to synaptic plasticity as measured in changes of long-term potentiation (LTP) and long-term depression (LTD) in animal models of brain diseases. LTD reflects a sustained reduction of the synaptic AMPA receptor content based on targeted clathrin-mediated endocytosis. AMPA receptor endocytosis is initiated by dephosphorylation of Tyr876 on the C-terminus of the AMPAR subunit GluA2. The brain-specific striatal-enriched protein tyrosine phosphatase (STEP) is responsible for this process. To identify new, highly effective inhibitors of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) internalization, we performed structure-based design of peptides able to inhibit STEP-GluA2-CT complex formation. Two short peptide derivatives were found as efficient in vitro inhibitors. Our in vivo experiments evidenced that both peptides restore the memory deficits and display anxiolytic and antidepressant effects in a scopolamine-treated rat model. The interference peptides identified and characterized here represent promising lead compounds for novel cognitive enhancers and/or behavioral modulators.