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.

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.

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.

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/.

Semisynthetic teicoplanin derivatives with dual antimicrobial activity against SARS-CoV-2 and multiresistant bacteria.

Patients infected with SARS-CoV-2 risk co-infection with Gram-positive bacteria, which severely affects their prognosis. Antimicrobial drugs with dual antiviral and antibacterial activity would be very useful in this setting. Although glycopeptide antibiotics are well-known as strong antibacterial drugs, some of them are also active against RNA viruses like SARS-CoV-2. It has been shown that the antiviral and antibacterial efficacy can be enhanced by synthetic modifications. We here report the synthesis and biological evaluation of seven derivatives of teicoplanin bearing hydrophobic or superbasic side chain. All but one teicoplanin derivatives were effective in inhibiting SARS-CoV-2 replication in VeroE6 cells. One lipophilic and three perfluoroalkyl conjugates showed activity against SARS-CoV-2 in human Calu-3 cells and against HCoV-229E, an endemic human coronavirus, in HEL cells. Pseudovirus entry and enzyme inhibition assays established that the teicoplanin derivatives efficiently prevent the cathepsin-mediated endosomal entry of SARS-CoV-2, with some compounds inhibiting also the TMPRSS2-mediated surface entry route. The teicoplanin derivatives showed good to excellent activity against Gram-positive bacteria resistant to all approved glycopeptide antibiotics, due to their ability to dually bind to the bacterial membrane and cell-wall. To conclude, we identified three perfluoralkyl and one monoguanidine analog of teicoplanin as dual inhibitors of Gram-positive bacteria and SARS-CoV-2.

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.

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.

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.

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.

Natural Apocarotenoids and Their Synthetic Glycopeptide Conjugates Inhibit SARS-CoV-2 Replication.

The protracted global COVID-19 pandemic urges the development of new drugs against the causative agent SARS-CoV-2. The clinically used glycopeptide antibiotic, teicoplanin, emerged as a potential antiviral, and its efficacy was improved with lipophilic modifications. This prompted us to prepare new lipophilic apocarotenoid conjugates of teicoplanin, its pseudoaglycone and the related ristocetin aglycone. Their antiviral effect was tested against SARS-CoV-2 in Vero E6 cells, using a cell viability assay and quantitative PCR of the viral RNA, confirming their micromolar inhibitory activity against viral replication. Interestingly, two of the parent apocarotenoids, bixin and ß-apo-8'carotenoic acid, exerted remarkable anti-SARS-CoV-2 activity. Mechanistic studies involved cathepsin L and B, as well as the main protease 3CLPro, and the results were rationalized by computational studies. Glycopeptide conjugates show dual inhibitory action, while apocarotenoids have mostly cathepsin B and L affinity. Since teicoplanin is a marketed antibiotic and the natural bixin is an approved, cheap and widely used red colorant food additive, these readily available compounds and their conjugates as potential antivirals are worthy of further exploration.

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.

Extended many-item similarity indices for sets of nucleotide and protein sequences.

Quantification of similarities between protein sequences or DNA/RNA strands is a (sub-)task that is ubiquitously present in bioinformatics workflows, and is usually accomplished by pairwise comparisons of sequences, utilizing simple (e.g. percent identity) or more intricate concepts (e.g. substitution scoring matrices). Complex tasks (such as clustering) rely on a large number of pairwise comparisons under the hood, instead of a direct quantification of set similarities. Based on our recently introduced framework that enables multiple comparisons of binary molecular fingerprints (i.e., direct calculation of the similarity of fingerprint sets), here we introduce novel symmetric similarity indices for analogous calculations on sets of character sequences with more than two (t) possible items (e.g. DNA/RNA sequences with t = 4, or protein sequences with t = 20). The features of these new indices are studied in detail with analysis of variance (ANOVA), and demonstrated with three case studies of protein/DNA sequences with varying degrees of similarity (or evolutionary proximity). The Python code for the extended many-item similarity indices is publicly available at: https://github.com/ramirandaq/tn_Comparisons.

Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development.

Binary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluation. This work aims to define the discrimination ability of CATA participants by calculating the consensus values of 44 binary similarity measures. The proposed methodology consists of three steps: (i) calculating the binary similarity values of the assessors, sample pair-wise; (ii) clustering participants into good and poor discriminators based on their binary similarity values; (iii) performing correspondence analysis on the CATA data of the two clusters. Results of three case studies are presented, highlighting that a simple clustering based on the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination (even according to overall liking).

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.

Exploring protein hotspots by optimized fragment pharmacophores.

Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores at the most preferred hotspots. The efficiency of this approach is demonstrated with a pilot library of 96 fragment-sized compounds (SpotXplorer0) that is validated on popular target classes and emerging drug targets. Biochemical screening against a set of GPCRs and proteases retrieves compounds containing an average of 70% of known pharmacophores for these targets. More importantly, SpotXplorer0 screening identifies confirmed hits against recently established challenging targets such as the histone methyltransferase SETD2, the main protease (3CLPro) and the NSP3 macrodomain of SARS-CoV-2.

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection.

Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over "traditional" pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the n-ary and binary indices rank the data in the same way. We demonstrate the use of the new n-ary similarity metrics on t-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons.

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristics†.

Quantification of the similarity of objects is a key concept in many areas of computational science. This includes cheminformatics, where molecular similarity is usually quantified based on binary fingerprints. While there is a wide selection of available molecular representations and similarity metrics, there were no previous efforts to extend the computational framework of similarity calculations to the simultaneous comparison of more than two objects (molecules) at the same time. The present study bridges this gap, by introducing a straightforward computational framework for comparing multiple objects at the same time and providing extended formulas for as many similarity metrics as possible. In the binary case (i.e. when comparing two molecules pairwise) these are naturally reduced to their well-known formulas. We provide a detailed analysis on the effects of various parameters on the similarity values calculated by the extended formulas. The extended similarity indices are entirely general and do not depend on the fingerprints used. Two types of variance analysis (ANOVA) help to understand the main features of the indices: (i) ANOVA of mean similarity indices; (ii) ANOVA of sum of ranking differences (SRD). Practical aspects and applications of the extended similarity indices are detailed in the accompanying paper: Miranda-Quintana et al. J Cheminform. 2021. https://doi.org/10.1186/s13321-021-00504-4 . Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons .

Discovery of selective fragment-sized immunoproteasome inhibitors.

Proteasomes contribute to maintaining protein homeostasis and their inhibition is beneficial in certain types of cancer and in autoimmune diseases. However, the inhibition of the proteasomes in healthy cells leads to unwanted side-effects and significant effort has been made to identify inhibitors specific for the immunoproteasome, especially to treat diseases which manifest increased levels and activity of this proteasome isoform. Here, we report our efforts to discover fragment-sized inhibitors of the human immunoproteasome. The screening of an in-house library of structurally diverse fragments resulted in the identification of benzo[d]oxazole-2(3H)-thiones, benzo[d]thiazole-2(3H)-thiones, benzo[d]imidazole-2(3H)-thiones, and 1-methylbenzo[d]imidazole-2(3H)-thiones (with a general term benzoXazole-2(3H)-thiones) as inhibitors of the chymotrypsin-like (ß5i) subunit of the immunoproteasome. A subsequent structure-activity relationship study provided us with an insight regarding growing vectors. Binding to the ß5i subunit was shown and selectivity against the ß5 subunit of the constitutive proteasome was determined. Thorough characterization of these compounds suggested that they inhibit the immunoproteasome by forming a disulfide bond with the Cys48 available specifically in the ß5i active site. To obtain fragments with biologically more tractable covalent interactions, we performed a warhead scan, which yielded benzoXazole-2-carbonitriles as promising starting points for the development of selective immunoproteasome inhibitors with non-peptidic scaffolds.