Strigolactones as Broad-Spectrum Antivirals against ß-Coronaviruses through Targeting the Main Protease Mpro.

The current SARS-CoV-2 pandemic and the likelihood that new coronavirus strains will emerge in the immediate future point out the urgent need to identify new pan-coronavirus inhibitors. Strigolactones (SLs) are a class of plant hormones with multifaceted activities whose roles in plant-related fields have been extensively explored. Recently, we proved that SLs also exert antiviral activity toward herpesviruses, such as human cytomegalovirus (HCMV). Here we show that the synthetic SLs TH-EGO and EDOT-EGO impair ß-coronavirus replication including SARS-CoV-2 and the common cold human coronavirus HCoV-OC43. Interestingly, in silico simulations suggest the binding of SLs in the SARS-CoV-2 main protease (Mpro) active site, and this was further confirmed by an in vitro activity assay. Overall, our results highlight the potential efficacy of SLs as broad-spectrum antivirals against ß-coronaviruses, which may provide the rationale for repurposing this class of hormones for the treatment of COVID-19 patients.

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery.

INTRODUCTION: Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experimental methods is likely to change the perspectives of protein-protein modulator research. Noteworthy, some novel low molecular weight (LMW) and short peptide modulators of PPIs are already in clinical trials for the treatment of relevant diseases. AREAS COVERED: This review focuses on the main molecular properties of protein-protein interfaces and on key concepts pertaining to the modulation of PPIs. The authors survey recently reported state-of-the-art methods dealing with the rational design of PPI modulators and highlight the role of several computer-based approaches. EXPERT OPINION: Interfering specifically with large protein interfaces is still an open challenge. The initial concerns about the unfavorable physicochemical properties of many of these modulators are nowadays less acute with several molecules lying beyond the rule of 5, orally available and successful in clinical trials. As the cost of biologics interfering with PPIs is very high, it would seem reasonable to put more effort, both in academia and the private sectors, on actively developing novel low molecular weight compounds and short peptides to perform this task.

ELIOT: A platform to navigate the E3 pocketome and aid the design of new PROTACs.

Proteolysis-targeting chimeras (PROTACs) are novel therapeutics for the treatment of human disease. They exploit the enormous potential of the E3 ligases, a class of proteins that mark a target protein for degradation via the ubiquitin-proteasome system. Despite the existence of several E3 ligase-related databases, the choice of the functioning ligase is limited to only 1.6% of those available, probably due to the fragmentary understanding of their structures and their known ligands; in fact, none of the existing databases report detailed studies covering their 3D structure or their pockets. Here, we report ELIOT (E3 LIgase pocketOme navigaTor), an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were characterized with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity. All data are available at https://eliot.moldiscovery.com.

Exploiting ELIOT for Scaffold-Repurposing Opportunities: TRIM33 a Possible Novel E3 Ligase to Expand the Toolbox for PROTAC Design.

The field of targeted protein degradation, through the control of the ubiquitin-proteasome system (UPS), is progressing considerably; to exploit this new therapeutic modality, the proteolysis targeting chimera (PROTAC) technology was born. The opportunity to use PROTACs engaging of new E3 ligases that can hijack and control the UPS system could greatly extend the applicability of degrading molecules. To this end, here we show a potential application of the ELIOT (E3 LIgase pocketOme navigaTor) platform, previously published by this group, for a scaffold-repurposing strategy to identify new ligands for a novel E3 ligase, such as TRIM33. Starting from ELIOT, a case study of the cross-relationship using GRID Molecular Interaction Field (MIF) similarities between TRIM24 and TRIM33 binding sites was selected. Based on the assumption that similar pockets could bind similar ligands and considering that TRIM24 has 12 known co-crystalised ligands, we applied a scaffold-repurposing strategy for the identification of TRIM33 ligands exploiting the scaffold of TRIM24 ligands. We performed a deeper computational analysis to identify pocket similarities and differences, followed by docking and water analysis; selected ligands were synthesised and subsequently tested against TRIM33 via HTRF binding assay, and we obtained the first-ever X-ray crystallographic complexes of TRIM33α with three of the selected compounds.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening.

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

Exploring Ligand Binding Domain Dynamics in the NRs Superfamily.

Nuclear receptors (NRs) are transcription factors that play an important role in multiple diseases, such as cancer, inflammation, and metabolic disorders. They share a common structural organization composed of five domains, of which the ligand-binding domain (LBD) can adopt different conformations in response to substrate, agonist, and antagonist binding, leading to distinct transcription effects. A key feature of NRs is, indeed, their intrinsic dynamics that make them a challenging target in drug discovery. This work aims to provide a meaningful investigation of NR structural variability to outline a dynamic profile for each of them. To do that, we propose a methodology based on the computation and comparison of protein cavities among the crystallographic structures of NR LBDs. First, pockets were detected with the FLAPsite algorithm and then an "all against all" approach was applied by comparing each pair of pockets within the same sub-family on the basis of their similarity score. The analysis concerned all the detectable cavities in NRs, with particular attention paid to the active site pockets. This approach can guide the investigation of NR intrinsic dynamics, the selection of reference structures to be used in drug design and the easy identification of alternative binding sites.

Discovery of a new class of triazole based inhibitors of acetyl transferase KAT2A.

We have recently developed a new synthetic methodology that provided both N-aryl-5-hydroxytriazoles and N-pyridine-4-alkyl triazoles. A selection of these products was carried through virtual screening towards targets that are contemporary and validated for drug discovery and development. This study determined a number of potential structure target dyads of which N-pyridinium-4-carboxylic-5-alkyl triazole displayed the highest score specificity towards KAT2A. Binding affinity tests of abovementioned triazole and related analogs towards KAT2A confirmed the predictions of the in-silico assay. Finally, we have run in vitro inhibition assays of selected triazoles towards KAT2A; the ensemble of binding and inhibition assays delivered pyridyl-triazoles carboxylates as the prototype of a new class of inhibitors of KAT2A.

CROMATIC: Cross-Relationship Map of Cavities from Coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19 disease, has rapidly imposed an urgent need to identify effective drug candidates. In this context, the high resolution and non-redundant beta-Coronavirus protein cavities database is pivotal to help virtual screening protocols. Furthermore, the cross-relationship among cavities can lead to highlighting multitarget therapy chances. Here, we first collect all protein cavities on SARS-CoV-2, SARS-CoV, and MERS-CoV X-ray structures, and then, we compute a similarity map by using molecular interaction fields (MIFs). All the results come together in CROMATIC (CROss-relationship MAp of CaviTIes from Coronaviruses). CROMATIC encloses both a comprehensive and a non-redundant version of the cavities collection and a similarity map revealing, on the one hand, cavities that are conserved among the three Coronaviruses and, on the other hand, unexpected similarities among cavities that can represent a key starting point for multitarget therapy strategies. Similarity analysis was also performed for the available structures of SARS-CoV-2 spike variants, linking sequence mutations to three-dimensional interaction alterations. The CROMATIC repository is freely available to the scientific community at https://github.com/moldiscovery/sars-cromatic.

Discovery of novel SARS-CoV-2 inhibitors targeting the main protease Mpro by virtual screenings and hit optimization.

Two years after its emergence, SARS-CoV-2 still represents a serious and global threat to human health. Antiviral drug development usually takes a long time and, to increase the chances of success, chemical variability of hit compounds represents a valuable source for the discovery of new antivirals. In this work, we applied a platform of variably oriented virtual screening campaigns to seek for novel chemical scaffolds for SARS-CoV-2 main protease (Mpro) inhibitors. The study on the resulting 30 best hits led to the identification of a series of structurally unrelated Mpro inhibitors. Some of them exhibited antiviral activity in the low micromolar range against SARS-CoV-2 and other human coronaviruses (HCoVs) in different cell lines. Time-of-addition experiments demonstrated an antiviral effect during the viral replication cycle at a time frame consistent with the inhibition of SARS-CoV-2 Mpro activity. As a proof-of-concept, to validate the pharmaceutical potential of the selected hits against SARS-CoV-2, we rationally optimized one of the hit compounds and obtained two potent SARS-CoV-2 inhibitors with increased activity against Mpro both in vitro and in a cellular context, as well as against SARS-CoV-2 replication in infected cells. This study significantly contributes to the expansion of the chemical variability of SARS-CoV-2 Mpro inhibitors and provides new scaffolds to be exploited for pan-coronavirus antiviral drug development.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions.

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

Indomethacin-based PROTACs as pan-coronavirus antiviral agents.

Indomethacin (INM), a well-known non-steroidal anti-inflammatory drug, has recently gained attention for its antiviral activity demonstrated in drug repurposing studies against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although the mechanism of action of INM is not yet fully understood, recent studies have indicated that it acts at an early stage of the coronaviruses (CoVs) replication cycle. In addition, a proteomic study reported that the anti-SARS-CoV-2 activity of INM could be also ascribed to its ability to inhibit human prostaglandin E synthase type 2 (PGES-2), a host protein which interacts with the SARS-CoV-2 NSP7 protein. Although INM does not potently inhibit SARS-CoV-2 replication in infected Vero E6 cells, here we have explored for the first time the application of the Proteolysis Targeting Chimeras (PROTACs) technology in order to develop more potent INM-derived PROTACs with anti-CoV activity. In this study, we report the design, synthesis, and biological evaluation of a series of INM-based PROTACs endowed with antiviral activity against a panel of human CoVs, including different SARS-CoV-2 strains. Two PROTACs showed a strong improvement in antiviral potency compared to INM. Molecular modelling studies support human PGES-2 as a potential target of INM-based antiviral PROTACs, thus paving the way toward the development of host-directed anti-CoVs strategies. To the best of our knowledge, these PROTACs represent the first-in-class INM-based PROTACs with antiviral activity and also the first example of the application of PROTACs to develop pan-coronavirus agents.

Automatic Identification of Lansoprazole Degradants under Stress Conditions by LC-HRMS with MassChemSite and WebChembase.

Stress testing is one of the most important parts of the drug development process, helping to foresee stability problems and to identify degradation products. One of the processes involving stress testing is represented by forced degradation studies, which can predict the impact of certain conditions of pH, moisture, heat, or other negative effects due to transportation or packaging issues on drug potency and purity, ensuring patient safety. Regulatory agencies have been working on a standardization of laboratory procedures since the past two decades. One of the results of those years of intensive research is the International Conference on Harmonization (ICH) guidelines, which clearly define which forced degradation studies should be performed on new drugs, which become a routine work in pharmaceutical laboratories. Since used techniques based on high-performance liquid chromatography coupled with high-resolution mass spectrometry have been developed years ago and are now mastered by pharmaceutical scientists, automation of data analysis, and thus data processing, is becoming a hot topic nowadays. In this work, we present MassChemSite and WebChembase as a tandem to automatize the routine analysis studies without missing information quality, using as a case study the degradation of lansoprazole under acidic, oxidative, basic, and neutral stress conditions.

Bisphenol A binding promiscuity: A virtual journey through the universe of proteins.

Significant efforts are currently being made to move toxicity testing from animal experimentation to human relevant, mechanism-based approaches. In this context, the identification of molecular target(s) responsible for mechanisms of action is an essential step. Inspired by the recent concept of polypharmacology (the ability of drugs to interact with multiple targets) we argue that whole proteome virtual screening might become a breakthrough tool in toxicology reflecting the real complexity of chemical-biological interactions. Therefore, we investigated the value of performing ligand-protein binding prediction screening across the full proteome to identify new mechanisms of action for food chemicals. We applied the new approach to make a broader comparison between bisphenol A (BPA) (food-packaging chemical) and the endogenous estrogen, 17ß-estradiol (EST). Applying a novel high-throughput ligand-protein binding prediction tool (BioGPS) by the Amazon Web Services (AWS) cloud (to speed-up the calculation), we investigated the value of performing in silico screening across the full proteome (all human and rodent x-ray protein structures available in the Protein Data Bank). The strong correlation between in silico predictions and available in vitro data demonstrates the high predictive power of the method used. The most striking results obtained was that BPA was predicted to bind to many more proteins than the ones already known, most of which were common to EST. Our findings provide a new and unprecedented insight on the complexity of chemical-protein interactions, highlighting the binding promiscuity of BPA and its broader similarity compared to the female sex hormone, EST.

Detecting similar binding pockets to enable systems polypharmacology.

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case.

Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.

Exploring Basic Tail Modifications of Coumarin-Based Dual Acetylcholinesterase-Monoamine Oxidase B Inhibitors: Identification of Water-Soluble, Brain-Permeant Neuroprotective Multitarget Agents.

Aiming at modulating two key enzymatic targets for Alzheimer's disease (AD), i.e., acetylcholinesterase (AChE) and monoamine oxidase B (MAO B), a series of multitarget ligands was properly designed by linking the 3,4-dimethylcoumarin scaffold to 1,3- and 1,4-substituted piperidine moieties, thus modulating the basicity to improve the hydrophilic/lipophilic balance. After in vitro enzymatic inhibition assays, multipotent inhibitors showing potencies in the nanomolar and in the low micromolar range for hMAO B and eeAChE, respectively, were prioritized and evaluated in human SH-SY5Y cell-based models for their cytotoxicity and neuroprotective effect against oxidative toxins (H2O2, rotenone, and oligomycin-A). The present study led to the identification of a promising multitarget hit compound (5b) exhibiting high hMAO B inhibitory activity (IC50 = 30 nM) and good MAO B/A selectivity (selectivity index, SI = 94) along with a micromolar eeAChE inhibition (IC50 = 1.03 µM). Moreover, 5b behaves as a water-soluble, brain-permeant neuroprotective agent against oxidative insults without interacting with P-gp efflux system.

Challenging AQP4 druggability for NMO-IgG antibody binding using molecular dynamics and molecular interaction fields.

Neuromyelitis optica (NMO) is a multiple sclerosis-like immunopathology disease affecting optic nerves and the spinal cord. Its pathological hallmark is the deposition of a typical immunoglobulin, called NMO-IgG, against the water channel Aquaporin-4 (AQP4). Preventing NMO-IgG binding would represent a valuable molecular strategy for a focused NMO therapy. The recent observation that aspartate in position 69 (D69) is determinant for the formation of NMO-IgG epitopes prompted us to carry out intensive Molecular Dynamics (MD) studies on a number of single-point AQP4 mutants. Here, we report a domino effect originating from the point mutation at position 69: we find that the side chain of T62 is reoriented far from its expected position leaning on the lumen of the pore. More importantly, the strength of the H-bond interaction between L53 and T56, at the basis of the loop A, is substantially weakened. These events represent important pieces of a clear-cut mechanistic rationale behind the failure of the NMO-IgG binding, while the water channel function as well as the propensity to aggregate into OAPs remains unaltered. The molecular interaction fields (MIF)-based analysis of cavities complemented MD findings indicating a putative binding site comprising the same residues determining epitope reorganization. In this respect, docking studies unveiled an intriguing perspective to address the future design of small drug-like compounds against NMO. In agreement with recent experimental observations, the present study is the first computational attempt to elucidate NMO-IgG binding at the molecular level, as well as a first effort toward a less elusive AQP4 druggability.

BioGPS: navigating biological space to predict polypharmacology, off-targeting, and selectivity.

The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical "one target one drug" approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off-target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach.

BioGPS descriptors for rational engineering of enzyme promiscuity and structure based bioinformatic analysis.

A new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space). The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of "pre-organized environment" able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS "roadmap".

BioGPS: The Music for the Chemo- and Bioinformatics Walzer.

Identifying cross-relationships among protein binding sites is becoming increasingly important in the chemo- and bioinformatics field; indeed, protein structural similarity might provide the right answer to a number of questions including Is a drug repurposable for another target? What is the molecular mechanism of a drug side-effect? How can we improve the ligand selectivity? The comparison of protein binding sites in terms of their three-dimensional structure molecular interaction fields can be a useful technique to approach all of these problems. Here, we report a semi-automated method for comparing and clustering protein pockets, called BioGPS, that combines the GRID Molecular Interactions Fields (MIFs) with FLAP pharmacophoric fingerprints. BioGPS identifies and compares protein binding sites by aligning them each other and directly comparing their MIFs. The strengths of this approach are that it is MIF-based, and therefore describes molecular interactions from a ligand perspective, and it is independent of protein superposition or sequence alignment. This approach enables protein-protein virtual screening (drug repurposing, polypharmacology, off-target effects), and also clustering to relate sequence-based similarities to structure-based differences among protein binding sites.