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

VHL-Modified PROteolysis TArgeting Chimeras (PROTACs) as a Strategy to Evade Metabolic Degradation in In Vitro Applications

PROteolysis TArgeting Chimeras (PROTACs) are tripartite molecules consisting of a linker connecting a ligand for a protein of interest to an E3 ligase recruiter, whose rationale relies on proteasome-based protein degradation. PROTACs have expanded as a therapeutic strategy to open new avenues for unmet medical needs. Leveraging our expertise, we undertook a series of in vitro experiments aimed at elucidating PROTAC metabolism. In particular, we focused on PROTACs recruiting the von Hippel-Lindau (VHL) E3 ligase. After high-resolution mass spectrometry measurements, a characteristic metabolite with mass reduction of 200 units was detected and successively confirmed as a product deriving from the cleavage of the VHL ligand moiety. Subsequently, we identified hepatic and extrahepatic prolyl endopeptidases as the main putative metabolic enzymes involved. Finally, we designed and synthesized analogs of the VHL ligands that we further exploited for the synthesis of novel VHL-directed PROTACs with an improved metabolic stability in in vitro applications.

Strigolactones as Broad-Spectrum Antivirals against β-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 β-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 β-coronaviruses, which may provide the rationale for repurposing this class of hormones for the treatment of COVID-19 patients.

New bioisosteric sulphur-containing choline kinase inhibitors with a tracked mode of action

Since the identification of human choline kinase as a protein target against cancer progression, many compounds have been designed to inhibit its function and reduce the biosynthesis of phosphatidylcholine. Herein, we propose a series of bioisosteric inhibitors that are based on the introduction of sulphur and feature improved activity and lipophilic/hydrophilic balance. The evaluation of the inhibitory and of the antiproliferative properties of the PL (dithioethane) and FP (disulphide) libraries led to the identification of PL 48, PL 55 and PL 69 as the most active compounds of the series. Docking analysis using FLAP suggests that for hits to leads, binding mostly involves an interaction with the Mg²⁺ cofactor, or its destabilization. The most active compounds of the two series are capable of inducing apoptosis following the mitochondrial pathway and to significantly reduce the expression of anti-apoptotic proteins such as the Mcl-1. The fluorescence properties of the compounds of the PL library allowed the tracking of their mode of action, while PAINS (Pan Assays Interference Structures) filtration databases suggest the lack of any unspecific biological response.

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.

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.

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

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.

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 (M^pro) inhibitors. The study on the resulting 30 best hits led to the identification of a series of structurally unrelated M^pro 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 M^pro 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 M^pro 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 M^pro 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.

Apigenin enhances apoptosis induction by 5-fluorouracil through regulation of thymidylate synthase in colorectal cancer cells

Although effective drugs have been developed, including 5-fluorouracil (5-FU), advanced colorectal cancer (CRC) shows low therapeutic sensitivity resulting from the development of 5-FU resistance. Thymidylate synthase (TS) is a target protein of 5-FU, and elevated TS lowers the 5-FU sensitivity of CRC cells. Here, we tested the efficacy of several candidate phytochemicals against human CRC-derived HCT116 cells expressing wild-type tumor suppressor protein P53 and HT29 cells expressing mutant P53. Among them, we found that apigenin enhanced the inhibitory effect of 5-FU on cell viability. In addition, apigenin inhibited the upregulation of TS induced by 5-FU. Apigenin also potentiated 5-FU-induced apoptosis of HCT116 cells and enhanced cell cycle disruption. Furthermore, apigenin increased reactive oxygen species production, intracellular and intramitochondrial Ca²⁺ concentrations, and mitochondrial membrane potential upon cotreatment with 5-FU. Knockdown of forkhead box protein M, a transcription factor modulating 5-FU sensitivity, enhanced the potentiation of apoptosis by apigenin in HCT116 cells. Moreover, apigenin suppressed TS expression and inhibited the viability of 5-FU-resistant HCT116 cells. Therefore, apigenin may improve the therapeutic efficacy of 5-FU against CRC by suppressing TS, but apoptosis induction is mainly dependent on functional P53.

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Apigenin inhibits the upregulation of TS induced by 5-FU for apoptosis of CRC.

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FOXM1 silencing enhances the potentiation of apoptosis by apigenin.

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Suppressing TS and promoting P53 activity by apigenin reduce acquired 5-FU resistance.

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.

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.

Traditionally, the fact that most drugs are promiscuous binders has been a major concern in pharmacology, due to the occurrence of undesired off-target clinical events. In the recent years, however, the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature, since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks. One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites. We have massively done so for all human proteins with a known high-resolution three-dimensional structure, unveiling a vast space of ‘polypharmacology’ opportunities. Of these, we know, a great majority is not of therapeutic interest. To pinpoint promising multi-target combinations, we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks.

Chroman-4-One Derivatives Targeting Pteridine Reductase 1 and Showing Anti-Parasitic Activity

Flavonoids have previously been identified as antiparasitic agents and pteridine reductase 1 (PTR1) inhibitors. Herein, we focus our attention on the chroman-4-one scaffold. Three chroman-4-one analogues (1–3) of previously published chromen-4-one derivatives were synthesized and biologically evaluated against parasitic enzymes (Trypanosoma brucei PTR1–TbPTR1 and Leishmania major–LmPTR1) and parasites (Trypanosoma brucei and Leishmania infantum). A crystal structure of TbPTR1 in complex with compound 1 and the first crystal structures of LmPTR1-flavanone complexes (compounds 1 and 3) were solved. The inhibitory activity of the chroman-4-one and chromen-4-one derivatives was explained by comparison of observed and predicted binding modes of the compounds. Compound 1 showed activity both against the targeted enzymes and the parasites with a selectivity index greater than 7 and a low toxicity. Our results provide a basis for further scaffold optimization and structure-based drug design aimed at the identification of potent anti-trypanosomatidic compounds targeting multiple PTR1 variants.

Design of efficient computational workflows for in silico drug repurposing

Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.