Discovery of a novel inhibitor of macropinocytosis with antiviral activity.

Several viruses hijack various forms of endocytosis in order to infect host cells. Here, we report the discovery of a molecule with antiviral properties that we named virapinib, which limits viral entry by macropinocytosis. The identification of virapinib derives from a chemical screen using High-Throughput Microscopy, where we identified chemical entities capable of preventing infection with a pseudotype virus expressing the spike (S) protein from SARS-CoV-2. Subsequent experiments confirmed the capacity of virapinib to inhibit infection by SARS-CoV-2, as well as by additional viruses, such as Monkeypox virus and TBEV. Mechanistic analyses revealed that the compound inhibited macropinocytosis, limiting this entry route for the viruses. Importantly, virapinib has no significant toxicity to host cells. In summary, we present the discovery of a molecule that inhibits macropinocytosis, thereby limiting the infectivity of viruses that use this entry route such as SARS-CoV2.

A phenomics approach for antiviral drug discovery.

BACKGROUND: The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections. RESULTS: We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. CONCLUSIONS: The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

Proteochemometric model for predicting the inhibition of penicillin-binding proteins.

Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing ß-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic ß-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability (R2=0.91, Q2=0.77, QExt2=0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.

Illuminating the origins of spectral properties of green fluorescent proteins via proteochemometric and molecular modeling.

Green fluorescent protein (GFP) has immense utility in biomedical imaging owing to its autofluorescent nature. In efforts to broaden the spectral diversity of GFP, there have been several reports of engineered mutants via rational design and random mutagenesis. Understanding the origins of spectral properties of GFP could be achieved by means of investigating its structure-activity relationship. The first quantitative structure-property relationship study for modeling the spectral properties, particularly the excitation and emission maximas, of GFP was previously proposed by us some years ago in which quantum chemical descriptors were used for model development. However, such simplified model does not consider possible effects that neighboring amino acids have on the conjugated π-system of GFP chromophore. This study describes the development of a unified proteochemometric model in which the GFP chromophore and amino acids in its vicinity are both considered in the same model. The predictive performance of the model was verified by internal and external validation as well as Y-scrambling. Our strategy provides a general solution for elucidating the contribution that specific ligand and protein descriptors have on the investigated spectral property, which may be useful in engineering novel GFP variants with desired characteristics.

Design and evaluation of substrate-based octapeptide and non substrate-based tetrapeptide inhibitors of dengue virus NS2B-NS3 proteases.

A series of 45 peptide inhibitors was designed, synthesized, and evaluated against the NS2B-NS3 proteases of the four subtypes of dengue virus, DEN-1-4. The design was based on proteochemometric models for Michaelis (Km) and cleavage rate constants (kcat) of protease substrates. This led first to octapeptides showing submicromolar or low micromolar inhibitory activities on the four proteases. Stepwise removal of cationic substrate non-prime side residues and variations in the prime side sequence resulted finally in an uncharged tetrapeptide, WYCW-NH2, with inhibitory Ki values of 4.2, 4.8, 24.4, and 11.2 µM for the DEN-1-4 proteases, respectively. Analysis of the inhibition data by proteochemometric modeling suggested the possibility for different binding poses of the shortened peptides compared to the octapeptides, which was supported by results of docking of WYCW-NH2 into the X-ray structure of DEN-3 protease.

Services for prediction of drug susceptibility for HIV proteases and reverse transcriptases at the HIV drug research centre.

SUMMARY: The HIV Drug Research Centre (HIVDRC) has established Web services for prediction of drug susceptibility for HIV proteases and reverse transcriptases. The services are based on two proteochemometric models which accepts a protease or reverse transcriptase sequence in amino acid form, and outputs the predicted drug susceptibility values. The predictions are based on a comprehensive analysis where all the relevant inhibitors are included, resulting in models with excellent predictive capabilities. AVAILABILITY AND IMPLEMENTATION: The services are implemented as interoperable Web services (REST and XMPP), with supporting web pages to allow for individual analyses. A set of plugins were also developed which make the services available from the Bioclipse workbench for life science. Services are available at http://www.hivdrc.org/services.

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects.

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes in the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques.

BACKGROUND: Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity. RESULTS: We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (Kd). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P2 = 0.67-0.73; for new kinases it ranged P2kin = 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P2 = 0.47, P2kin = 0.42 and AUC = 0.83. CONCLUSIONS: Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.

Proteochemometric modeling of HIV protease susceptibility.

BACKGROUND: A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations. RESULTS: The model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72. CONCLUSION: Our results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.

Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases.

The prime side specificity of dengue protease substrates was investigated by use of proteochemometrics, a technology for drug target interaction analysis. A set of 48 internally quenched peptides were designed using statistical molecular design (SMD) and assayed with proteases of four subtypes of dengue virus (DEN-1-4) for Michaelis (K(m)) and cleavage rate constants (k(cat)). The data were subjected to proteochemometrics modeling, concomitantly modeling all peptides on all the four dengue proteases, which yielded highly predictive models for both activities. Detailed analysis of the models then showed that considerably differing physico-chemical properties of amino acids contribute independently to the K(m) and k(cat) activities. For k(cat), only P1' and P2' prime side residues were important, while for K(m) all four prime side residues, P1'-P4', were important. The models could be used to identify amino acids for each P' substrate position that are favorable for, respectively, high substrate affinity and cleavage rate.

A confidence predictor for logD using conformal regression and a support-vector machine.

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Origin of aromatase inhibitory activity via proteochemometric modeling.

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure-activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.

A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms.

A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.

Identification and analysis of functionally important amino acids in human purinergic 12 receptor using a Saccharomyces cerevisiae expression system.

The purinergic 12 receptor (P2Y12) is a major drug target for anticoagulant therapies, but little is known about the regions involved in ligand binding and activation of this receptor. We generated four randomized P2Y12 libraries and investigated their ligand binding characteristics. P2Y12 was expressed in a Saccharomyces cerevisiae model system. Four libraries were generated with randomized amino acids at positions 181, 256, 265 and 280. Mutant variants were screened for functional activity in yeast using the natural P2Y12 ligand ADP. Activation results were investigated using quantitative structure-activity relationship (QSAR) models and ligand-receptor docking. We screened four positions in P2Y12 for functional activity by substitution with amino acids with diverse physiochemical properties. This analysis revealed that positions E181, R256 and R265 alter the functional activity of P2Y12 in a specific manner. QSAR models for E181 and R256 mutant libraries strongly supported the experimental data. All substitutions of amino acid K280 were completely inactive, highlighting the crucial role of this residue in P2Y12 function. Ligand-receptor docking revealed that K280 is likely to be a key element in the ligand-binding pocket of P2Y12. The results of this study demonstrate that positions 181, 256, 265 and 280 of P2Y12 are important for the functional integrity of the receptor. Moreover, K280 appears to be a crucial feature of the P2Y12 ligand-binding pocket. These results are important for rational design of novel antiplatelet agents.

Enzymatic analysis of recombinant Japanese encephalitis virus NS2B(H)-NS3pro protease with fluorogenic model peptide substrates.

BACKGROUND: Japanese encephalitis virus (JEV), a member of the Flaviviridae family, causes around 68,000 encephalitis cases annually, of which 20-30% are fatal, while 30-50% of the recovered cases develop severe neurological sequelae. Specific antivirals for JEV would be of great importance, particularly in those cases where the infection has become persistent. Being indispensable for flaviviral replication, the NS2B-NS3 protease is a promising target for design of anti-flaviviral inhibitors. Contrary to related flaviviral proteases, the JEV NS2B-NS3 protease is structurally and mechanistically much less characterized. Here we aimed at establishing a straightforward procedure for cloning, expression, purification and biochemical characterization of JEV NS2B(H)-NS3pro protease. METHODOLOGY/PRINCIPAL FINDINGS: The full-length sequence of JEV NS2B-NS3 genotype III strain JaOArS 982 was obtained as a synthetic gene. The sequence of NS2B(H)-NS3pro was generated by splicing by overlap extension PCR (SOE-PCR) and cloned into the pTrcHisA vector. Hexahistidine-tagged NS2B(H)-NS3pro, expressed in E. coli as soluble protein, was purified to >95% purity by a single-step immobilized metal affinity chromatography. SDS-PAGE and immunoblotting of the purified enzyme demonstrated NS2B(H)-NS3pro precursor and its autocleavage products, NS3pro and NS2B(H), as 36, 21, and 10 kDa bands, respectively. Kinetic parameters, K(m) and k(cat), for fluorogenic protease model substrates, Boc-GRR-amc, Boc-LRR-amc, Ac-nKRR-amc, Bz-nKRR-amc, Pyr-RTKR-amc and Abz-(R)(4)SAG-nY-amide, were obtained using inner filter effect correction. The highest catalytic efficiency k(cat)/K(m) was found for Pyr-RTKR-amc (k(cat)/K(m): 1962.96 ± 85.0 M(-1) s(-1)) and the lowest for Boc-LRR-amc (k(cat)/K(m): 3.74±0.3 M(-1) s(-1)). JEV NS3pro is inhibited by aprotinin but to a lesser extent than DEN and WNV NS3pro. CONCLUSIONS/SIGNIFICANCE: A simplified procedure for the cloning, overexpression and purification of the NS2B(H)-NS3pro was established which is generally applicable to other flaviviral proteases. Kinetic parameters obtained for a number of model substrates and inhibitors, are useful for the characterization of substrate specificity and eventually for the design of high-throughput assays aimed at antiviral inhibitor discovery.

Linking the Resource Description Framework to cheminformatics and proteochemometrics.

BACKGROUND: Semantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but have not found widespread use yet. The semantic web technology Resource Description Framework (RDF) and related methods show to be sufficiently versatile to change that situation. RESULTS: The work presented here focuses on linking RDF approaches to existing molecular chemometrics fields, including cheminformatics, QSAR modeling and proteochemometrics. Applications are presented that link RDF technologies to methods from statistics and cheminformatics, including data aggregation, visualization, chemical identification, and property prediction. They demonstrate how this can be done using various existing RDF standards and cheminformatics libraries. For example, we show how IC50 and Ki values are modeled for a number of biological targets using data from the ChEMBL database. CONCLUSIONS: We have shown that existing RDF standards can suitably be integrated into existing molecular chemometrics methods. Platforms that unite these technologies, like Bioclipse, makes this even simpler and more transparent. Being able to create and share workflows that integrate data aggregation and analysis (visual and statistical) is beneficial to interoperability and reproducibility. The current work shows that RDF approaches are sufficiently powerful to support molecular chemometrics workflows.

Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors.

BACKGROUND: Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens. METHODOLOGY/PRINCIPAL FINDINGS: We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2=0.89 and Q2ext=0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant. CONCLUSIONS/SIGNIFICANCE: Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.

Towards Proteome-Wide Interaction Models Using the Proteochemometrics Approach.

A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pKi ) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80 % and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes.

Proteochemometric modeling of drug resistance over the mutational space for multiple HIV protease variants and multiple protease inhibitors.

The main therapeutic targets in HIV are its protease and reverse transcriptase. A major problem in treatment of HIV is the ability of the virus to develop drug resistance by accumulating mutations in its targets. Acquiring detailed understanding of the molecular mechanisms for the interactions of drugs with mutated variants of the HIV virus is mandatory to be able to design inhibitors that can evade the resistance. Here we have used proteochemometric modeling to simultaneously analyze the interactions of 21 protease inhibitors with 72 unique protease variants. Inhibition data (pK(i)) were correlated to descriptions of chemical and structural properties of the inhibitors and proteases. The proteochemometric model obtained showed excellent fit and predictive ability (R(2)=0.92, Q(2)=0.83, Q(2)(inh)=0.78) and provided quantitative assessments for the contribution of each mutation and their combinations to the decrease in inhibitor activity, both for the whole compounds series as well as for individual compounds. The model revealed the most deleterious mutations in the protease to be D30N, V32I, G48V, I50V, I54V, V82A, I84V, and L90M. The model was further used to identify molecular properties of chemical compounds that are important for their inhibition of multimutated protease variants. Our results give directions how to design novel improved inhibitors.

Reductive amination products containing naphthalene and indole moieties bind to melanocortin receptors.

Presumed pharmacophoric groups of melanocortin peptides (naphthalene, amino or guanidine, and indole moieties) were combined in mimetics molecules looking for their favorable location for activity at melanocortin (MC) receptors. Twenty-two compounds were prepared and tested. The best of these displayed micromolar affinities for the MC receptors.