Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations.

BACKGROUND: While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. RESULTS: In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. CONCLUSION: With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.

Identification of pan-kinase-family inhibitors using graph convolutional networks to reveal family-sensitive pre-moieties.

BACKGROUND: Human protein kinases, the key players in phosphoryl signal transduction, have been actively investigated as drug targets for complex diseases such as cancer, immune disorders, and Alzheimer's disease, with more than 60 successful drugs developed in the past 30 years. However, many of these single-kinase inhibitors show low efficacy and drug resistance has become an issue. Owing to the occurrence of highly conserved catalytic sites and shared signaling pathways within a kinase family, multi-target kinase inhibitors have attracted attention. RESULTS: To design and identify such pan-kinase family inhibitors (PKFIs), we proposed PKFI sets for eight families using 200,000 experimental bioactivity data points and applied a graph convolutional network (GCN) to build classification models. Furthermore, we identified and extracted family-sensitive (only present in a family) pre-moieties (parts of complete moieties) by utilizing a visualized explanation (i.e., where the model focuses on each input) method for deep learning, gradient-weighted class activation mapping (Grad-CAM). CONCLUSIONS: This study is the first to propose the PKFI sets, and our results point out and validate the power of GCN models in understanding the pre-moieties of PKFIs within and across different kinase families. Moreover, we highlight the discoverability of family-sensitive pre-moieties in PKFI identification and drug design.

Discovery of moiety preference by Shapley value in protein kinase family using random forest models.

BACKGROUND: Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). RESULTS: In this study, we used "merged moiety-based interpretable features (MMIFs)," which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. CONCLUSIONS: In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.

Pharmacophore anchor models of flaviviral NS3 proteases lead to drug repurposing for DENV infection.

BACKGROUND: Viruses of the flaviviridae family are responsible for some of the major infectious viral diseases around the world and there is an urgent need for drug development for these diseases. Most of the virtual screening methods in flaviviral drug discovery suffer from a low hit rate, strain-specific efficacy differences, and susceptibility to resistance. It is because they often fail to capture the key pharmacological features of the target active site critical for protein function inhibition. So in our current work, for the flaviviral NS3 protease, we summarized the pharmacophore features at the protease active site as anchors (subsite-moiety interactions). RESULTS: For each of the four flaviviral NS3 proteases (i.e., HCV, DENV, WNV, and JEV), the anchors were obtained and summarized into 'Pharmacophore anchor (PA) models'. To capture the conserved pharmacophore anchors across these proteases, were merged the four PA models. We identified five consensus core anchors (CEH1, CH3, CH7, CV1, CV3) in all PA models, represented as the "Core pharmacophore anchor (CPA) model" and also identified specific anchors unique to the PA models. Our PA/CPA models complied with 89 known NS3 protease inhibitors. Furthermore, we proposed an integrated anchor-based screening method using the anchors from our models for discovering inhibitors. This method was applied on the DENV NS3 protease to screen FDA drugs discovering boceprevir, telaprevir and asunaprevir as promising anti-DENV candidates. Experimental testing against DV2-NGC virus by in-vitro plaque assays showed that asunaprevir and telaprevir inhibited viral replication with EC50 values of 10.4 µM & 24.5 µM respectively. The structure-anchor-activity relationships (SAAR) showed that our PA/CPA model anchors explained the observed in-vitro activities of the candidates. Also, we observed that the CEH1 anchor engagement was critical for the activities of telaprevir and asunaprevir while the extent of inhibitor anchor occupation guided their efficacies. CONCLUSION: These results validate our NS3 protease PA/CPA models, anchors and the integrated anchor-based screening method to be useful in inhibitor discovery and lead optimization, thus accelerating flaviviral drug discovery.

Case Report: Survival from Clinical Rabies in a Young Child from Maharashtra, India, 2022.

A 3.5-year-old male child from Maharashtra, India, presented with features of meningoencephalitis approximately 1 month after sustaining severe bite injuries on the right hand from a stray dog. He had received four doses of post-exposure intradermal rabies vaccination (on days 0, 3, and 7 of the bite and erroneously on day 20, instead of day 28 as recommended in the updated Thai Red Cross regimen) as well as local and systemic injections of equine rabies immune globulin. The child was initially diagnosed with and treated for acute encephalitis syndrome before rabies encephalitis was confirmed by detection of rabies virus neutralizing antibodies in the cerebrospinal fluid. During the emergent period, he also received the antimalarial drug artesunate, recently reported to have antiviral effects against rabies virus. With intensive and supportive care, the child showed substantial clinical improvement over the next few weeks. He has now survived for more than 10 months after disease onset, albeit with severe neurological sequelae including diffuse cerebral and cerebellar atrophy.

Furin and TMPRSS2 Resistant Spike Induces Robust Humoral and Cellular Immunity Against SARS-CoV-2 Lethal Infection.

An effective COVID-19 vaccine against broad SARS-CoV-2 variants is still an unmet need. In the study, the vesicular stomatitis virus (VSV)-based vector was used to express the SARS-CoV-2 Spike protein to identify better vaccine designs. The replication-competent of the recombinant VSV-spike virus with C-terminal 19 amino acid truncation (SΔ19 Rep) was generated. A single dose of SΔ19 Rep intranasal vaccination is sufficient to induce protective immunity against SARS-CoV-2 infection in hamsters. All the clones isolated from the SΔ19 Rep virus contained R682G mutation located at the Furin cleavage site. An additional S813Y mutation close to the TMPRSS2 cleavage site was identified in some clones. The enzymatic processing of S protein was blocked by these mutations. The vaccination of the R682G-S813Y virus produced a high antibody response against S protein and a robust S protein-specific CD8+ T cell response. The vaccinated animals were protected from the lethal SARS-CoV-2 (delta variant) challenge. The S antigen with resistance to enzymatic processes by Furin and TMPRSS2 will provide better immunogenicity for vaccine design.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 µM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

An integrated systematic approach for investigating microcurrent electrical nerve stimulation (MENS) efficacy in STZ-induced diabetes mellitus.

Diabetes mellitus (DM) is a major metabolic disorder and an increasing health problem worldwide. Effective non-invasive therapies for DM are still lacking. Here, we have developed Microcurrent electrical nerve stimulation (MENS), a non-invasive therapy, and tested on 46 mice clustered into five groups, such as control, STZ-induced DM, and MENS treatment groups. Experimental results show that MENS treatment is able to improve seven biochemical indexes (e.g., hemoglobin A1c and glucose level). To investigate the mechanisms of MENS treatment on STZ-induced DM, we selected six representative samples to perform microarray experiments for several groups and developed an integrated Hierarchical System Biology Model (HiSBiM) to analyze these omics data. The results indicate that MENS can affect fatty acid metabolism pathways, peroxisome proliferator-activated receptor (PPAR) signaling pathway and cell cycle. Additionally, the DM biochemical indexes and omics data profiles of MENS treatment were found to be consistent. We then compared the therapeutic effects of MENS with anti-diabetic compounds (e.g., quercetin, metformin, and rosiglitazone), using the HiSBiM four-level biological functions and processes of multiple omics data. The results show MENS and these anti-diabetic compounds have similar effect pathways highly correlated to the diabetes processes, such as the PPAR signaling pathway, bile secretion, and insulin signaling pathways. We believe that MENS is an effective and non-invasive therapy for DM and our HiSBiM is an useful method for investigating multiple omics data.

Pharmacophore anchor models of ATAT1 to discover potential inhibitors and lead optimization.

Post-translation modification of microtubules is associated with many diseases like cancer. Alpha Tubulin Acetyltransferase 1 (ATAT1) is a major enzyme that acetylates 'Lys-40' in alpha-tubulin on the luminal side of microtubules and is a drug target that lacks inhibitors. Here, we developed pharmacophore anchor models of ATAT1 which were constructed statistically using thousands of docked compounds, for drug design and investigating binding mechanisms. Our models infer the compound moiety preferences with the physico-chemical properties for the ATAT1 binding site. The results from the pharmacophore anchor models show the three main sub-pockets, including S1 acetyl site, S2 adenine site, and S3 diphosphate site with anchors, where conserved moieties interact with respective sub-pocket residues in each site and help in guiding inhibitor discovery. We validated these key anchors by analyzing 162 homologous protein sequences (>99 species) and over 10 structures with various bound ligands and mutations. Our results were consistent with previous works also providing new interesting insights. Our models applied in virtual screening predicted several ATAT1 potential inhibitors. We believe that our model is useful for future inhibitor discovery and for guiding lead optimization.

Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurposing.

The infectious SARS-CoV-2 causes COVID-19, which is now a global pandemic. Aiming for effective treatments, we focused on the key drug target, the viral 3C-like (3CL) protease. We modeled a big dataset with 42 SARS-CoV-2 3CL protease-ligand complex structures from ∼98.7% similar SARS-CoV 3CL protease with abundant complex structures. The diverse flexible active site conformations identified in the dataset were clustered into six protease pharmacophore clusters (PPCs). For the PPCs with distinct flexible protease active sites and diverse interaction environments, we identified pharmacophore anchor hotspots. A total of 11 "PPC consensus anchors" (a distinct set observed in each PPC) were observed, of which three "PPC core anchors" EHV2, HV1, and V3 are strongly conserved across PPCs. The six PPC cavities were then applied in virtual screening of 2122 FDA drugs for repurposing, using core anchor-derived "PPC scoring S" to yield seven drug candidates. Experimental testing by SARS-CoV-2 3CL protease inhibition assay and antiviral cytopathic effect assays discovered active hits, Boceprevir and Telaprevir (HCV drugs) and Nelfinavir (HIV drug). Specifically, Boceprevir showed strong protease inhibition with micromolar IC50 of 1.42 µM and an antiviral activity with EC50 of 49.89 µM, whereas Telaprevir showed moderate protease inhibition only with an IC50 of 11.47 µM. Nelfinavir solely showed antiviral activity with a micromolar EC50 value of 3.28 µM. Analysis of binding mechanisms of protease inhibitors revealed the role of PPC core anchors. Our PPCs revealed the flexible protease active site conformations, which successfully enabled drug repurposing.

Zika Virus NS3 Protease Pharmacophore Anchor Model and Drug Discovery.

Zika virus (ZIKV) of the flaviviridae family, is the cause of emerging infections characterized by fever, Guillain-Barré syndrome (GBS) in adults and microcephaly in newborns. There exists an urgent unmet clinical need for anti-ZIKV drugs for the treatment of infected individuals. In the current work, we aimed at the promising virus drug target, ZIKV NS3 protease and constructed a Pharmacophore Anchor (PA) model for the active site. The PA model reveals a total of 12 anchors (E, H, V) mapped across the active site subpockets. We further identified five of these anchors to be critical core anchors (CEH1, CH3, CH7, CV1, CV3) conserved across flaviviral proteases. The ZIKV protease PA model was then applied in anchor-enhanced virtual screening yielding 14 potential antiviral candidates, which were tested by in vitro assays. We discovered FDA drugs Asunaprevir and Simeprevir to have potent anti-ZIKV activities with EC50 values 4.7 µM and 0.4 µM, inhibiting the viral protease with IC50 values 6.0 µM and 2.6 µM respectively. Additionally, the PA model anchors aided in the exploration of inhibitor binding mechanisms. In conclusion, our PA model serves as a promising guide map for ZIKV protease targeted drug discovery and the identified 'previr' FDA drugs are promising for anti-ZIKV treatments.

Identification of neuraminidase inhibitors against dual H274Y/I222R mutant strains.

Influenza is an annual seasonal epidemic that has continually drawn public attentions, due to the potential death toll and drug resistance. Neuraminidase, which is essential for the spread of influenza virus, has been regarded as a valid target for the treatment of influenza infection. Although neuraminidase drugs have been developed, they are susceptible to drug-resistant mutations in the sialic-binding site. In this study, we established computational models (site-moiety maps) of H1N1 and H5N1 to determine properties of the 150-cavity, which is adjacent to the drug-binding site. The models reveal that hydrogen-bonding interactions with residues R118, D151, and R156 and van der Waals interactions with residues Q136, D151, and T439 are important for identifying 150-cavitiy inhibitors. Based on the models, we discovered three new inhibitors with IC50 values <10 µM that occupies both the 150-cavity and sialic sites. The experimental results identified inhibitors with similar activities against both wild-type and dual H274Y/I222R mutant neuraminidases and showed little cytotoxic effects. Furthermore, we identified three new inhibitors situated at the sialic-binding site with inhibitory effects for normal neuraminidase, but lowered effects for mutant strains. The results suggest that the new inhibitors can be used as a starting point to combat drug-resistant strains.