Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning

Kaempferol sophoroside glucoside mitigates acetaminophen-induced hepatotoxicity: Role of Nrf2/NF-κB and JNK/ASK-1 signaling pathways

APAP (Acetaminophen)-induced hepatic injury is a major public health threat that requires continuous searching for new effective therapeutics. KSG (Kaempferol-3-sophoroside-7-glucoside) is a kaempferol derivative that was separated from plant species belonging to different genera. This study explored the protective effects of KSG on ALI (acute liver injury) caused by APAP overdose in mice and elucidated its possible mechanisms. The results showed that KSG pretreatment alleviated APAP-induced hepatic damage as it reduced hepatic pathological lesions as well as the serum parameters of liver injury. Moreover, KSG opposed APAP-associated oxidative stress and augmented hepatic antioxidants. KSG suppressed the inflammatory response as it decreased the genetic and protein expression as well as the levels of inflammatory cytokines. Meanwhile, KSG enhanced the mRNA expression and level of anti-inflammatory cytokine, IL-10 (interleukin-10). KSG repressed the activation of NF-κB (nuclear-factor kappa-B), besides it promoted the activation of Nrf2 signaling. Additionally, KSG markedly hindered the elevation of ASK-1 (apoptosis-signal regulating-kinase-1) and JNK (c-Jun-N-terminal kinase). Furthermore, KSG suppressed APAP-induced apoptosis as it decreased the level and expression of Bax (BCL2-associated X-protein), and caspase-3 concurrent with an enhancement of anti-apoptotic protein, Bcl2 in the liver. More thoroughly, Computational studies reveal indispensable binding affinities between KSG and Keap1 (Kelch-like ECH-associated protein-1), ASK1 (apoptosis signal-regulating kinase-1), and JNK1 (c-Jun N-terminal protein kinase-1) with distinctive tendencies for selective inhibition. Taken together, our data showed the hepatoprotective capacity of KSG against APAP-produced ALI via modulation of Nrf2/NF-κB and JNK/ASK-1/caspase-3 signaling. Henceforth, KSG could be a promising hepatoprotective candidate for ALI.

Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties

Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the "rule of five (RO5)". Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery

We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demonstrated by inhibition assays using a PPI target, Kelch-like ECH-associated protein 1/nuclear factor erythroid 2-related factor 2 (Keap1/Nrf2), and a deep neural network model based on the first-round assay data showed a highest hit rate of 27.3%. Using the models, we identified novel active and non-flat compounds far from public datasets, expanding the chemical space.

The Complex Genetic and Epigenetic Regulation of the Nrf2 Pathways: A Review

Nrf2 is a major transcription factor that significantly regulates-directly or indirectly-more than 2000 genes. While many of these genes are involved in maintaining redox balance, others are involved in maintaining balance among metabolic pathways that are seemingly unrelated to oxidative stress. In the past 25 years, the number of factors involved in the activation, nuclear translocation, and deactivation of Nrf2 has continued to expand. The purpose of this review is to provide an overview of the remarkable complexity of the tortuous sequence of stop-and-go signals that not only regulate expression or repression, but may also modify transcriptional intensity as well as the specificity of promoter recognition, allowing fluidity of its gene expression profile depending on the various structural modifications the transcription factor encounters on its journey to the DNA. At present, more than 45 control points have been identified, many of which represent sites of action of the so-called Nrf2 activators. The complexity of the pathway and the synergistic interplay among combinations of control points help to explain the potential advantages seen with phytochemical compositions that simultaneously target multiple control points, compared to the traditional pharmaceutical paradigm of "one-drug, one-target".

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

KEAP1-NRF2 protein-protein interaction inhibitors: Design, pharmacological properties and therapeutic potential

The transcription factor nuclear factor erythroid 2-related factor 2 (NRF2) is considered the master regulator of the phase II antioxidant response. It controls a plethora of cytoprotective genes related to oxidative stress, inflammation, and protein homeostasis, among other processes. Activation of these pathways has been described in numerous pathologies including cancer, cardiovascular, respiratory, renal, digestive, metabolic, autoimmune, and neurodegenerative diseases. Considering the increasing interest of discovering novel NRF2 activators due to its clinical application, initial efforts were devoted to the development of electrophilic drugs able to induce NRF2 nuclear accumulation by targeting its natural repressor protein Kelch-like ECH-associated protein 1 (KEAP1) through covalent modifications on cysteine residues. However, off-target effects of these drugs prompted the development of an innovative strategy, the search of KEAP1-NRF2 protein-protein interaction (PPI) inhibitors. These innovative activators are proposed to target NRF2 in a more selective way, leading to potentially improved drugs with the application for a variety of diseases that are currently under investigation. In this review, we summarize known KEAP1-NRF2 PPI inhibitors to date and the bases of their design highlighting the most important features of their respective interactions. We also discuss the preclinical pharmacological properties described for the most promising compounds.