Small-molecule Inhibitors Of Protein-protein Interactions Research Articles

Computational Repurposing of Potential Dimerization Inhibitors against SARS-CoV-2 Main Protease

Background: The screening, design, and synthesis of various dimerization inhibitors have been an active area of interest for structure-based drug design efforts. Functionally important dimers, such as human immunodeficiency virus (HIV) protease and surviving, are being targeted for such studies over time. Computational repurposing of potential drug candidates provides a cost and time-efficient way in the drug discovery life cycle. Objective: Concerning the current coronavirus disease (COVID-19) scenario, the functionally active dimer of SARS-CoV-2 (severe acute respiratory syndrome) main protease (Mpro) is used as a target to screen possible dimerization inhibitors. Methods: A database of small molecule protein-protein interaction inhibitors was screened for the study. This study used molecular docking, followed by molecular dynamics (MD) simulation and postsimulation binding energy predictions. Results: From the selected 183 compounds, a diazene-based compound and a salicylic-type compound were identified as possible dimerization inhibitors in this study. These two compounds formed stable complexes with the Mpro during the MD simulations. The complexes formed by these two compounds were also unable to form important salt bridge interactions required for the dimerization of the protomers. Conclusion: Experimental studies on both compounds were previously conducted as dimerization inhibitors in HIV. The data led to the possibility of exploring the identified compounds as dimerization inhibitors, which could be important for SARS-CoV-2 therapeutics.

Development of small molecule inhibitors of natural killer group 2D receptor (NKG2D)

Natural killer group 2D (NKG2D) is a homodimeric activating immunoreceptor whose function is to detect and eliminate compromised cells upon binding to the NKG2D ligands (NKG2DL) major histocompatibility complex (MHC) molecules class I-related chain A (MICA) and B (MICB) and UL16 binding proteins (ULBP1-6). While typically present at low levels in healthy cells and tissue, NKG2DL expression can be induced by viral infection, cellular stress or transformation. Aberrant activity along the NKG2D/NKG2DL axis has been associated with autoimmune diseases due to the increased expression of NKG2D ligands in human disease tissue, making NKG2D inhibitors an attractive target for immunomodulation. Herein we describe the discovery and optimization of small molecule PPI (protein–protein interaction) inhibitors of NKG2D/NKG2DL. Rapid SAR was guided by structure-based drug design and accomplished by iterative singleton and parallel medicinal chemistry synthesis. These efforts resulted in the identification of several potent analogs (14, 21, 30, 45) with functional activity and improved LLE.

Medicinal Chemistry Insights into the Development of Small-Molecule Kelch-Like ECH-Associated Protein 1-Nuclear Factor Erythroid 2-Related Factor 2 (Keap1-Nrf2) Protein-Protein Interaction Inhibitors.

Oxidative stress has been implicated in a wide range of pathological conditions. The transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) exerts a central role in regulating the cellular defense system against oxidative and electrophilic insults. Nonelectrophilic inhibition of the protein-protein interaction (PPI) between Kelch-like ECH-associated protein 1 (Keap1) and Nrf2 has become a promising approach to activate Nrf2. Recently, multiple drug discovery strategies have facilitated the development of small-molecule Keap1-Nrf2 PPI inhibitors with potent activity and favorable drug-like properties. In this Perspective, we summarize the latest progress of small-molecule Keap1-Nrf2 PPI inhibitors from medicinal chemistry insights and discuss future prospects and challenges in this field.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces.

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

Development of Noncovalent Small-Molecule Keap1-Nrf2 Inhibitors by Fragment-Based Drug Discovery.

Targeting the protein-protein interaction (PPI) between the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) and its repressor, Kelch-like ECH-associated protein 1 (Keap1), constitutes a promising strategy for treating diseases involving oxidative stress and inflammation. Here, a fragment-based drug discovery (FBDD) campaign resulted in novel, high-affinity (Ki = 280 nM), and cell-active noncovalent small-molecule Keap1-Nrf2 PPI inhibitors. We screened 2500 fragments using orthogonal assays─fluorescence polarization (FP), thermal shift assay (TSA), and surface plasmon resonance (SPR)─and validated the hits by saturation transfer difference (STD) NMR, leading to 28 high-priority hits. Thirteen co-structures showed fragments binding mainly in the P4 and P5 subpockets of Keap1's Kelch domain, and three fluorenone-based fragments featuring a novel binding mode were optimized by structure-based drug discovery. We thereby disclose several fragment hits, including their binding modes, and show how FBDD can be performed to find new small-molecule Keap1-Nrf2 PPI inhibitors.

Interrupting specific hydrogen bonds between ELF3 and MED23 as an alternative drug resistance-free strategy for HER2-overexpressing cancers

IntroductionHER2 overexpression induces cancer aggression and frequent recurrences in many solid tumors. Because HER2 overproduction is generally followed by gene amplification, inhibition of protein–protein interaction (PPI) between transcriptional factor ELF3 and its coactivator MED23 has been considered an effective but challenging strategy. ObjectivesThis study aimed to determine the hotspot of ELF3-MED23 PPI and further specify the essential residues and their key interactions in the hotspot which are controllable by small molecules with significant anticancer activity. MethodsIntensive biological evaluation methods including SEAP, fluorescence polarization, LC-MS/MS-based quantitative, biosensor, GST-pull down assays, and in silico structural analysis were performed to determine hotspot of ELF3-MED23 PPI and to elicit YK1, a novel small molecule PPI inhibitor. The effects of YK1 on possible PPIs of MED23 and the efficacy of trastuzumab were assessed using cell culture and tumor xenograft mouse models. ResultsELF3-MED23 PPI was found to be specifically dependent on H-bondings between D400, H449 of MED23 and W138, I140 of ELF3 for upregulating HER2 gene transcription. Employing YK1, we confirmed that interruption on these H-bondings significantly attenuated the HER2-mediated oncogenic signaling cascades and exhibited significant in vitro and in vivo anticancer activity against HER2-overexpressing breast and gastric cancers even in their trastuzumab refractory clones. ConclusionOur approach to develop specific ELF3-MED23 PPI inhibitor without interfering other PPIs of MED23 can finally lead to successful development of a drug resistance-free compound to interrogate HER2 biology in diverse conditions of cancers overexpressing HER2.

Fragment hopping protocol for the design of small-molecule protein–protein interaction inhibitors

Fragment-based ligand discovery (FBLD) is one of the most successful approaches to designing small-molecule protein–protein interaction (PPI) inhibitors. The incorporation of computational tools to FBLD allows the exploration of chemical space in a time- and cost-efficient manner. Herein, a computational protocol for the development of small-molecule PPI inhibitors using fragment hopping, a fragment-based de novo design approach, is described and a case study is presented to illustrate the efficiency of this protocol. Fragment hopping facilitates the design of PPI inhibitors from scratch solely based on key binding features in the PPI complex structure. This approach is an open system that enables the inclusion of different state-of-the-art programs and softwares to improve its performances.

Development of a First-in-Class Small-Molecule Inhibitor of the C-Terminal Hsp90 Dimerization.

Heat shock proteins 90 (Hsp90) are promising therapeutic targets due to their involvement in stabilizing several aberrantly expressed oncoproteins. In cancerous cells, Hsp90 expression is elevated, thereby exerting antiapoptotic effects, which is essential for the malignant transformation and tumor progression. Most of the Hsp90 inhibitors (Hsp90i) under investigation target the ATP binding site in the N-terminal domain of Hsp90. However, adverse effects, including induction of the prosurvival resistance mechanism (heat shock response or HSR) and associated dose-limiting toxicity, have so far precluded their clinical approval. In contrast, modulators that interfere with the C-terminal domain (CTD) of Hsp90 do not inflict HSR. Since the CTD dimerization of Hsp90 is essential for its chaperone activity, interfering with the dimerization process by small-molecule protein–protein interaction inhibitors is a promising strategy for anticancer drug research. We have developed a first-in-class small-molecule inhibitor (5b) targeting the Hsp90 CTD dimerization interface, based on a tripyrimidonamide scaffold through structure-based molecular design, chemical synthesis, binding mode model prediction, assessment of the biochemical affinity, and efficacy against therapy-resistant leukemia cells. 5b reduces xenotransplantation of leukemia cells in zebrafish models and induces apoptosis in BCR-ABL1+ (T315I) tyrosine kinase inhibitor-resistant leukemia cells, without inducing HSR.

Developing Small-Molecule Inhibitors of Protein-Protein Interactions Involved in Viral Entry as Potential Antivirals for COVID-19

Blocking protein-protein interactions (PPIs) involved in the initiation of the cell attachment and entry of viruses is an important antiviral mechanism of action including for neutralizing antibodies. Doing it with small-molecule inhibitors (SMIs) is challenging, as it is for all other PPIs, and might require the exploration of chemical space beyond that of typical drug-like structures. However, it could lead to new antiviral agents suitable for oral administration and acting on alternative targets, considerations that are essential for the development of widely acceptable and broad-spectrum preventive or curative therapeutics. Fostemsavir, an antiretroviral that acts via blocking of the gp120–CD4 PPI, supports the feasibility of the concept. Here, a brief review of relevant drug design considerations is presented together with a summary of the progress made toward the identification of SMIs targeting the PPI between the SARS-CoV-2 spike protein and ACE2 that initiates the viral attachment and cellular entry of this coronavirus causing the COVID-19 pandemic. SMIs identified in various screening assays that were also confirmed to have antiviral activity in a live virus or pseudovirus assay with an IC50 < 30 µM so far include several organic dyes (methylene blue, Evans blue, Congo red, direct violet 1), verteporfin, DRI-C23041, and cannabigerolic and cannabidiolic acids. While specificity and activity profiles still need improvement, results so far already provide proof-of-principle evidence for the feasibility of SMIs targeting the SARS-CoV-2-S–hACE2 PPI. Methylene blue, which is approved for clinical use, is orally bioactive, and could act by multiple mechanisms of action, might have potential for repurposing for COVID-19 prevention and treatment.

In silico identification of small molecule protein-protein interaction inhibitors: targeting hotspot regions at the interface of MXRA8 and CHIKV envelope protein

Chikungunya virus (CHIKV) is an arthritogenic arbovirus responsible for re-emerging epidemics of Chikungunya fever around the world for centuries. Chikungunya has become endemic in Africa, Southeast Asia, the Indian subcontinent, and subtropical regions of the Americas. The unavailability of antiviral therapy or vaccine against the CHIKV and its continuous re-emergence demands an urgent need to develop potential candidate therapeutics. CHIKV entry into the host cell is mediated by its envelope proteins engaging the cellular receptor MXRA8 to invade the susceptible cells. We report here two essential target binding sites at the CHIKV E1-E2 proteins by identifying hotspot regions at the E1-E2-MXRA8 binding interface. Further, we employed high throughput computational screening to identify potential small molecule protein-protein interaction (PPI) modulators which could effectively bind at the identified target sites. Molecular dynamics simulations and binding free energy calculations confirmed the stability of three compounds, viz., ZINC299817498, ZINC584908978, and LAS52155651, at both the predicted interface binding sites. The polar and charged residues at the interface were responsible for energetically holding the ligands at the binding sites. Altogether, our findings suggest that the predicted target binding sites at the E1-E2 dimer could be essential to block the receptor interaction as well as the fusion process of the CHIKV particles. Thus, we identified a few small molecule PPI inhibitors with great potential to block the E1-E2-MXRA8 interaction and act as promising templates to design anti-CHIKV drugs. Communicated by Ramaswamy H. Sarma

Abstract P098: A chemoproteomic platform for identifying small-molecule modulators of protein-protein interactions, discovering new cancer targets, and revealing previously unknown targets for well-known drugs

Abstract BridGene Biosciences has developed a novel chemoproteomic platform IMTACTM (Isobaric Mass-Tagged Affinity Characterization) for discovering small-molecule inhibitors of protein-protein interactions, disclosing new cancer targets, and identifying previously unknown targets for known drugs. The key components of the IMTACTM platform include a unique library of drug-like covalent small molecules containing an alkyne tag, live-cell screening, and quantitative mass spec analysis. The IMTACTM analysis begins with treating live cells with probes from the covalent library, proceeds with enrichment of the probe-modified proteins, and then employs Mass Spec to identify the probe-modified proteins and determine the relative binding affinity. This powerful chemoproteomic platform enables systematic mapping of the direct interactions of small-molecules across the proteome. A prominent application of BridGene’s IMTACTM platform is to identify hits for “hard to drug” targets from live-cell screening. IMTAC TM screening is well suited for identifying drug-like ligands for such hard-to-drug targets, including those with shallow binding pockets or temporary pockets formed under certain cellular settings. Lead optimization can be immediately initiated after confirmation of the ligand’s binding to relevant targets. Using the IMTACTM platform, BridGene has discovered small-molecule ligands for a number of “hard-to-drug” targets as well as oncogenic mutants, including GTPases (e.g. RhoA), transcription factors (e.g. TEAD), splicing factors (e.g. SRSF1), epigenetic modulators (e.g. WDR5), E3 ligases, etc. The potency of the hits ranges from low nanomolar to micromolar. Because of the IMTACTM platform’s proteome-wide profiling capability, BridGene has also, for the first time, revealed new targets for some well-known drugs. Combining IMTAC TM screening with phenotypic screening, BridGene is also discovering new/unknown targets that drive certain disease phenotypic changes. IMTACTM is a novel platform to discover small molecule drugs for hard-to-drug targets. It allows the mapping of targets for small molecules on a proteome-wide scale, which can provide comprehensive selectivity information to facilitate lead optimization and lower off-target toxicity. IMTACTM has the potential to redefine precision medicine, discover new drugs and new targets, and identify new indications of known drugs. Citation Format: Cindy Huang, Vivian Zhang, Ning Deng, Irene Yuan, Linda Pullan, C. Glenn Begley, Ping Cao. A chemoproteomic platform for identifying small-molecule modulators of protein-protein interactions, discovering new cancer targets, and revealing previously unknown targets for well-known drugs [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P098.

Deconstructing Noncovalent Kelch-like ECH-Associated Protein 1 (Keap1) Inhibitors into Fragments to Reconstruct New Potent Compounds.

Targeting the protein-protein interaction (PPI) between nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) is a potential therapeutic strategy to control diseases involving oxidative stress. Here, six classes of known small-molecule Keap1-Nrf2 PPI inhibitors were dissected into 77 fragments in a fragment-based deconstruction reconstruction (FBDR) study and tested in four orthogonal assays. This gave 17 fragment hits of which six were shown by X-ray crystallography to bind in the Keap1 Kelch binding pocket. Two hits were merged into compound 8 with a 220-380-fold stronger affinity (Ki = 16 μM) relative to the parent fragments. Systematic optimization resulted in several novel analogues with Ki values of 0.04-0.5 μM, binding modes determined by X-ray crystallography, and enhanced microsomal stability. This demonstrates how FBDR can be used to find new fragment hits, elucidate important ligand-protein interactions, and identify new potent inhibitors of the Keap1-Nrf2 PPI.

An affinity prediction approach for the ligand of E3 ligase Cbl-b and an insight into substrate binding pattern

Protein-protein interactions (PPIs) are essentially fundamental to all cellular processes, so that developing small molecule inhibitors of PPIs have great significance despite representing a huge challenge. Studying PPIs with the help of peptide motifs could obtain the structural information and reference significance to reduce the difficulty in the development of small molecules. Computational methods are powerful tools to characterize peptide-protein interactions, especially molecular dynamics simulation and binding free energy calculation. Here, we established an affinity prediction model suitable for Casitas B lymphoma-b (Cbl-b) and phosphorylated motif system. According to the affinity data set of multiple truncated peptides, the force field, solvent model, and internal dielectric constant of molecular mechanics/generalized Born surface area (MM/GBSA) method were optimized. Further, we predicted the affinity of the rationally designed new sequences through this model and obtained a new 6-mer motif with a 7-fold increase in affinity and the comprehensive structure–activity relationship. Moreover, we proposed an insight of unexpected activity of the truncated 5-mer peptide and revealed the possible binding mode of the new highly active 6-mer motif by extended simulation. Our results showed that the activity enhancement of the truncated peptide was caused by the acetyl-mediated conformation change. The side chain of Arg and pTyr in the 6-mer motif co-occupied the site p1 to form numerous hydrogen bond interactions and increased hydrophobic interaction formed with Tyr266, leading to the higher affinity. The present work provided a reference to investigate the PPI of Cbl-b and phosphorylated substrates and guided the development of Cbl-b inhibitors.

Single-molecule analysis of interaction between p53TAD and MDM2 using aerolysin nanopores.

Protein–protein interactions (PPIs) are regarded as important, but undruggable targets. Intrinsically disordered p53 transactivation domain (p53TAD) mediates PPI with mouse double minute 2 (MDM2), which is an attractive anticancer target for therapeutic intervention. Here, using aerolysin nanopores, we probed the p53TAD peptide/MDM2 interaction and its modulation by small-molecule PPI inhibitors or p53TAD phosphorylation. Although the p53TAD peptide showed short-lived (<100 ms) translocation, the protein complex induced the characteristic extraordinarily long-lived (0.1 s ∼ tens of min) current blockage, indicating that the MDM2 recruitment by p53TAD peptide almost fully occludes the pore. Simultaneously, the protein complex formation substantially reduced the event frequency of short-lived peptide translocation. Notably, the addition of small-molecule PPI inhibitors, Nutlin-3 and AMG232, or Thr18 phosphorylation of p53TAD peptide, were able to diminish the extraordinarily long-lived events and restore the short-lived translocation of the peptide rescued from the complex. Taken together, our results elucidate a novel mechanism of single-molecule sensing for analyzing PPIs and their inhibitors using aerolysin nanopores. This novel methodology may contribute to remarkable improvements in drug discovery targeted against undruggable PPIs.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Development of a Homogeneous Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) Assay for the Inhibition of Keap1-Nrf2 Protein-Protein Interaction.

The transcription factor, nuclear factor erythroid 2-related factor 2 (Nrf2), plays a major role in regulating the antioxidant defense system through the Kelch-like ECH-associated protein 1-Nrf2-antioxidant response element (Keap1-Nrf2-ARE) pathway. Small-molecule inhibitors targeting Keap1-Nrf2 protein-protein interaction (PPI) decrease the rate of Nrf2 degradation by the 26S proteasome and thus increase the intracellular level of Nrf2, which translocates into the nucleus, leading to upregulated expression of cytoprotective and antioxidant enzymes. Such inhibitors can be developed into potential preventive and therapeutic agents of diseases caused by oxidative damage. To more effectively identify promising Nrf2 activators through the inhibition of Keap1-Nrf2 PPI, a homogeneous time-resolved fluorescence resonance energy transfer (TR-FRET) assay was developed in this work by indirectly labeling the Keap1 Kelch domain protein with Tb-anti-His antibody as the donor and using, as the acceptor, fluorescein isothiocyanate (FITC)-labeled 9mer Nrf2 peptide amide, the same fluorescent probe that was used in an earlier fluorescence polarization (FP) assay. Assay conditions, including concentrations of the various components, buffer type, and incubation time, were optimized in the TR-FRET competition assay with known small-molecule inhibitors of Keap1-Nrf2 PPI. Under the optimized conditions, the Keap1-Nrf2 TR-FRET assay exhibited great sensitivity with a high dynamic range and considerable stability for as long as 5 h. The Z' factor was determined to be 0.82, suggesting that the assay is suitable for high-throughput screening and lead optimization of inhibitors of Keap1-Nrf2 PPI. Furthermore, the TR-FRET assay is capable of differentiating potent inhibitors of Keap1-Nrf2 PPI down to the subnanomolar inhibition constant (Ki) range.

Nrf2 activation through the inhibition of Keap1-Nrf2 protein-protein interaction.

Activation of the transcription factor Nrf2 via the Keap1-Nrf2-ARE signaling system regulates the transcription and subsequent expression of cellular cytoprotective proteins and plays a crucial role in preventing pathological conditions exacerbated by the overproduction of oxidative stress. In addition to electrophilic modulators, direct non-covalent inhibitors that interrupt the Keap1-Nrf2 protein-protein interaction (PPI) leading to Nrf2 activation have attracted a great deal of attention as potential preventive and therapeutic agents for oxidative stress-related diseases. Structural studies of Keap1-binding ligands, development of biochemical and cellular assays, and new structure-based design approaches have facilitated the discovery of small molecule PPI inhibitors. This perspective reviews the Keap1-Nrf2-ARE system, its physiological functions, and the recent progress in the discovery and the potential applications of direct inhibitors of Keap1-Nrf2 PPI.

CPUY192018, a potent inhibitor of the Keap1-Nrf2 protein-protein interaction, alleviates renal inflammation in mice by restricting oxidative stress and NF-κB activation

The Keap1-Nrf2-ARE pathway regulates the constitutive and inducible transcription of various genes that encode detoxification enzymes, antioxidant proteins and anti-inflammatory proteins and has pivotal roles in the defence against cellular oxidative stress. In this study, we investigated the therapeutic potential of CPUY192018, a potent small-molecule inhibitor of the Keap1-Nrf2 protein-protein interaction (PPI), in renal inflammation. In human proximal tubular epithelial HK-2 cells, CPUY192018 treatment significantly increased Nrf2 protein level and Nrf2 nuclear translocation, which enhanced Nrf2-ARE transcription capacity and the downstream protein content in a Nrf2 dependent manner. In lipopolysaccharide (LPS)-challenged human HK-2 cells, CPUY192018 exhibited cytoprotective effects by enhancing the Nrf2-ARE regulated antioxidant system and diminished the LPS-induced inflammatory response by hindering the ROS-mediated activation of the NF-κB pathway. In the LPS-induced mouse model of chronic renal inflammation, by activating Nrf2, CPUY192018 treatment balanced renal oxidative stress and suppressed inflammatory responses. Hence, administration of CPUY192018 reduced kidney damage and ameliorated pathological alterations of the glomerulus. Taken together, our study suggested that small-molecule Keap1-Nrf2 PPI inhibitors can activate the Nrf2-based cytoprotective system and protect the kidney from inflammatory injury, raising a potential application of Keap1-Nrf2 PPI inhibitors in the treatment of inflammatory kidney disorders.

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases.

To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

FarPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods.

Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due to the relatively 'undruggable' features of PPI interfaces, accurate predictions of the binding structures for ligands towards PPI targets are quite challenging for most docking algorithms. Here, we constructed a non-redundant pose ranking benchmark dataset for small-molecule PPI inhibitors, which contains 900 binding poses for 184 protein-ligand complexes. Then, we evaluated the performance of MM/PB(GB)SA approaches to identify the correct binding poses for PPI inhibitors, including two Prime MM/GBSA procedures from the Schrödinger suite and seven different MM/PB(GB)SA procedures from the Amber package. Our results showed that MM/PBSA outperformed the Glide SP scoring function (success rate of 58.6%) and MM/GBSA in most cases, especially the PB3 procedure which could achieve an overall success rate of ∼74%. Moreover, the GB6 procedure (success rate of 68.9%) performed much better than the other MM/GBSA procedures, highlighting the excellent potential of the GBNSR6 implicit solvation model for pose ranking. Finally, we developed the webserver of Fast Amber Rescoring for PPI Inhibitors (farPPI), which offers a freely available service to rescore the docking poses for PPI inhibitors by using the MM/PB(GB)SA methods. farPPI web server is freely available at http://cadd.zju.edu.cn/farppi/. Supplementary data are available at Bioinformatics online.