Potential Binding Region Research Articles

Sodium butyrate regulates macrophage polarization by TGR5/β-arrestin2 in vitro

BackgroundMacrophages play an important role in the pathogenesis of ulcerative colitis (UC). We will explore the effects of sodium butyrate (SB) on macrophage function.MethodsThe targets of butyric acid were identified using SwissTargetPrediction database and surface plasmon resonance (SPR). Limited proteolysis mass spectrometry (Lip-MS) was used to further investigate the binding sites of butyric acid with its targets and molecular docking was employed to simulate their binding modes. Macrophage polarization model was established with lipopolysaccharide (LPS) in vitro. Takeda G protein-coupled receptor 5 (TGR5) and β-arrestin2 expression and macrophage polarization markers were detected with or without SB.ResultsTGR5 was identified as the target of butyric acid. Moreover, the amino acid regions 275–286 and 321–330 of TGR5 (GPBAR1 [275–286] and GPBAR1 [321–330]) were the potential binding regions for butyric acid. Based on molecular docking analysis, butyric acid formed effective hydrogen-bonding interactions with ASP-284 and TYR-287 of TGR5. In cell experiments, LPS inhibited the expression of TGR5, β-arrestin2, IL-10, ARG1, and CD206 and increased the expression of IL-1β, iNOS, and CD86, while SB reversed the effect of LPS. SBI-115, a TGR5 antagonist, and knockdown of β-arrestin2 inhibited the effect of sodium butyrate. INT-777, a TGR5 agonist, reversed the inhibitory effect of knockdown of β-arrestin2.ConclusionSB inhibited M1-like polarization and promoted M2-like polarization induced by LPS via TGR5/β-arrestin2 in RAW264.7 cells and TGR5 was the target of SB.

Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme

The COVID-19 pandemic, driven by the novel coronavirus SARS-CoV-2, has drastically reshaped global health and socioeconomic landscapes. The papain-like protease (PLpro) plays a critical role in viral polyprotein cleavage and immune evasion, making it a prime target for therapeutic intervention. Numerous compounds have been identified as inhibitors of SARS-CoV-2 PLpro, with many characterized through crystallographic studies. To date, over 70 three-dimensional (3D) structures of PLpro complexed ligands have been deposited in the Protein Data Bank, offering valuable insight into ligand-binding features that could aid the discovery and development of effective COVID-19 treatments targeting PLpro. In this study, we reviewed and analyzed these 3D structures, focusing on the key residues involved in ligand interactions. Our analysis revealed that most inhibitors bind to PLpro’s substrate recognition sites S3/S4 and SUb2. While these sites are highly attractive and have been extensively explored, other potential binding regions, such as SUb1 and the Zn(II) domain, are less explored and may hold untapped potential for future COVID-19 drug discovery and development. Our structural analysis provides insights into the molecular features of PLpro that could accelerate the development of novel therapeutics targeting this essential viral enzyme.

A novel partnership between lncTCF7 and SND1 regulates the expression of the TCF7 gene via recruitment of the SWI/SNF complex

Long non-coding RNAs (lncRNAs) play key roles in cellular pathways and disease progression, yet their molecular mechanisms remain largely understudied. The lncRNA lncTCF7 has been shown to promote tumor progression by recruiting the SWI/SNF complex to the TCF7 promoter, activating its expression and the WNT signaling pathway. However, how lncTCF7 recruits SWI/SNF remains to be determined, and lncTCF7-specific binding partners are unknown. Using RNA-pulldown and quantitative mass spectrometry, we identified a novel interacting protein partner for lncTCF7, SND1, a multifunctional RNA binding protein that can also function as a transcription co-activator. Knockdown analysis of lncTCF7 and SND1 reveals that they are both required for the expression of TCF7. Chromatin immunoprecipitation assays suggest that both SND1 and lncTCF7 are required for recruiting the SWI/SNF chromatin remodeling complex, and these functions, in tandem, activate the expression of TCF7. Finally, using structural probing and RNA-pulldown of lncTCF7 and its subdomains, we highlight the potential binding region for SND1 in the 3’-end of lncTCF7. Overall, this study highlights the critical roles lncRNAs play in regulating gene expression and provides new insights into the complex network of interactions that underlie this process.

Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.

Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, a novel Nucleotide-Level Deep Neural Network (NLDNN) is first proposed to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task, which takes DNA sequences as input and directly predicts experimental coverage values. Beyond predictive performance, it also assesses model performance by locating potential TF binding regions, discriminating TF-specific single-nucleotide polymorphisms (SNPs), and identifying causal disease-associated SNPs. The experimental results show that NLDNN outperforms the competing methods in these tasks. Then, a dual-path framework is designed for adversarial training of NLDNN to further improve the cross-species prediction performance by pulling the domain space of human and mouse species closer. Through comparison and analysis, it finds that adversarial training not only can improve the cross-species prediction performance between humans and mice but also enhance the ability to locate TF binding regions and discriminate TF-specific SNPs. By visualizing the predictions, it is figured out that the framework corrects some mispredictions by amplifying the coverage values of incorrectly predicted peaks.

Crystal structure of human Cep57 C-terminal domain reveals the presence of leucine zipper and the potential microtubule binding region.

Cep57, a vital centrosome-associated protein, recruits essential regulatory enzymes for centriole duplication. Its dysfunction leads to anomalies, including reduced centrioles and mosaic-variegated aneuploidy syndrome. Despite functional investigations, understanding structural aspects and their correlation with functions is partial till date. We present the structure of human Cep57 C-terminal microtubule binding (MT-BD) domain, revealing conserved motifs ensuring functional preservation across evolution. A leucine zipper, with an adjacent possible microtubule-binding region, potentially forms a stabilizing scaffold for microtubule nucleation-accommodating pulling and tension from growing microtubules. This study highlights conserved structural features of Cep57 protein, compares them with other analogous proteins, and explores how protein function is maintained across diverse organisms.

Efficiency of membrane fusion inhibitors on different hemagglutinin subtypes: insight from a molecular dynamics simulation perspective

The challenge in vaccine development, along with drug resistance issues, has encouraged the search for new anti-influenza drugs targeting different viral proteins. Hemagglutinin (HA) glycoprotein, crucial in the viral replication cycle, has emerged as a promising therapeutic target. CBS1117 and JNJ4796 were reported to exhibit similar potencies against infectious group 1 influenza, which included H1 and H5 HAs; however, their potencies were significantly reduced against group 2 HA. This study aims to explore the molecular binding mechanisms and group specificity of these fusion inhibitors against both group 1 (H5) and group 2 (H3) HA influenza viruses using molecular dynamics simulations. CBS1117 and JNJ4796 exhibit stronger interactions with key residues within the H5 HA binding pocket compared to H3-ligand complexes. Hydrogen bonding and hydrophobic interactions involving residues, such as H381, Q401, T3251 (H5-CBS1117), T3181 (H5-JNJ4796), W212, I452, V482, and V522 predominantly contribute to stabilizing H5-ligand systems. In contrast, these interactions are notably weakened in H3-inhibitor complexes. Predicted protein-ligand binding free energies align with experimental data, indicating CBS1117 and JNJ4796's preference for heterosubtypic group 1 HA binding. Understanding the detailed atomistic mechanisms behind the varying potencies of these inhibitors against the two HA groups can significantly contribute to the development and optimization of effective HA fusion inhibitors. To accomplish this, the knowledge of the transition of HA from its pre- to post-fusion states, the molecular size of ligands, and their potential binding regions, could be carefully considered. Communicated by Ramaswamy H. Sarma

Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning.

Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computational approaches remain highly challenging due to the limited knowledge of residue binding patterns. The binding pattern of a residue should be characterized by the spatial distribution of its neighboring residues combined with their physicochemical information interaction, which yet cannot be achieved by previous methods. Here, we design GraphRBF, a hierarchical geometric deep learning model to learn residue binding patterns from big data. To achieve it, GraphRBF describes physicochemical information interactions by designing an enhanced graph neural network and characterizes residue spatial distributions by introducing a prioritized radial basis function neural network. After training and testing, GraphRBF shows great improvements over existing state-of-the-art methods and strong interpretability of its learned representations. Applying GraphRBF to the SARS-CoV-2 omicron spike protein, it successfully identifies known epitopes of the protein. Moreover, it predicts multiple potential binding regions for new nanobodies or even new drugs with strong evidence. A user-friendly online server for GraphRBF is freely available at http://liulab.top/GraphRBF/server.

Insights on the nuclear shuttling of H2A-H2B histone chaperones

All cellular processes that involve the unwinding of DNA also lead to the systematic shuttling of histones. Histone shuttling across the nuclear membrane is facilitated by a class of proteins known as – histone chaperones. Histone chaperones are classified based on their binding to H3/H4 histones or H2A/H2B histones. During the shuttling process, two types of signals - NLS and NES are recognized by the nuclear transport proteins. However, this is the nuclear transport protein and the mechanism of signal recognition by the protein is still unknown. Thus, in this piece of work, the NLS and NES signals are predicted on important H2A/H2B binding histone chaperones. In addition, cellular localization and potential DNA binding regions of histone chaperones are predicted. Mapping of predicted regions on the histone chaperone’s structure suggested that the critical binding regions mainly lie on the disordered region of the histone chaperones. NLS and NES are present in the N- and C-terminal of the histone chaperones. Most histone chaperones contain bipartiate NLS signals. This article sheds light on the crucial aspect that in addition of being directly engaged in nucleosome synthesis and disassembly in vivo, histone chaperone also performs various specific roles via histone binding activity.

Multi-dimensional search for drug–target interaction prediction by preserving the consistency of attention distribution

Predicting drug–target interaction (DTI) is a crucial step in the process of drug repurposing and new drug development. Although the attention mechanism has been widely used to capture the interactions between drugs and targets, it mainly uses the Simplified Molecular Input Line Entry System (SMILES) and two-dimensional (2D) molecular graph features of drugs. In this paper, we propose a neural network model called MdDTI for DTI prediction. The model searches for binding sites that may interact with the target from the multiple dimensions of drug structure, namely the 2D substructures and the three-dimensional (3D) spatial structure. For the 2D substructures, we have developed a novel substructure decomposition strategy based on drug molecular graphs and compared its performance with the SMILES-based decomposition method. For the 3D spatial structure of drugs, we constructed spatial feature representation matrices for drugs based on the Cartesian coordinates of heavy atoms (without hydrogen atoms) in each drug. Finally, to ensure the search results of the model are consistent across multiple dimensions, we construct a consistency loss function. We evaluate MdDTI on four drug–target interaction datasets and three independent compound–protein affinity test sets. The results indicate that our model surpasses a series of state-of-the-art models. Case studies demonstrate that our model is capable of capturing the potential binding regions between drugs and targets, and it shows efficacy in drug repurposing. Our code is available at https://github.com/lhhu1999/MdDTI.

Vibrational and computational study on the characterization of Ag-Threonine complex

The current manuscript is emphasized on the vibrational spectroscopic study of l-Threonine-TCA (LTh-TCA) complex and metal-based complexes such as (l-Threonine-TCA)Ag [(LTh-TCA)Ag] and (l-Threonine-TCA)Ag2 [(LTh-TCA)Ag2] investigated under the Raman, FTIR and UV–vis techniques. The most favourable binding sites of the mentioned complexes were predicted under the density functional theory (DFT) evaluated using the B3LYP method and LANL2DZ basis set. The quantum chemical parameters of the complexes calculated under the DFT method affirm the chemical reactivity, bioactive property and distribution of electrons within the atoms of the molecules. The electronic transition of the atoms are analysed using the UV–vis simulation method performed under the IEFPCM solvation model by employing methanol as solvent. The AIM analysis is carried out to understand the interaction types manifested by the mentioned complexes. The bioactivity score and drug-likeness properties of the complexes are also analysed. The theoretical and experimental vibrational wave numbers of L-Th-TCA complex and its metal-based complexes are compared with each other and are found in good agreement with each other. The potential binding regions of the mentioned compounds with 1QCY collagen receptor is evaluated using the molecular docking analysis.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

Identification, binding, and structural characterization of single domain anti-PD-L1 antibodies inhibitory of immune regulatory proteins PD-1 and CD80

Programmed death-ligand 1 (PD-L1) is a key immune regulatory protein that interacts with programmed cell death protein 1 (PD-1), leading to T-cell suppression. Whilst this interaction is key in self-tolerance, cancer cells evade the immune system by overexpressing PD-L1. Inhibition of the PD-1/PD-L1 pathway with standard monoclonal antibodies has proven a highly effective cancer treatment, however, single domain antibodies (VHH) may offer numerous potential benefits. Here we report the identification and characterisation of a diverse panel of sixteen novel VHHs specific to PD-L1. The panel of VHHs demonstrate affinities of 0.7 nM to 5.1 μM and were able to completely inhibit PD-1 binding to PD-L1. The binding site for each VHH on PD-L1 was determined using NMR chemical shift perturbation mapping and revealed a common binding surface encompassing the PD-1 binding site. Additionally, we solved crystal structures of two representative VHHs in complex with PD-L1, which revealed unique binding modes. Similar NMR experiments were used to identify the binding site of CD80 on PD-L1, which is another immune response regulatory element and interacts with PD-L1 localized on the same cell surface. CD80 and PD-1 were revealed to share a highly overlapping binding site on PD-L1, with the panel of VHHs identified expected to inhibit CD80 binding. Comparison of the CD80 and PD-1 binding sites on PD-L1 enabled the identification of a potential antibody binding region able to confer specificity for the inhibition of PD-1 binding only, which may offer therapeutic benefits to counteract cancer cell evasion of the immune system.

Synthetic antibody-derived immunopeptide provides neuroprotection in glaucoma through molecular interaction with retinal protein histone H3.1.

Glaucoma is a group of optic neuropathies characterized by the progressive degeneration of retinal ganglion cells (RGCs) as well as their axons leading to irreversible loss of sight. Medical management of the intraocular pressure (IOP) still represents the gold standard in glaucoma therapy, which only manages a single risk factor and does not directly address the neurodegenerative component of this eye disease. Recently, our group showed that antibody-derived immunopeptides (encoding complementarity-determining regions, CDRs) provide attractive glaucoma medication candidates and directly interfere its pathogenic mechanisms by different modes of action. In accordance with these findings, the present study showed the synthetic complementary-determining region 2 (CDR2) peptide (INSDGSSTSYADSVK) significantly increased RGC viability in vitro in a concentration-dependent manner (p < 0.05 using a CDR2 concentration of 50 μg/mL). Employing state-of the-art immunoprecipitation experiments, we confirmed that synthetic CDR2 exhibited a high affinity toward the retinal target protein histone H3.1 (HIST1H3A) (p < 0.001 and log2-fold change > 3). Furthermore, molecular dynamics (MD) simulations along with virtual docking analyses predicted potential CDR2-specific binding regions of HIST1H3A, which might represent essential post-translational modification (PTM) sites for epigenetic regulations. Quantitative mass spectrometry (MS) analysis of retinas demonstrated 39 proteins significantly affected by CDR2 treatment (p < 0.05). An up-regulation of proteins involved in the energy production (e.g., ATP5F1B and MT-CO2) as well as the regulatory ubiquitin proteasome system (e.g., PSMC5) was induced by the synthetic CDR2 peptide. On the other hand, CDR2 reduced metabolic key enzymes (e.g., DDAH1 and MAOB) as well as ER stress-related proteins (e.g., SEC22B and VCP) and these data were partially confirmed by microarray technology. Our outcome measurements indicate that specific protein-peptide interactions influence the regulatory epigenetic function of HIST1H3A promoting the neuroprotective mechanism on RGCs in vitro. In addition to IOP management, such synthetic peptides as CDR2 might serve as a synergistic immunotherapy for glaucoma in the future.

Base-resolution prediction of transcription factor binding signals by a deep learning framework.

Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level (named as FCNsignal). The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs. Besides, FCNsignal can also be used to predict opening regions across the whole genome. The experimental results on 53 TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets show that our proposed framework outperforms some existing state-of-the-art methods. In addition, we explored to use the trained FCNsignal to locate all potential TF-DNA binding regions on a whole chromosome and predict DNA sequences of arbitrary length, and the results show that our framework can find most of the known binding regions and accept sequences of arbitrary length. Furthermore, we demonstrated the potential ability of our framework in discovering causal disease-associated single-nucleotide polymorphisms (SNPs) through a series of experiments.

Structural basis for METTL6-mediated m3C RNA methylation

RNA modifications play important roles in mediating the biological functions of RNAs. 3-methylcytidine (m3C), albeit less abundant, is found to exist extensively in tRNAs, rRNAs and mRNAs. Human METTL6 is a m3C methyltransferase for tRNAs, including tRNASER(UGA). We solved the structure of human METTL6 in the presence of S-adenosyl-L-methionine and found by enzyme assay that recombinant human METTL6 is active towards tRNASER(UGA). Structural analysis indicated the detailed interactions between S-adenosyl-L-methionine and METTL6, and suggested potential tRNA binding region on the surface of METTL6. The structural research, complemented by biochemistry enzyme assay, will definitely shed light on the design of potent inhibitors for METTL6 in near future.

LncRNA NEAT1 promotes glioma cancer progression via regulation of miR-98-5p/BZW1.

Background: Glioma is the most common malignant tumor in the human central nervous system. Long noncoding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) promotes oncogenesis in various tumors. In the present study, we aimed to examine the role of NEAT1 in altering the properties of gliomas.Methods: Quantitative real-time PCR technology was used to determine the expression levels of relevant genes in tumor tissues and cell lines. The protein expression levels were validated by Western blotting. Cell counting kit-8 (CCK-8) and colony formation assays were used to test the cell proliferation ability. A luciferase reporter assay was used to determine the interactions of the genes. Tumor xenografts were used to detect the role of NEAT1 in gliomas in vivo.Results: We demonstrated that NEAT1 up-regulated glioma cells and negatively correlated with miR-98-5p in glioma tissues. A potential binding region between NEAT1 and miR-98-5p was confirmed by dual-luciferase assays. NEAT1 knockdown inhibited glioma cell proliferation. The inhibition of miR-98-5p rescued the knockdown of NEAT1 in glioma cells. Basic leucine zipper and W2 domain containing protein 1 (BZW1) was identified as a direct target of miR-98-5p. We also identified that BZW1 was positively correlated with NEAT1 in glioma tissues. NEAT1 knockdown inhibited glioma cell proliferation in vivo via miR-98-5p/BZW1.Conclusion: Our results suggest that NEAT1 plays an oncogenic function in glioma progression. Targeting NEAT1/miR-98-5p/BZW1 may be a novel therapeutic treatment approach for glioma patients.

NEAT1 siRNA Packed with Chitosan Nanoparticles Regulates the Development of Colon Cancer Cells via lncRNA NEAT1/miR-377-3p Axis.

This study was for verifying that transfecting colon cancer cells (CCCs) with lncRNA NEAT1 packed with siRNA chitosan nanoparticles (CNPs) can suppress lncRNA NEAT1 and biological behaviors of the cells. siRNA targeting lncRNA NEAT1 expression vector was constructed and then transfected into CCCs after being packed with CNPs. Subsequently, the impact of the transfection on biological behaviors of the cells was evaluated. As a result, with high expression in CCCs, NEAT1 was negatively bound up with miR-377-3p in cases with colon cancer (CC), and dual luciferase reporter assay confirmed the potential binding region. Additionally, after downregulating NEAT1 in CCCs, transfection of NEAT1 siRNA packed with CNPs brought a great inhibition on cell proliferation and a promotion on apoptosis, and inhibiting miR-377-3p was able to offset the role of silencing NEAT1 in CCCs. Therefore, in our opinion, NEAT1 siRNA packed with CNPs can hinder the growth and metastasis of CCCs by knocking down NEAT1 in CC, and its mechanism may be achieved by targeting miR-377-3p, which offers a novel direction for treating CC.

Interactions of ferritin with scavenger receptor class A members

Scavenger receptors are a superfamily of membrane-bound receptors that recognize both self and nonself targets. Scavenger receptor class A (SR-A) has five known members (SCARA1 to -5 or SR-A1 to -A5), which are type II transmembrane proteins that form homotrimers on the cell surface. SR-A members recognize various ligands and are involved in multiple biological pathways. Among them, SCARA5 can function as a ferritin receptor; however, the interaction between SCARA5 and ferritin has not been fully characterized. Here, we determine the crystal structures of the C-terminal scavenger receptor cysteine-rich (SRCR) domain of both human and mouse SCARA5 at 1.7 and 2.5 Å resolution, respectively, revealing three Ca2+-binding sites on the surface. Using biochemical assays, we show that the SRCR domain of SCARA5 recognizes ferritin in a Ca2+-dependent manner, and both L- and H-ferritin can be recognized by SCARA5 through the SRCR domain. Furthermore, the potential binding region of SCARA5 on the surface of ferritin is explored by mutagenesis studies. We also examine the interactions of ferritin with other SR-A members and find that SCARA1 (SR-A1, CD204) and MARCO (SR-A2, SCARA2), which are highly expressed on macrophages, also interact with ferritin. By contrast, SCARA3 and SCARA4, the two SR-A members without the SRCR domain, have no detectable binding with ferritin. Overall, these results provide a mechanistic view regarding the interactions between the SR-A members and ferritin that may help to understand the regulation of ferritin homeostasis by scavenger receptors.

Aptamer-linked immobilized sorbent assay for detecting GMO marker, phosphinothricin acetyltransferase (PAT)

Development of genetically modified crops has rapidly increased in last few years. The most widely grown GM crops express genes that confer herbicide tolerance and insect resistance. Detection system of GM crops is important for safety evaluation before its consumption. The purpose of this research is to detect GM crops, especially PAT, in food-samples. The bar gene (PAT protein, herbicide resistant) was cloned in pGEX-4T-1 and expressed by E. coli. The high-affinity PAT-specific single-stranded DNA (ssDNA) aptamers were obtained from a random DNA library. MOE docking study was performed to identify the potential binding region of the selected aptamers on PAT. Aptamer-linked immobilized sorbent assay (ALISA) method was used to detect PAT. We screened aptamer against PAT for developing an efficient detection method. The selected PAT specific aptamers, HRPA-05 and HRPA-07, showed the distinct target binding behaviors, and detected PAT protein by aptamer-linked immobilized sorbent assay method with high efficiency and selectivity.

Regulation of epidermal differentiation through KDF1-mediated deubiquitination of IKKα.

Progenitor cells at the basal layer of skin epidermis play an essential role in maintaining tissue homeostasis and enhancing wound repair in skin. The proliferation, differentiation, and cell death of epidermal progenitor cells have to be delicately regulated, as deregulation of this process can lead to many skin diseases, including skin cancers. However, the underlying molecular mechanisms involved in skin homeostasis remain poorly defined. In this study, with quantitative proteomics approach, we identified an important interaction between KDF1 (keratinocyte differentiation factor 1) and IKKα (IκB kinase α) in differentiating skin keratinocytes. Ablation of either KDF1 or IKKα in mice leads to similar but striking abnormalities in skin development, particularly in skin epidermal differentiation. With biochemical and mouse genetics approach, we further demonstrate that the interaction of IKKα and KDF1 is essential for epidermal differentiation. To probe deeper into the mechanisms, we find that KDF1 associates with a deubiquitinating protease USP7 (ubiquitin-specific peptidase 7), and KDF1 can regulate skin differentiation through deubiquitination and stabilization of IKKα. Taken together, our study unravels an important molecular mechanism underlying epidermal differentiation and skin tissue homeostasis.