Enhancement Of Affinity Research Articles

Development and Characterization of CD44-Targeted X-Aptamers with Enhanced Binding Affinity for Cancer Therapeutics

CD44, a pivotal cell surface molecule, plays a crucial role in many cellular functions, including cell-cell interactions, adhesion, and migration. It serves as a receptor for hyaluronic acid and is involved in lymphocyte activation, recirculation, homing, and hematopoiesis. Moreover, CD44 is a commonly used cancer stem cell marker associated with tumor progression and metastasis. The development of CD44 aptamers that specifically target CD44 can be utilized to target CD44-positive cells, including cancer stem cells, and for drug delivery. Building on the primary sequences of our previously selected thioaptamers (TAs) and observed variations, we developed a bead-based X-aptamer (XA) library by conjugating drug-like ligands (X) to the 5-positions of certain uridines on a complete monothioate backbone. From this, we selected an XA with high affinity to the CD44 hyaluronic acid binding domain (HABD) from a large combinatorial X-aptamer library modified with N-acetyl-2,3-dehydro-2-deoxyneuraminic acid (ADDA). This XA demonstrated an enhanced binding affinity for the CD44 protein up to 23-fold. The selected CD44 X-aptamers (both amine form and ADDA form) also showed enhanced binding affinity to CD44-overexpressing human ovarian cancer IGROV cells. Secondary structure predictions of CD44 using MFold identified several binding motifs and smaller constructs of various stem-loop regions. Among our identified binding motifs, X-aptamer motif 3 and motif 5 showed enhanced binding affinity to CD44-overexpressing human ovarian cancer IGROV cells with ADDA form, compared to the binding affinities with amine form and scrambled sequence. The effect of ADDA as a binding affinity enhancer was not uniform within the aptamer, highlighting the importance of optimal ligand positioning. The incorporation of ADDA not only broadened the XA’s chemical diversity but also increased the binding surface area, offering enhanced specificity. Therefore, the strategic use of site-directed modifications allows for fine-tuning aptamer properties and offers a flexible, generalizable framework for developing high-performance aptamers that target a wide range of molecules.

Amino Acids Frequency and Interaction Trends: Comprehensive Analysis of Experimentally Validated Viral Antigen-Antibody Complexes.

Antibodies have specific binding capabilities and therapeutic potential for treating various diseases, including viral infections. The amino acid composition of the hypervariable complementarity determining regions (CDR) loops and the framework regions (FR) are the determining factors for the affinity and therapeutic efficacy of the antibodies. In this study selected and curated, 77 viral antigen-human antibody complexes from Protein data bank from the Thera-SAbdab database were analyzed. The results revealed diversity indices within specific CDR regions, amino acid frequencies, paratope-epitope interactions, bond formations, and bond types among the analyzed viral Ag-Ab complexes. The finding revealed that Ser, Gly, Tyr, Thr, and Phe are prominently present in the antibody CDRs. Analysis of CDR profiles indicated an average amino acid diversity of 60-80% in heavy chain CDRs and 45-60% in light chain CDRs. Aromatic residues, particularly Tyr, Phe, and Trp showed significant involvement in the paratope-epitope interactions in the heavy chain, while Tyr, Ser, and Thr were key contributors in the light chain. Furthermore, the study examined the occurrence of amino acids in both light and heavy chains of viral Ag- human Ab complexes, analyzing the presence of amino acids as single residues, dipeptides and tripeptides. The analysis is crucial for enhancing the antibody engineering processes including, design, optimization, affinity enhancement, and overall antibody development.

An allelic atlas of immunoglobulin heavy chain variable regions reveals antibody binding epitope preference resilient to SARS-CoV-2 mutation escape.

Although immunoglobulin (Ig) alleles play a pivotal role in the antibody response to pathogens, research to understand their role in the humoral immune response is still limited. We retrieved the germline sequences for the IGHV from the IMGT database to illustrate the amino acid polymorphism present within germline sequences of IGHV genes. We aassembled the sequences of IgM and IgD repertoire from 130 people to investigate the genetic variations in the population. A dataset comprising 10,643 SARS-CoV-2 spike-specific antibodies, obtained from COV-AbDab, was compiled to assess the impact of SARS-CoV-2 infection on allelic gene utilization. Binding affinity and neutralizing activity were determined using bio-layer interferometry and pseudovirus neutralization assays. Primary docking was performed using ZDOCK (3.0.2) to generate the initial conformation of the antigen-antibody complex, followed by simulations of the complete conformations using Rosetta SnugDock software. The original and simulated structural conformations were visualized and presented using ChimeraX (v1.5). We present an allelic atlas of immunoglobulin heavy chain (IgH) variable regions, illustrating the diversity of allelic variants across 33 IGHV family germline sequences by sequencing the IgH repertoire of in the population. Our comprehensive analysis of SARS-CoV-2 spike-specific antibodies revealed the preferential use of specific Ig alleles among these antibodies. We observed an association between Ig alleles and antibody binding epitopes. Different allelic genotypes binding to the same RBD epitope on the spike show different neutralizing potency and breadth. We found that antibodies carrying the IGHV1-69*02 allele tended to bind to the RBD E2.2 epitope. The antibodies carrying G50 and L55 amino acid residues exhibit potential enhancements in binding affinity and neutralizing potency to SARS-CoV-2 variants containing the L452R mutation on RBD, whereas R50 and F55 amino acid residues tend to have reduced binding affinity and neutralizing potency. IGHV2-5*02 antibodies using the D56 allele bind to the RBD D2 epitope with greater binding and neutralizing potency due to the interaction between D56 on HCDR2 and K444 on RBD of most Omicron subvariants. In contrast, IGHV2-5*01 antibodies using the N56 allele show increased binding resistance to the K444T mutation on RBD. This study provides valuable insights into humoral immune responses from the perspective of Ig alleles and population genetics. These findings underscore the importance of Ig alleles in vaccine design and therapeutic antibody development.

Computational design and improvement of a broad influenza virus HA stem targeting antibody.

Broadly neutralizing antibodies (nAbs) are vital therapeutic tools to counteract both pandemic and seasonal influenza threats. Traditional strategies for optimizing nAbs generally rely on labor-intensive, high-throughput mutagenesis screens. Here, we present an innovative structure-based design framework for the optimization of nAbs, which integrates epitope-paratope analysis, computational modeling, and rational design approaches, complemented by comprehensive experimental assessment. This approach was applied to optimize MEDI8852, a nAb targeting the stalk region of influenza A virus hemagglutinin (HA). The resulting variant, M18.1.2.2, shows a marked enhancement in both affinity and neutralizing efficacy, as demonstrated both invitro and invivo. Computational modeling reveals that this improvement can be attributed to the fine-tuning of interactions between the antibody's side-chains and the epitope residues that are highly conserved across the influenza A virus HA stalk. Our dry-wet iterative protocol for nAb optimization presented here yielded a promising candidate for influenza intervention.

Novel Strategy of Antibody Affinity Maturation and Enhancement of Nucleolin-Mediated Antibody-Dependent Cellular Cytotoxicity Against Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer that remains an unmet medical need. Because TNBC cells do not express the most common markers of breast cancers, there is an active search for novel molecular targets in triple-negative tumors. Additionally, this subtype of breast cancer presents strong immunogenic characteristics which have been encouraging the development of immunotherapeutic approaches against the disease. In this context, nucleolin arises as a promising target for immunotherapy against TNBC. Our group has previously developed an anti-nucleolin VHH-Fc antibody capable of eliciting antibody-dependent cellular cytotoxicity (ADCC). Moreover, we constructed and characterized an antibody library, that was screened against nucleolin-overexpressing cells, originating an enriched anti-nucleolin antibody pool. In this work, a strategy to select individual clones from the pool was designed, combining NGS data with 3D modeling. Two antibodies demonstrated a significant 4.4- and 6.1-fold increase in binding to nucleolin-overexpressing and TNBC cells, and an improvement in affinity to the sub-micromolar range (0.19µM and 83.69nM). Additionally, an increment in 4.6- and 3.1-fold in ADCC activity against respective cell lines was observed for the M2 antibody clone. Herein, the affinity maturation strategy developed was validated and corroborated a positive, but not proportional, correlation between antibody binding, affinity, and ADCC.

Therapeutic hypoxia for mitochondrial disease via enhancement of hemoglobin affinity and inhibition of HIF-2α.

Therapeutic hypoxia for mitochondrial disease via enhancement of hemoglobin affinity and inhibition of HIF-2α.

Augmenting antioxidative capacity of myosin and cytoprotective potential of myosin digestion products through the integration of crocin and crocetin: A comprehensive analysis via quantum chemical computing and molecular dynamics

This study explores the binding properties of two important constituents from Crocus sativus L (crocin and crocetin) with myosin, examining their influence on antioxidant capacity in myosin and a grilled meat model. And their impact on cytoprotective potential of myosin digestion products was also assessed in Caco-2 cells. Crocin and crocetin exhibited discernible binding affinity to myosin via static quenching, which induced conformational alterations that bolstered the antioxidant capacity of myosin, preventing peroxidation, which also corroborated in a grilled meat model. Crocin resulted in greater enhancement of antioxidant capacity and binding affinity, as confirmed by quantum chemical calculations. Molecular dynamics simulations revealed the stable binding of crocin to GLU:272, GLU:606, GLN:628, and PHE:417 residues of myosin. In addition, crocin substantially enhanced the protective efficacy of myosin digestion products against H2O2-induced damage in Caco-2 cells by upregulating superoxide dismutase and GSH-Px and simultaneously downregulating reactive oxygen species and malondialdehyde levels.

Engineering affinity of humanized ScFv targeting CD147 antibody: A combined approach of mCSM-AB2 and molecular dynamics simulations

This study aims to assess the effectiveness of mCSM-AB2, a graph-based signature machine learning method, for affinity engineering of the humanized single-chain Fv anti-CD147 (HuScFvM6-1B9). In parallel, molecular dynamics (MD) simulations were used to gain valuable insights into the dynamics and affinity of the HuScFvM6-1B9-CD147 complex. The result analysis involved integrating free energy changes calculated from the mCSM-AB2 with binding free energy predictions from MD simulations. The simulated structures of the modified HuScFvM6-1B9-CD147 domain 1 complex from MD simulations were used to highlight critical residues participating in the binding surface. Interestingly, alterations in the pattern of amino acids of HuScFvM6-1B9 at the complementarity determining regions interacting with the 31EDLGS35 epitope were observed, particularly in mutants that lost binding activity. The predicted mutants of HuScFvM6-1B9 were subsequently engineered and expressed in E. coli for subsequent binding property validation. Compared to WT HuScFvM6-1B9, the mutant HuScFvM6-1B9L1:N32Y exhibited a 1.66-fold increase in binding affinity, with a KD of 1.75 × 10−8 M. While mCSM-AB2 demonstrates insignificant improvement in predicting binding affinity enhancements, it excels at predicting negative effects, aligning well with experimental validation. In addition to binding free energies, total entropy was considered to explain the discrepancy between mCSM-AB2 predictions and experimental results. This study provides guidelines and identifies the limitations of mCSM-AB2 and MD simulations in antibody engineering.

Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

Rapid transformation of nanobodies affinity based on AlphaFold2's high-accuracy predictions and interaction analysis for enrofloxacin detection in coastal fish

High-affinity antibodies are crucial in biosensors, disease diagnostics, therapeutic drug development, and immunological analysis, making the enhancement of antibody affinity a key research focus within the field. Computer-aided design is recognized as a time-saving and labor-efficient method for nanobodies in vitro affinity maturation. Compared to experimental mutagenesis techniques, it is advantageous due to the elimination of the need for laborious library construction and screening processes. However, these approaches are constrained by structural prediction since inaccuracy in structure could readily result in maturation failures. Herein, a novel nanobodies modification method for in vitro affinity maturation, utilizing the high accuracy prediction of AlphaFold2, was employed to rapidly transform a low affinity nanobody against enrofloxacin (ENR) into one with high affinity. The molecular docking results revealed a 1.5- to 2.5-fold increase in the number of noncovalent interactions of modified nanobodies, accompanied by a reduction in binding free energy ranging from 14.1 to 62.6%. The evaluation results from ELISA and BLI indicated that the affinity of the modified nanobodies had been enhanced by 6.2–91.6 times compared to the template nanobody. Furthermore, the modified nanobodies were employed for the detection of ENR-spiked coastal fish samples. In summary, this research proposed a nanobodies modification method from a new perspective, endowing its great application potential in biosensors, food safety, and environmental monitoring.

Discovery of Novel Neo-Clerodane Derivatives as Potent Dual-Functional Antiosteoporosis Agents through Targeting Peroxisome Proliferator-Activated Receptor-γ.

A library of 31 natural neo-clerodanes isolated from Ajuga decumbens was assayed for antiosteoporosis. This results in 18 neo-clerodane osteoclastogenesis inhibitors, and compound 3 prevents bone loss in vivo. Further mechanistic studies demonstrated that these compounds inhibit osteoporosis by antagonizing peroxisome proliferator-activated receptor-γ (PPARγ). We designed and synthesized 17 compounds by chemically modifying the natural neo-clerodane 19 (highly potent and the major composition of A. decumbens extract) by means of structure-based drug design techniques. Among these neo-clerodane derivatives, compound 34 is the most potent osteoporosis inhibitor with a 46-fold improvement in inhibiting osteoclastogenesis (IC50 = 0.042 vs 1.92 μM), 11-fold increased activity in PPARγ antagonism (EC50 = 0.75 vs 8.35 μM), 66-fold enhancement in receptor affinity (KD = 0.27 vs 17.7 μM), and enhanced osteogenic promotion compared to 19. This underscores the potential of neo-clerodane diterpenoids as promising leads for osteoporosis treatment by targeting PPARγ.

Insights into Cis-Amide-Modified Carbon Nanotubes for Selective Purification of CH4 and H2 from Gas Mixtures: A Comparative DFT Study.

Among a plethora of mixtures, the methane (CH4) and hydrogen (H2) mixture has garnered considerable attention for multiple reasons, especially in the framework of energy production and industrial processes as well as ecological considerations. Despite the fact that the CH4/H2 mixture performs many critical tasks, the presence of other gases, such as carbon dioxide, sulfur compounds like H2S, and water vapor, leads to many undesirable consequences. Thus purification of this mixture from these gases assumes considerable relevance. In the current research, first-principle calculations in the frame of density functional theory are carried out to propose a new functional group for vertically aligned carbon nanotubes (VA-CNTs) interacting preferentially with polar molecules rather than CH4 and H2 in order to obtain a more efficient methane and hydrogen separations The binding energies associated with the interactions between several chemical groups and target gases were calculated first, and then a functional group formed by a modified ethylene glycol and acetyl amide was selected. This functional group was attached to the CNT edge with an appropriate diameter, and hence the binding energies with the target gases and steric hindrance were evaluated. The binding energy of the most polar molecule (H2O) was found to be more than six times higher than that of H2, indicating a significant enhancement of the nanotube tip's affinity toward polar gases. Thus, this functionalization is beneficial for enhancing the capability of highly packed functionalized VA-CNT membranes to purify CH4/H2 gas mixtures.

Binding of SARS-CoV-2 Nonstructural Protein 1 to 40S Ribosome Inhibits mRNA Translation.

Experimental evidence has established that SARS-CoV-2 NSP1 acts as a factor that restricts cellular gene expression and impedes mRNA translation within the ribosome's 40S subunit. However, the precise molecular mechanisms underlying this phenomenon have remained elusive. To elucidate this issue, we employed a combination of all-atom steered molecular dynamics and coarse-grained alchemical simulations to explore the binding affinity of mRNA to the 40S ribosome, both in the presence and absence of SARS-CoV-2 NSP1. Our investigations revealed that the binding of SARS-CoV-2 NSP1 to the 40S ribosome leads to a significant enhancement in the binding affinity of mRNA. This observation, which aligns with experimental findings, strongly suggests that SARS-CoV-2 NSP1 has the capability to inhibit mRNA translation. Furthermore, we identified electrostatic interactions between mRNA and the 40S ribosome as the primary driving force behind mRNA translation. Notably, water molecules were found to play a pivotal role in stabilizing the mRNA-40S ribosome complex, underscoring their significance in this process. We successfully pinpointed the specific SARS-CoV-2 NSP1 residues that play a critical role in triggering the translation arrest.

Beyond Shell-Filling: Strong Enhancement of Electron Affinity of Metal Clusters through a Noninvasive Oriented External Electric Field.

Traditional electron counting rules, like the Jellium model, have long been successfully utilized in designing superhalogens by modifying clusters to have one electron less than a filled electronic shell. However, this shell-filling approach, which involves altering the intrinsic properties of the clusters, can be complex and challenging to control, especially in experiments. In this letter, we theoretically establish that the oriented external electric field (OEEF) can substantially enhance the electron affinity (EA) of diverse aluminum-based metal clusters with varying valence electron configurations, leading to the creation of superhalogen species without altering their shell arrangements. This OEEF approach offers a noninvasive alternative to traditional superatom design strategies, as it does not disrupt the clusters' geometrical structures and superatomic states. These findings contribute a vital piece to the puzzle of constructing superalkalis and superhalogens, extending beyond conventional shell-filling strategies and potentially expanding the range of applications for functional clusters.

Peptide nucleic acids (PNAs) control function of SARS-CoV-2 frameshifting stimulatory element trough PNA-RNA-PNA triplex formation

The highly structured nature of the SARS-CoV-2 genome provides many promising antiviral drug targets. One particularly promising target is a cis-acting RNA pseudoknot found within a critical region called the frameshifting stimulatory element (FSE). In this study, peptide nucleic acids (PNAs) binding to stem 2 of FSE RNA inhibited protein translation and frameshifting, as measured by a cell-free dual luciferase assay, more effectively than PNAs binding to stem 1, stem 3, or the slippery site. Surprisingly, simple antisense PNAs were stronger disruptors of frameshifting than PNA tail-clamps, despite higher thermal stability of the PNA-RNA-PNA triplexes formed by the latter. Another unexpected result was a strong and sequence non-specific enhancement of frameshifting inhibition when using a cationic triplex-forming PNA in conjunction with an antisense PNA targeting key regions of the frameshifting element. Our results illustrate both the potential and the challenges of using antisense PNAs to target highly structured RNAs, such as SARS-CoV-2 pseudoknots. While triplex forming PNAs, including PNA tail-clamps, are emerging as promising ligands for RNA recognition, the binding affinity enhancements when using cationic modifications in triplex-forming PNAs must be carefully balanced to avoid loss of sequence specificity in complex biological systems.

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

Antibody Engineering to Enhancement of Ranibizumab Binding Affinity for the Prevention and Treatment of Diabetic Retinopathy

Objective: The VEGF function blockage effectively reduces the progression of diabetic retinopathy. Ranibizumab and bevacizumab are some anti-VEGF monoclonal antibodies (mAb). Considering the importance of affinity maturation of ranibizumab, we aimed to find the essential amino acids of the ranibizumab antibody (Ab). Materials and Methods: We tried to find the important amino acids of this antibody via Paratome, Meta-PPISP, and the WESA web server. Subsequently, these amino acids were mutated to improve the binding affinity of the Ab variants to antigen (Ag). In this regard, the ranibizumab anti-VEGF-A was mutated. The structural docking prediction of the ranibizumab-VEGF-A complex was used for the design and validation of ranibizumab with a higher affinity for binding to VEGF-A. Finally, we measured the binding affinity of Ab variants to Ag by computational docking. Results: Bioinformatic analyzes such as molecular docking and dynamics showed that several mutant variants successfully improved the properties of Ab binding compared to the wild-type Ab. Conclusion: Consistent with the use of anti-VEGF monoclonal antibodies in the treatment of diabetic retinopathy, the mutant variants of ranibizumab may be potential candidates for stronger affinity binding to VEGF, which may affect the specificity and sensitivity of the antibody.

Water adsorption kinetics on graphene controlled by surface modification of supporting substrates

Sensing layers with an increased affinity for water molecules are essential for the development of highly sensitive humidity sensors. Graphene possesses superior electrical properties that make it suitable for the fabrication of low-noise miniaturized sensors. However, the enhancement of water affinity by introducing surface defects such as covalently attached hydrophilic groups reduces the electrical conductivity of graphene. In this study, we exploit the wetting transparency of graphene to increase its water affinity without introducing defects. Kinetic measurements using a Kelvin probe with a large-diameter tip showed that the rate constant of water adsorption was higher for graphene deposited on a hydrophilic substrate. These findings suggest that the wetting transparency of graphene can be exploited to reduce defect introduction into the graphene sensing layer, and has potential applications in sensor technologies.

Plasmonic Imaging of Multivalent NTD-Nucleic Acid Interactions for Broad-Spectrum Antiviral Drug Analysis.

The discovery and identification of broad-spectrum antiviral drugs are of great significance for blocking the spread of pathogenic viruses and corresponding variants of concern. Herein, we proposed a plasmonic imaging-based strategy for assessing the efficacy of potential broad-spectrum antiviral drugs targeting the N-terminal domain of a nucleocapsid protein (NTD) and nucleic acid (NA) interactions. With NTD and NA conjugated gold nanoparticles as core and satellite nanoprobes, respectively, we found that the multivalent binding interactions could drive the formation of core-satellite nanostructures with enhanced scattering brightness due to the plasmonic coupling effect. The core-satellite assembly can be suppressed in the presence of antiviral drugs targeting the NTD-NA interactions, allowing the drug efficacy analysis by detecting the dose-dependent changes in the scattering brightness by plasmonic imaging. By quantifying the changes in the scattering brightness of plasmonic nanoprobes, we uncovered that the constructed multivalent weak interactions displayed a 500-fold enhancement in affinity as compared with the monovalent NTD-NA interactions. We demonstrated the plasmonic imaging-based strategy for evaluating the efficacy of a potential broad-spectrum drug, PJ34, that can target the NTD-NA interactions, with the IC50 as 24.35 and 14.64 μM for SARS-CoV-2 and SARS-CoV, respectively. Moreover, we discovered that ceftazidime holds the potential as a candidate drug to inhibit the NTD-NA interactions with an IC50 of 22.08 μM from molecular docking and plasmonic imaging-based drug analysis. Finally, we validated that the potential antiviral drug, 5-benzyloxygramine, which can induce the abnormal dimerization of nucleocapsid proteins, is effective for SARS-CoV-2, but not effective against SARS-CoV. All these demonstrations indicated that the plasmonic imaging-based strategy is robust and can be used as a powerful strategy for the discovery and identification of broad-spectrum drugs targeting the evolutionarily conserved viral proteins.

Self-extinguishing polyimide sandwiched separators for high-safety and fast-charging lithium metal batteries

Developing fast-charging lithium metal battery (LMB) with high specific capacity arouses enormous attentions. Unfortunately, during the operation of fast-charging LMB, substantial amount of Joule heat generated within battery often induces inhomogeneous heat accumulation that concomitantly deteriorates the lithium dendrite growth issues, resulting in rapid capacity fading and potentially fatal thermal runaway. It is critically essential to develop advanced separator that possesses high ionic conductivity, effective dendrite inhibition and self-extinguishing property for fast-charging LMB with high-safety. Here, we report a facile approach to fabricate polyimide composite separator (denoted as PIFAP), where the polyimide fiber (PIF) membrane is sandwiched by aerogel layers of polyimide/ammonium polyphosphate composite. Notably, compared with PIF, the PIFAP features significant enhancement in flame retardance, electrolyte affinity, electrolyte adsorption and ionic conductivity. Moreover, beneficiating from the aerogel layers with uniform nanopores of ∼100 nm, the optimized PIFAP separator can enable homogeneous lithium deposition, achieving a dendrite-free lithium deposition on the surface of anode for over 1600 h, superior to the PIF and Celgard separators. The resulting “LiFePO4/PIFAP/Li” cell can exhibit a remarkably high cycling stability with 99.1 % of capacity retention after long-term cycling at a current density of 10 C.