Novel peptide inhibitor of human tumor necrosis factor-α has antiarthritic activity.

The inhibition of tumor necrosis factor (TNF)-α trimer formation renders it inactive for binding to its receptors, thus mitigating the vicious cycle of inflammation. We designed a peptide (PIYLGGVFQ) that simulates a sequence strand of human TNFα monomer using a series of in silico methods, such as active site finding (Acsite), protein-protein interaction (PPI), docking studies (GOLD and Flex-X) followed by molecular dynamics (MD) simulation studies. The MD studies confirmed the intermolecular interaction of the peptide with the TNFα. Fluorescence-activated cell sorting and fluorescence microscopy revealed that the peptide effectively inhibited the binding of TNF to the cell surface receptors. The cell culture assays showed that the peptide significantly inhibited the TNFα-mediated cell death. In addition, the nuclear translocation of the nuclear factor kappa B (NFκB) was significantly suppressed in the peptide-treated A549 cells, as observed in immunofluorescence and gel mobility-shift assays. Furthermore, the peptide protected against joint damage in the collagen-induced arthritis (CIA) mouse model, as revealed in the micro focal-CT scans. In conclusion, this TNFα antagonist would be helpful for the prevention and repair of inflammatory bone destruction and subsequent loss in the mouse model of CIA as well as human rheumatoid arthritis (RA) patients. This calls upon further clinical investigation to utilize its potential effect as an antiarthritic drug.

Characterization of metallothionein genes from Broussonetia papyrifera: metal binding and heavy metal tolerance mechanisms.

BACKGROUND: Broussonetia papyrifera is an economically significant tree with high utilization value, yet its cultivation is often constrained by soil contamination with heavy metals (HMs). Effective scientific cultivation management, which enhances the yield and quality of B. papyrifera, necessitates an understanding of its regulatory mechanisms in response to HM stress. RESULTS: Twelve Metallothionein (MT) genes were identified in B. papyrifera. Their open reading frames ranged from 186 to 372 bp, encoding proteins of 61 to 123 amino acids with molecular weights between 15,473.77 and 29,546.96 Da, and theoretical isoelectric points from 5.24 to 5.32. Phylogenetic analysis classified these BpMTs into three subclasses: MT1, MT2, and MT3, with MT2 containing seven members and MT3 only one. The expression of most BpMT genes was inducible by Cd, Mn, Cu, Zn, and abscisic acid (ABA) treatments, particularly BpMT2e, BpMT2d, BpMT2c, and BpMT1c, which showed significant responses and warrant further study. Yeast cells expressing these BpMT genes exhibited enhanced tolerance to Cd, Mn, Cu, and Zn stresses compared to control cells. Yeasts harboring BpMT1c, BpMT2e, and BpMT2d demonstrated higher accumulation of Cd, Cu, Mn, and Zn, suggesting a chelation and binding capacity of BpMTs towards HMs. Site-directed mutagenesis of cysteine (Cys) residues indicated that mutations in the C domain of type 1 BpMT led to increased sensitivity to HMs and reduced HM accumulation in yeast cells; While in type 2 BpMTs, the contribution of N and C domain to HMs' chelation possibly corelated to the quantity of Cys residues. CONCLUSION: The BpMT genes are crucial in responding to diverse HM stresses and are involved in ABA signaling. The Cys-rich domains of BpMTs are pivotal for HM tolerance and chelation. This study offers new insights into the structure-function relationships and metal-binding capabilities of type-1 and - 2 plant MTs, enhancing our understanding of their roles in plant adaptation to HM stresses.

TDP43 interacts with MLH1 and MSH6 proteins in a DNA damage-inducible manner.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects the motor neuron. One aspect of the neuropathology involved in ALS includes increased genomic damage and impaired DNA repair capability. The TAR-DNA binding protein 43 (TDP43) has been associated with both sporadic and familial forms of ALS, and is typically observed as cytosolic mislocalization of protein aggregates, termed TDP43 proteinopathy. TDP43 is a ubiquitous RNA/DNA binding protein with functional implications in a wide range of disease processes, including the repair of DNA double-strand breaks (DSBs). While TDP43 is widely known to regulate RNA metabolism, our lab has reported it also functions directly at the protein level to facilitate DNA repair. Here, we show that the TDP43 protein interacts with DNA mismatch repair (MMR) proteins MLH1 and MSH6 in a DNA damage-inducible manner. We utilized differentiated SH-SY5Y neuronal cultures to identify this inducible relationship using complementary approaches of proximity ligation assay (PLA) and co-immunoprecipitation (CoIP) assay. We observed that signals of TDP43 interaction with MLH1 and MSH6 increased significantly following a 2 h treatment of 10 µM methylmethanesulfonate (MMS), a DNA alkylating agent used to induce MMR repair. Likewise, we observed this effect was abolished in cell lines treated with siRNA directed against TDP43. Finally, we demonstrated these protein interactions were significantly increased in lumbar spinal cord samples of ALS-affected patients compared to age-matched controls. These results will inform our future studies to understand the mechanisms and consequences of this TDP43-MMR interaction in the context of ALS-affected neurons.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

GLUT1 promotes cell proliferation via binds and stabilizes phosphorylated EGFR in lung adenocarcinoma.

BACKGROUND: While previous studies have primarily focused on Glucose transporter type 1 (GLUT1) related glucose metabolism signaling, we aim to discover if GLUT1 promotes tumor progression through a non-metabolic pathway. METHODS: The RNA-seq and microarray data were comprehensively analyzed to evaluate the significance of GLUT1 expression in lung adenocarcinoma (LUAD). The cell proliferation, colony formation, invasion, and migration were used to test GLUT1 's oncogenic function. Co-immunoprecipitation and mass spectrum (MS) were used to uncover potential GLUT1 interacting proteins. RNA-seq, DIA-MS, western blot, and qRT-PCR to probe the change of gene and cell signaling pathways. RESULTS: We found that GLUT1 is highly expressed in LUAD, and higher expression is related to poor patient survival. GLUT1 knockdown caused a decrease in cell proliferation, colony formation, migration, invasion, and induced apoptosis in LUAD cells. Mechanistically, GLUT1 directly interacted with phosphor-epidermal growth factor receptor (p-EGFR) and prevented EGFR protein degradation via ubiquitin-mediated proteolysis. The GLUT1 inhibitor WZB117 can increase the sensitivity of LUAD cells to EGFR-tyrosine kinase inhibitors (TKIs) Gefitinib. CONCLUSIONS: GLUT1 expression is higher in LUAD and plays an oncogenic role in lung cancer progression. Combining GLUT1 inhibitors and EGFR-TKIs could be a potential therapeutic option for LUAD treatment.

Profilin affects microtubule dynamics via actin.

Profilin binds microtubules in vitro. However, a new study by Vitriol and colleagues (https://doi.org/10.1083/jcb.202309097) now suggests that effects of profilin on microtubule dynamics in cells are indirect and result from its impact on actin dynamics rather than its direct binding to microtubules.

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes.

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

The PCNA-Pol δ complex couples lagging strand DNA synthesis to parental histone transfer for epigenetic inheritance.

Inheritance of epigenetic information is critical for maintaining cell identity. The transfer of parental histone H3-H4 tetramers, the primary carrier of epigenetic modifications on histone proteins, represents a crucial yet poorly understood step in the inheritance of epigenetic information. Here, we show the lagging strand DNA polymerase, Pol δ, interacts directly with H3-H4 and that the interaction between Pol δ and the sliding clamp PCNA regulates parental histone transfer to lagging strands, most likely independent of their roles in DNA synthesis. When combined, mutations at Pol δ and Mcm2 that compromise parental histone transfer result in a greater reduction in nucleosome occupancy at nascent chromatin than mutations in either alone. Last, PCNA contributes to nucleosome positioning on nascent chromatin. On the basis of these results, we suggest that the PCNA-Pol δ complex couples lagging strand DNA synthesis to parental H3-H4 transfer, facilitating epigenetic inheritance.

How sensor Amt-like proteins integrate ammonium signals.

Unlike aquaporins or potassium channels, ammonium transporters (Amts) uniquely discriminate ammonium from potassium and water. This feature has certainly contributed to their repurposing as ammonium receptors during evolution. Here, we describe the ammonium receptor Sd-Amt1, where an Amt module connects to a cytoplasmic diguanylate cyclase transducer module via an HAMP domain. Structures of the protein with and without bound ammonium were determined to 1.7- and 1.9-Ångstrom resolution, depicting the ON and OFF states of the receptor and confirming the presence of a binding site for two ammonium cations that is pivotal for signal perception and receptor activation. The transducer domain was disordered in the crystals, and an AlphaFold2 prediction suggests that the helices linking both domains are flexible. While the sensor domain retains the trimeric fold formed by all Amt family members, the HAMP domains interact as pairs and serve to dimerize the transducer domain upon activation.

Allosteric activation of VCP, an AAA unfoldase, by small molecule mimicry.

The loss of function of AAA (ATPases associated with diverse cellular activities) mechanoenzymes has been linked to diseases, and small molecules that activate these proteins can be powerful tools to probe mechanisms and test therapeutic hypotheses. Unlike chemical inhibitors that can bind a single conformational state to block enzyme function, activator binding must be permissive to different conformational states needed for mechanochemistry. However, we do not know how AAA proteins can be activated by small molecules. Here, we focus on valosin-containing protein (VCP)/p97, an AAA unfoldase whose loss of function has been linked to protein aggregation-based disorders, to identify druggable sites for chemical activators. We identified VCP ATPase Activator 1 (VAA1), a compound that dose-dependently stimulates VCP ATPase activity up to ~threefold. Our cryo-EM studies resulted in structures (ranging from ~2.9 to 3.7 Å-resolution) of VCP in apo and ADP-bound states and revealed that VAA1 binds an allosteric pocket near the C-terminus in both states. Engineered mutations in the VAA1-binding site confer resistance to VAA1, and furthermore, modulate VCP activity. Mutation of a phenylalanine residue in the VCP C-terminal tail that can occupy the VAA1 binding site also stimulates ATPase activity, suggesting that VAA1 acts by mimicking this interaction. Together, our findings uncover a druggable allosteric site and a mechanism of enzyme regulation that can be tuned through small molecule mimicry.

IpaA reveals distinct modes of vinculin activation during Shigella invasion and cell-matrix adhesion.

Vinculin is a cytoskeletal linker strengthening cell adhesion. The Shigella IpaA invasion effector binds to vinculin to promote vinculin supra-activation associated with head-domain-mediated oligomerization. Our study investigates the impact of mutations of vinculin D1D2 subdomains' residues predicted to interact with IpaA VBS3. These mutations affected the rate of D1D2 trimer formation with distinct effects on monomer disappearance, consistent with structural modeling of a closed and open D1D2 conformer induced by IpaA. Notably, mutations targeting the closed D1D2 conformer significantly reduced Shigella invasion of host cells as opposed to mutations targeting the open D1D2 conformer and later stages of vinculin head-domain oligomerization. In contrast, all mutations affected the formation of focal adhesions (FAs), supporting the involvement of vinculin supra-activation in this process. Our findings suggest that IpaA-induced vinculin supra-activation primarily reinforces matrix adhesion in infected cells, rather than promoting bacterial invasion. Consistently, shear stress studies pointed to a key role for IpaA-induced vinculin supra-activation in accelerating and strengthening cell-matrix adhesion.

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

Structural and functional characterization of nanobodies that neutralize Omicron variants of SARS-CoV-2.

The Omicron strains of SARS-CoV-2 pose a significant challenge to the development of effective antibody-based treatments as immune evasion has compromised most available immune therapeutics. Therefore, in the 'arms race' with the virus, there is a continuing need to identify new biologics for the prevention or treatment of SARS-CoV-2 infections. Here, we report the isolation of nanobodies that bind to the Omicron BA.1 spike protein by screening nanobody phage display libraries previously generated from llamas immunized with either the Wuhan or Beta spike proteins. The structure and binding properties of three of these nanobodies (A8, H6 and B5-5) have been characterized in detail providing insight into their binding epitopes on the Omicron spike protein. Trimeric versions of H6 and B5-5 neutralized the SARS-CoV-2 variant of concern BA.5 both in vitro and in the hamster model of COVID-19 following nasal administration. Thus, either alone or in combination could serve as starting points for the development of new anti-viral immunotherapeutics.

Role of Mod(mdg4)-67.2 Protein in Interactions between Su(Hw)-Dependent Complexes and Their Recruitment to Chromatin.

Su(Hw) belongs to the class of proteins that organize chromosome architecture, determine promoter activity, and participate in formation of the boundaries/insulators between the regulatory domains. This protein contains a cluster of 12 zinc fingers of the C2H2 type, some of which are responsible for binding to the consensus site. The Su(Hw) protein forms complex with the Mod(mdg4)-67.2 and the CP190 proteins, where the last one binds to all known Drosophila insulators. To further study functioning of the Su(Hw)-dependent complexes, we used the previously described su(Hw)E8 mutation with inactive seventh zinc finger, which produces mutant protein that cannot bind to the consensus site. The present work shows that the Su(Hw)E8 protein continues to directly interact with the CP190 and Mod(mdg4)-67.2 proteins. Through interaction with Mod(mdg4)-67.2, the Su(Hw)E8 protein can be recruited into the Su(Hw)-dependent complexes formed on chromatin and enhance their insulator activity. Our results demonstrate that the Su(Hw) dependent complexes without bound DNA can be recruited to the Su(Hw) binding sites through the specific protein-protein interactions that are stabilized by Mod(mdg4)-67.2.

Highly biased agonism for GPCR ligands via nanobody tethering.

Ligand-induced activation of G protein-coupled receptors (GPCRs) can initiate signaling through multiple distinct pathways with differing biological and physiological outcomes. There is intense interest in understanding how variation in GPCR ligand structure can be used to promote pathway selective signaling ("biased agonism") with the goal of promoting desirable responses and avoiding deleterious side effects. Here we present an approach in which a conventional peptide ligand for the type 1 parathyroid hormone receptor (PTHR1) is converted from an agonist which induces signaling through all relevant pathways to a compound that is highly selective for a single pathway. This is achieved not through variation in the core structure of the agonist, but rather by linking it to a nanobody tethering agent that binds with high affinity to a separate site on the receptor not involved in signal transduction. The resulting conjugate represents the most biased agonist of PTHR1 reported to date. This approach holds promise for facile generation of pathway selective ligands for other GPCRs.

Native and tagged CENP-A histones are functionally inequivalent.

BACKGROUND: Over the past several decades, the use of biochemical and fluorescent tags has elucidated mechanistic and cytological processes that would otherwise be impossible. The challenging nature of certain nuclear proteins includes low abundancy, poor antibody recognition, and transient dynamics. One approach to get around those issues is the addition of a peptide or larger protein tag to the target protein to improve enrichment, purification, and visualization. However, many of these studies were done under the assumption that tagged proteins can fully recapitulate native protein function. RESULTS: We report that when C-terminally TAP-tagged CENP-A histone variant is introduced, it undergoes altered kinetochore protein binding, differs in post-translational modifications (PTMs), utilizes histone chaperones that differ from that of native CENP-A, and can partially displace native CENP-A in human cells. Additionally, these tagged CENP-A-containing nucleosomes have reduced centromeric incorporation at early G1 phase and poorly associates with linker histone H1.5 compared to native CENP-A nucleosomes. CONCLUSIONS: These data suggest expressing tagged versions of histone variant CENP-A may result in unexpected utilization of non-native pathways, thereby altering the biological function of the histone variant.

High-confidence placement of low-occupancy fragments into electron density using the anomalous signal of sulfur and halogen atoms.

Fragment-based drug design using X-ray crystallography is a powerful technique to enable the development of new lead compounds, or probe molecules, against biological targets. This study addresses the need to determine fragment binding orientations for low-occupancy fragments with incomplete electron density, an essential step before further development of the molecule. Halogen atoms play multiple roles in drug discovery due to their unique combination of electronegativity, steric effects and hydrophobic properties. Fragments incorporating halogen atoms serve as promising starting points in hit-to-lead development as they often establish halogen bonds with target proteins, potentially enhancing binding affinity and selectivity, as well as counteracting drug resistance. Here, the aim was to unambiguously identify the binding orientations of fragment hits for SARS-CoV-2 nonstructural protein 1 (nsp1) which contain a combination of sulfur and/or chlorine, bromine and iodine substituents. The binding orientations of carefully selected nsp1 analogue hits were focused on by employing their anomalous scattering combined with Pan-Dataset Density Analysis (PanDDA). Anomalous difference Fourier maps derived from the diffraction data collected at both standard and long-wavelength X-rays were compared. The discrepancies observed in the maps of iodine-containing fragments collected at different energies were attributed to site-specific radiation-damage stemming from the strong X-ray absorption of I atoms, which is likely to cause cleavage of the C-I bond. A reliable and effective data-collection strategy to unambiguously determine the binding orientations of low-occupancy fragments containing sulfur and/or halogen atoms while mitigating radiation damage is presented.

Analyzing noncovalent interactions between notoginseng saponins and lysozyme by deposition scanning intensity fading MALDI-TOF mass spectrometry.

Analysis of noncovalent interactions between natural products and proteins is important for rapid screening of active ingredients and understanding their pharmacological activities. In this work, the intensity fading MALDI-TOF mass spectrometry (IF-MALDI-MS) method with improved reproducibility was implemented to investigate the binding interactions between saponins from Panax notoginseng and lysozyme. The benchmark IF-MALDI-MS experiment was established using N,N',N″-triacetylchitotriose-lysozyme as a model system. The reproducibility of ion intensities in IF-MALDI-MS was improved by scanning the whole sample deposition with a focused laser beam. The relative standard deviation (RSD) of deposition scanning IF-MALDI-MS is 5.7%. Similar decay trends of the relative intensities of notoginseng saponins against increasing amounts of lysozyme were observed for all six notoginseng saponins. The half-maximal fading concentration (FC50) was calculated to quantitatively characterize the binding affinity of each ligand based on the decay curve. According to the FC50 values obtained, the binding affinities of the six notoginseng saponins were evaluated in the following order: notoginsenoside S > notoginsenoside Fc > ginsenoside Rb1 > ginsenoside Rd > notoginsenoside Ft1 > ginsenoside Rg1. The binding order was in accordance with molecular docking studies, which showed hydrogen bonding might play a key role in stabilizing the binding interaction. Our results demonstrated that deposition scanning IF-MALDI-MS can provide valuable information on the noncovalent interactions between ligands and proteins.

Atomistic description of the OCTN1 recognition mechanism via in silico methods.

The Organic Cation Transporter Novel 1 (OCTN1), also known as SLC22A4, is widely expressed in various human tissues, and involved in numerous physiological and pathological processes remains. It facilitates the transport of organic cations, zwitterions, with selectivity for positively charged solutes. Ergothioneine, an antioxidant compound, and acetylcholine (Ach) are among its substrates. Given the lack of experimentally solved structures of this protein, this study aimed at generating a reliable 3D model of OCTN1 to shed light on its substrate-binding preferences and the role of sodium in substrate recognition and transport. A chimeric model was built by grafting the large extracellular loop 1 (EL1) from an AlphaFold-generated model onto a homology model. Molecular dynamics simulations revealed domain-specific mobility, with EL1 exhibiting the highest impact on overall stability. Molecular docking simulations identified cytarabine and verapamil as highest affinity ligands, consistent with their known inhibitory effects on OCTN1. Furthermore, MM/GBSA analysis allowed the categorization of substrates into weak, good, and strong binders, with molecular weight strongly correlating with binding affinity to the recognition site. Key recognition residues, including Tyr211, Glu381, and Arg469, were identified through interaction analysis. Ach demonstrated a low interaction energy, supporting the hypothesis of its one-directional transport towards to outside of the membrane. Regarding the role of sodium, our model suggested the involvement of Glu381 in sodium binding. Molecular dynamics simulations of systems at increasing levels of Na+ concentrations revealed increased sodium occupancy around Glu381, supporting experimental data associating Na+ concentration to molecule transport. In conclusion, this study provides valuable insights into the 3D structure of OCTN1, its substrate-binding preferences, and the role of sodium in the recognition. These findings contribute to the understanding of OCTN1 involvement in various physiological and pathological processes and may have implications for drug development and disease management.

The human alpha7 nicotinic acetylcholine receptor is a host target for the rabies virus glycoprotein.

The rabies virus enters the nervous system by interacting with several molecular targets on host cells to modify behavior and trigger receptor-mediated endocytosis of the virion by poorly understood mechanisms. The rabies virus glycoprotein (RVG) interacts with the muscle acetylcholine receptor and the neuronal α4ß2 subtype of the nicotinic acetylcholine receptor (nAChR) family by the putative neurotoxin-like motif. Given that the neurotoxin-like motif is highly homologous to the α7 nAChR subtype selective snake toxin α-bungarotoxin (αBTX), other nAChR subtypes are likely involved. The purpose of this study is to determine the activity of the RVG neurotoxin-like motif on nAChR subtypes that are expressed in brain regions involved in rabid animal behavior. nAChRs were expressed in Xenopus laevis oocytes, and two-electrode voltage clamp electrophysiology was used to collect concentration-response data to measure the functional effects. The RVG peptide preferentially and completely inhibits α7 nAChR ACh-induced currents by a competitive antagonist mechanism. Tested heteromeric nAChRs are also inhibited, but to a lesser extent than the α7 subtype. Residues of the RVG peptide with high sequence homology to αBTX and other neurotoxins were substituted with alanine. Altered RVG neurotoxin-like peptides showed that residues phenylalanine 192, arginine 196, and arginine 199 are important determinants of RVG peptide apparent potency on α7 nAChRs, while serine 195 is not. The evaluation of the rabies ectodomain reaffirmed the observations made with the RVG peptide, illustrating a significant inhibitory impact on α7 nAChR with potency in the nanomolar range. In a mammalian cell culture model of neurons, we confirm that the RVG peptide binds preferentially to cells expressing the α7 nAChR. Defining the activity of the RVG peptide on nAChRs expands our understanding of basic mechanisms in host-pathogen interactions that result in neurological disorders.