Insights into cytokine-receptor interactions from cytokine engineering.

Cytokines exert a vast array of immunoregulatory actions critical to human biology and disease. However, the desired immunotherapeutic effects of native cytokines are often mitigated by toxicity or lack of efficacy, either of which results from cytokine receptor pleiotropy and/or undesired activation of off-target cells. As our understanding of the structural principles of cytokine-receptor interactions has advanced, mechanism-based manipulation of cytokine signaling through protein engineering has become an increasingly feasible and powerful approach. Modified cytokines, both agonists and antagonists, have been engineered with narrowed target cell specificities, and they have also yielded important mechanistic insights into cytokine biology and signaling. Here we review the theory and practice of cytokine engineering and rationalize the mechanisms of several engineered cytokines in the context of structure. We discuss specific examples of how structure-based cytokine engineering has opened new opportunities for cytokines as drugs, with a focus on the immunotherapeutic cytokines interferon, interleukin-2, and interleukin-4.

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.

A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.

Quantitative Proteomic Analysis Reveals Functional Alterations of the Peripheral Immune System in Colorectal Cancer.

Colorectal cancer (CRC) is characterized by high morbidity, high mortality, and limited response to immunotherapies. The peripheral immune system is an important component of tumor immunity, and enhancements of peripheral immunity help to suppress tumor progression. However, the functional alterations of the peripheral immune system in CRC are unclear. Here, we used mass spectrometry-based quantitative proteomics to establish a protein expression atlas for the peripheral immune system in CRC, including plasma and five types of immune cells (CD4+ T cells, CD8+ T cells, monocytes, natural killer cells, and B cells). Synthesizing the results of the multidimensional analysis, we observed an enhanced inflammatory phenotype in CRC, including elevated expression of plasma inflammatory proteins, activation of the inflammatory pathway in monocytes, and increased inflammation-related ligand-receptor interactions. Notably, we observed tumor effects on peripheral T cells, including altered cell subpopulation ratios and suppression of cell function. Suppression of CD4+ T cell function is mainly mediated by high expression levels of protein tyrosine phosphatases. Among them, the expression of protein tyrosine phosphatase receptor type J (PTPRJ) gradually increased with CRC progression; knockdown of PTPRJ in vitro could promote T cell activation, thereby enhancing peripheral immunity. We also found that the combination of leucine-rich α-2 glycoprotein 1 (LRG1) and apolipoprotein A4 (APOA4) had the best predictive ability for colorectal cancer and has the potential to be a biomarker. Overall, this study provides a comprehensive understanding of the peripheral immune system in CRC. It also offers insights regarding the potential clinical utilities of these peripheral immune characteristics as diagnostic indicators and therapeutic targets.

Two C-terminal isoforms of Aplysia tachykinin-related peptide receptors exhibit phosphorylation-dependent and phosphorylation-independent desensitization mechanisms.

Diversity, a hallmark of G protein-coupled receptor (GPCR) signaling, partly stems from alternative splicing of a single gene generating more than one isoform for a receptor. Additionally, receptor responses to ligands can be attenuated by desensitization upon prolonged or repeated ligand exposure. Both phenomena have been demonstrated and exemplified by the deuterostome tachykinin signaling system, although the role of phosphorylation in desensitization remains a subject of debate. Here, we describe the signaling system for tachykinin-related peptides (TKRPs) in a protostome, mollusk Aplysia. We cloned the Aplysia TKRP precursor, which encodes three TKRPs (apTKRP-1, apTKRP-2a, and apTKRP-2b) containing the FXGXR-amide motif. In situ hybridization and immunohistochemistry showed predominant expression of TKRP mRNA and peptide in the cerebral ganglia. TKRPs and their posttranslational modifications were observed in extracts of central nervous system ganglia using mass spectrometry. We identified two Aplysia TKRP receptors (apTKRPRs), named apTKRPR-A and apTKRPR-B. These receptors are two isoforms generated through alternative splicing of the same gene and differ only in their intracellular C termini. Structure-activity relationship analysis of apTKRP-2b revealed that both C-terminal amidation and conserved residues of the ligand are critical for receptor activation. C-terminal truncates and mutants of apTKRPRs suggested that there is a C-terminal phosphorylation-independent desensitization for both receptors. Moreover, apTKRPR-B also exhibits phosphorylation-dependent desensitization through the phosphorylation of C-terminal Ser/Thr residues. This comprehensive characterization of the Aplysia TKRP signaling system underscores the evolutionary conservation of the TKRP and TK signaling systems, while highlighting the intricacies of receptor regulation through alternative splicing and differential desensitization mechanisms.

Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA).

Sequencing-based spatial transcriptomics (ST) methods allow unbiased capturing of RNA molecules at barcoded spots, charting the distribution and localization of cell types and transcripts across a tissue. While the coarse resolution of these techniques is considered a disadvantage, we argue that the inherent proximity of transcriptomes captured on spots can be leveraged to reconstruct cellular networks. To this end, we developed ISCHIA (Identifying Spatial Co-occurrence in Healthy and InflAmed tissues), a computational framework to analyze the spatial co-occurrence of cell types and transcript species within spots. Co-occurrence analysis is complementary to differential gene expression, as it does not depend on the abundance of a given cell type or on the transcript expression levels, but rather on their spatial association in the tissue. We applied ISCHIA to analyze co-occurrence of cell types, ligands and receptors in a Visium dataset of human ulcerative colitis patients, and validated our findings at single-cell resolution on matched hybridization-based data. We uncover inflammation-induced cellular networks involving M cell and fibroblasts, as well as ligand-receptor interactions enriched in the inflamed human colon, and their associated gene signatures. Our results highlight the hypothesis-generating power and broad applicability of co-occurrence analysis on spatial transcriptomics data.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb.

BACKGROUND: Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS: Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS: In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

Decoding functional impact of epigenetic regulator mutations on ligand-receptor interaction perturbations for evaluation of cancer immunotherapy.

Cellular crosstalk mediated by ligand-receptor interactions largely complicates the tumour ecosystem, resulting in heterogeneous tumour microenvironments that affect immune response and clinical benefits from immunotherapy. Epigenetic mechanisms are pivotal to expression changes of immune-related genes and can modulate the anti-tumour immune response. However, the functional consequences of disrupted epigenetic regulators (ERs) on ligand-receptor interactions in the tumour microenvironment remain largely unexplored. Here, we proposed mutations of ERs in perturbed interactions (MERIN), a molecular network-based approach that incorporates multi-omics data, to infer the potential consequences of ER mutations on ligand-receptor interaction perturbations. Leveraging cancer genomic profiles and molecular interaction data, we comprehensively decoded the functional consequences of ER mutations on dysregulated ligand-receptor interactions across 33 cancers. The dysregulated ligand-receptor genes were indeed enriched in cancer and immune-related function. We demonstrated the potential significance of PD1-PDL1 interaction-related ER mutations in stratifying cancer patients from multiple independent data cohorts. The ER mutation group showed distinct immunological characterizations and prognoses. Furthermore, we highlighted that the ER mutations could potentially predict clinical outcomes of immunotherapy. Our computational and clinical assessment underscore the utility of MERIN for elucidating the functional relevance of ER mutations in cancer immune response, potentially aiding patients' stratification for immunotherapy.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding.

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies.

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Integrated proteomic, phosphoproteomic, and N-glycoproteomic analyses of small extracellular vesicles from C2C12 myoblasts identify specific PTM patterns in ligand-receptor interactions.

Small extracellular vesicles (sEVs) are important mediators of intercellular communication by transferring of functional components (proteins, RNAs, and lipids) to recipient cells. Some PTMs, including phosphorylation and N-glycosylation, have been reported to play important role in EV biology, such as biogenesis, protein sorting and uptake of sEVs. MS-based proteomic technology has been applied to identify proteins and PTM modifications in sEVs. Previous proteomic studies of sEVs from C2C12 myoblasts, an important skeletal muscle cell line, focused on identification of proteins, but no PTM information on sEVs proteins is available.In this study, we systematically analyzed the proteome, phosphoproteome, and N-glycoproteome of sEVs from C2C12 myoblasts with LC-MS/MS. In-depth analyses of the three proteomic datasets revealed that the three proteomes identified different catalogues of proteins, and PTMomic analysis could expand the identification of cargos in sEVs. At the proteomic level, a high percentage of membrane proteins, especially tetraspanins, was identified. The sEVs-derived phosphoproteome had a remarkably high level of tyrosine-phosphorylated sites. The tyrosine-phosphorylated proteins might be involved with EPH-Ephrin signaling pathway. At the level of N-glycoproteomics, several glycoforms, such as complex N-linked glycans and sialic acids on glycans, were enriched in sEVs. Retrieving of the ligand-receptor interaction in sEVs revealed that extracellular matrix (ECM) and cell adhesion molecule (CAM) represented the most abundant ligand-receptor pairs in sEVs. Mapping the PTM information on the ligands and receptors revealed that N-glycosylation mainly occurred on ECM and CAM proteins, while phosphorylation occurred on different categories of receptors and ligands. A comprehensive PTM map of ECM-receptor interaction and their components is also provided.In summary, we conducted a comprehensive proteomic and PTMomic analysis of sEVs of C2C12 myoblasts. Integrated proteomic, phosphoproteomic, and N-glycoproteomic analysis of sEVs might provide some insights about their specific uptake mechanism.

Endothelial-immune crosstalk contributes to vasculopathy in nonalcoholic fatty liver disease.

The top cause of mortality in patients with nonalcoholic fatty liver disease (NAFLD) is cardiovascular complications. However, mechanisms of NAFLD-associated vasculopathy remain understudied. Here, we show that blood outgrowth endothelial cells (BOECs) from NAFLD subjects exhibit global transcriptional upregulation of chemokines and human leukocyte antigens. In mouse models of diet-induced NAFLD, we confirm heightened endothelial expressions of CXCL12 in the aortas and the liver vasculatures, and increased retention of infiltrated leukocytes within the vessel walls. To elucidate endothelial-immune crosstalk, we performed immunoprofiling by single-cell analysis, uncovering T cell intensification in NAFLD patients. Functionally, treatment with a CXCL12-neutralizing antibody is effective at moderating the enhanced chemotactic effect of NAFLD BOECs in recruiting CD8+ T lymphocytes. Interference with the CXCL12-CXCR4 axis using a CXCR4 antagonist also averts the impact of immune cell transendothelial migration and restores endothelial barrier integrity. Clinically, we detect threefold more circulating damaged endothelial cells in NAFLD patients than in healthy controls. Our work provides insight into the modulation of interactions with effector immune cells to mitigate endothelial injury in NAFLD.

Mannose Ligands for Mannose Receptor Targeting.

The mannose receptor (MR, CD 206) is an endocytic receptor primarily expressed by macrophages and dendritic cells, which plays a critical role in both endocytosis and antigen processing and presentation. MR carbohydrate recognition domains (CRDs) exhibit a high binding affinity for branched and linear oligosaccharides. Furthermore, multivalent mannose presentation on the various templates like peptides, proteins, polymers, micelles, and dendrimers was proven to be a valuable approach for the selective and efficient delivery of various therapeutically active agents to MR. This review provides a detailed account of the most relevant and recent aspects of the synthesis and application of mannosylated bioactive formulations for MR-mediated delivery in treatments of cancer and other infectious diseases. It further highlights recent findings related to the necessary structural features of the mannose-containing ligands for successful binding to the MR.

AI protein structure prediction-based modeling and mutagenesis of a protostome receptor and peptide ligands reveal key residues for their interaction.

The protostome leucokinin (LK) signaling system, including LK peptides and their G protein-coupled receptors, has been characterized in several species. Despite the progress, molecular mechanisms governing LK peptide-receptor interactions remain to be elucidated. Previously, we identified a precursor protein for Aplysia leucokinin-like peptides (ALKs) that contains the greatest number of amidated peptides among LK precursors in all species identified so far. Here, we identified the first ALK receptor from Aplysia, ALKR. We used cell-based IP1 activation assays to demonstrate that two ALK peptides with the most copies, ALK1 and ALK2, activated ALKR with high potencies. Other endogenous ALK-derived peptides bearing the FXXWX-amide motif also activated ALKR to various degrees. Our examination of cross-species activity of ALKs with the Anopheles LK receptor was consistent with a critical role for the FXXWX-amide motif in receptor activity. Furthermore, we showed, through alanine substitution of ALK1, the highly conserved phenylalanine (F), tryptophan (W), and C-terminal amidation were each essential for receptor activation. Finally, we used an artificial intelligence-based protein structure prediction server (Robetta) and Autodock Vina to predict the ligand-bound conformation of ALKR. Our model predicted several interactions (i.e., hydrophobic interactions, hydrogen bonds, and amide-pi stacking) between ALK peptides and ALKR, and several of our substitution and mutagenesis experiments were consistent with the predicted model. In conclusion, our results provide important information defining possible interactions between ALK peptides and their receptors. The workflow utilized here may be useful for studying other ligand-receptor interactions for a neuropeptide signaling system, particularly in protostomes.

CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice.

Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.

Peptide signaling through leucine-rich repeat receptor kinases: insight into land plant evolution.

Multicellular organisms need mechanisms for communication between cells so that they can fulfill their purpose in the organism as a whole. Over the last two decades, several small post-translationally modified peptides (PTMPs) have been identified as components of cell-to-cell signaling modules in flowering plants. Such peptides most often influence growth and development of organs not universally conserved among land plants. PTMPs have been matched to subfamily XI leucine-rich repeat receptor-like kinases with > 20 repeats. Phylogenetic analyses, facilitated by recently published genomic sequences of non-flowering plants, have identified seven clades of such receptors with a history back to the common ancestor of bryophytes and vascular plants. This raises a number of questions: When did peptide signaling arise during land plant evolution? Have orthologous peptide-receptor pairs preserved their biological functions? Has peptide signaling contributed to major innovations, such as stomata, vasculature, roots, seeds, and flowers? Using genomic, genetic, biochemical, and structural data and non-angiosperm model species, it is now possible to address these questions. The vast number of peptides that have not yet found their partners suggests furthermore that we have far more to learn about peptide signaling in the coming decades.

Multivalent calix[4]arene-based mannosylated dendrons as new FimH ligands and inhibitors.

We report the synthesis and biological characterization of a novel class of multivalent glycoconjugates as hit compounds for the design of new antiadhesive therapies against urogenital tract infections (UTIs) caused by uropathogenic E. coli strains (UPEC). The first step of UTIs is the molecular recognition of high mannose N-glycan expressed on the surface of urothelial cells by the bacterial lectin FimH, allowing the pathogen adhesion required for mammalian cell invasion. The inhibition of FimH-mediated interactions is thus a validated strategy for the treatment of UTIs. To this purpose, we designed and synthesized d-mannose multivalent dendrons supported on a calixarene core introducing a significant structural change from a previously described family of dendrimers bearing the same dendrons units on a flexible pentaerythritol scaffold core. The new molecular architecture increased the inhibitory potency against FimH-mediated adhesion processes by about 16 times, as assessed by yeast agglutination assay. Moreover, the direct molecular interaction of the new compounds with FimH protein was assessed by on-cell NMR experiments acquired in the presence of UPEC cells.

Engineering a potent receptor superagonist or antagonist from a novel IL-6 family cytokine ligand.

Interleukin-6 (IL-6) family cytokines signal through multimeric receptor complexes, providing unique opportunities to create novel ligand-based therapeutics. The cardiotrophin-like cytokine factor 1 (CLCF1) ligand has been shown to play a role in cancer, osteoporosis, and atherosclerosis. Once bound to ciliary neurotrophic factor receptor (CNTFR), CLCF1 mediates interactions to coreceptors glycoprotein 130 (gp130) and leukemia inhibitory factor receptor (LIFR). By increasing CNTFR-mediated binding to these coreceptors we generated a receptor superagonist which surpassed the potency of natural CNTFR ligands in neuronal signaling. Through additional mutations, we generated a receptor antagonist with increased binding to CNTFR but lack of binding to the coreceptors that inhibited tumor progression in murine xenograft models of nonsmall cell lung cancer. These studies further validate the CLCF1-CNTFR signaling axis as a therapeutic target and highlight an approach of engineering cytokine activity through a small number of mutations.

Mechanism and site of action of big dynorphin on ASIC1a.

Acid-sensing ion channels (ASICs) are proton-gated cation channels that contribute to neurotransmission, as well as initiation of pain and neuronal death following ischemic stroke. As such, there is a great interest in understanding the in vivo regulation of ASICs, especially by endogenous neuropeptides that potently modulate ASICs. The most potent endogenous ASIC modulator known to date is the opioid neuropeptide big dynorphin (BigDyn). BigDyn is up-regulated in chronic pain and increases ASIC-mediated neuronal death during acidosis. Understanding the mechanism and site of action of BigDyn on ASICs could thus enable the rational design of compounds potentially useful in the treatment of pain and ischemic stroke. To this end, we employ a combination of electrophysiology, voltage-clamp fluorometry, synthetic BigDyn analogs, and noncanonical amino acid-mediated photocrosslinking. We demonstrate that BigDyn binding results in an ASIC1a closed resting conformation that is distinct from open and desensitized states induced by protons. Using alanine-substituted BigDyn analogs, we find that the BigDyn modulation of ASIC1a is primarily mediated through electrostatic interactions of basic amino acids in the BigDyn N terminus. Furthermore, neutralizing acidic amino acids in the ASIC1a extracellular domain reduces BigDyn effects, suggesting a binding site at the acidic pocket. This is confirmed by photocrosslinking using the noncanonical amino acid azidophenylalanine. Overall, our data define the mechanism of how BigDyn modulates ASIC1a, identify the acidic pocket as the binding site for BigDyn, and thus highlight this cavity as an important site for the development of ASIC-targeting therapeutics.

Molecular Dynamics Investigations of Human DNA-Topoisomerase I Interacting with Novel Dewar Valence Photo-Adducts: Insights into Inhibitory Activity.

Chronic exposure to ultraviolet (UV) radiation is known to induce the formation of DNA photo-adducts, including cyclobutane pyrimidine dimers (CPDs) and Dewar valence derivatives (DVs). While CPDs usually occur at higher frequency than DVs, recent studies have shown that the latter display superior selectivity and significant stability in interaction with the human DNA/topoisomerase 1 complex (TOP1). With the aim to deeply investigate the mechanism of interaction of DVs with TOP1, we report here four all-atom molecular dynamic simulations spanning one microsecond. These simulations are focused on the stability and conformational changes of two DNA/TOP1-DV complexes in solution, the data being compared with the biomimetic thymine dimer counterparts. Results from root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) analyses unequivocally confirmed increased stability of the DNA/TOP1-DV complexes throughout the simulation duration. Detailed interaction analyses, uncovering the presence of salt bridges, hydrogen bonds, water-mediated interactions, and hydrophobic interactions, as well as pinpointing the non-covalent interactions within the complexes, enabled the identification of specific TOP1 residues involved in the interactions over time and suggested a potential TOP1 inhibition mechanism in action.