ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers

In silico design of ankyrin repeat proteins that bind to the insulin-like growth factor type 1 receptor

Ankyrins are proteins widely distributed in nature that mediate protein‒protein interactions. Owing to their outstanding stability and ability to recognize targets, ankyrins have been used as therapeutic and diagnostic tools in several diseases, including cancer. Insulin-like growth factor type 1 receptor (IGF-1R) is overexpressed in a variety of cancers, making it an attractive molecular target. Advances in anticancer treatment have focused on inhibiting the binding between IGF-1R and its natural ligand, IGF1. In this work, three ankyrins were designed to interact with IGF-1R, and molecular models using AlphaFold were generated. The designed ankyrin sequences included amino acids of IGF1 that recognize IGF-1R: a two-module ankyrin (DAN2SON), a loop ankyrin (Loop-DAN2SON) and a bispecific ankyrin (BI-DAN2SON-D1). Models with the best results from the predicted local distance difference test and predicted assigned error values were used to perform rigid binding tests with the ClusPro server. The best complexes were selected based on the binding energies. Further analysis of the interactions was performed with the PDBsum server. The three IGF1-R complexes showed negative free binding energies, indicating that the binding of these proteins could be energetically favorable. Molecular binding assays revealed that DAN2SON and Loop-DAN2SON bind to IGF-1R at the natural ligand binding site via hydrogen bonds and salt bridge interactions. This work shows that using artificial intelligence to generate protein models allows prediction of interactions between ankyrins and the IGF-1R, to be confirmed in subsequent studies using both in vitro and in vivo models.

Computer aided design of CGA-N9 derived peptides based on oligopeptide transporters and their antifungal evaluations

CGA-N9 is an antifungal peptide that primarily targets Candida spp. with a mild activity. Our preceding research confirmed that the CGA-N9 crosses cell membrane with the assistance of C. tropicalis oligopeptide transporter (CtOPT) -1 and - 9. In this study, CGA-N9-derived peptides were designed following the molecular docking results with CtOPT-1 and -9. Compared with CGAN9, they exhibit higher transmembrane efficiency with the assistance of CtOPT-1 during the early phase of transmembrane processes and CtOPT-9 in the late phase. And they displayed significantly enhanced antifungal activity, with lower minimum inhibitory concentrations (MICs) against C. tropicalis, C. albicans, and C. parapsilosis, as well as improved biosafety. Among them, CGAN93 was the most optimizing, with a therapeutic index of 145.33. Furthermore, in a mouse model of systemic candidiasis, CGAN93 demonstrated a therapeutic effect comparable to fluconazole, significantly improving the survival rate of mice, attenuating organ damage, and enhancing the immune organ index. In conclusion, OPTs-based computer aided design is an effective strategy for enhancing the activities of antimicrobial peptides (AMPs) by improving transmembrane transport efficiency. CGAN93 is a promising drug candidate for treating Candidiasis.

A comprehensive primer and review of PROTACs and their In Silico design

The cutting-edge technique of Proteolysis Targeting Chimeras, or PROTACs, has gained significant attention as a viable approach for specific protein degradation. This innovative technology has vast potential in fields such as cancer therapy and drug development. The development of effective and specific therapies for a range of diseases is within reach with PROTACs, which can target previously "undruggable" proteins while circumventing the off-target effects of conventional small molecule inhibitors. This manuscript aims to discuss the application of in silico techniques to the design of these groundbreaking molecules and develop PROTAC complexes, in order to identify potential PROTAC candidates with favorable drug-like properties. Additionally, this manuscript reviews the strengths and weaknesses of these methods to demonstrate their utility and highlights the challenges and future prospects of in silico PROTAC design. The present review provides a valuable and beginner-friendly resource for researchers and drug developers interested in using in silico methods for PROTAC design, specifically ternary structure prediction.

Effect of exogenous protein crosslinking on the physicochemical properties and in vitro digestibility of corn starch

Starch is a primary energy source of human diet. Its physicochemical properties and digestibility can be improved by incorporating exogenous protein. In this study, mung bean protein isolate was covalently crosslinked using transglutaminase and proanthocyanidin to create crosslinked mung bean protein isolate. This modified protein was combined with corn starch to form crosslinked mung bean protein isolate-corn starch composite samples. Results demonstrated that these composite samples exhibited superior physicochemical properties, including reduced swelling capacity, enhanced freeze-thaw stability, improved thermostability, and enhanced antioxidant properties. During in vitro digestion, the improved corn starch digestibility was attributed to two factors: first, hydrogen bonding and electrostatic interactions between crosslinked mung bean protein isolate and corn starch; and second, the synergistic crosslinking of transglutaminase and proanthocyanidin promoting the formation of a stable protein network of mung bean protein isolate, serving as a physical barrier to protect corn starch. After co-treatment with transglutaminase and proanthocyanidin, significant changes of mung bean protein isolate occurred in their secondary and tertiary structures, enhancing its protein network strength, thereby improving the physicochemical properties of corn starch. These findings propose a new strategy for reducing rapidly digestible starch and provide a theoretical foundation for developing low glycemic index starch foods.

AI-Assisted Protein-Peptide Complex Prediction in a Practical Setting

Accurate prediction of protein-peptide complex structures plays a critical role in structure-based drug design, including antibody design. Most peptide-docking benchmark studies were conducted using crystal structures of protein-peptide complexes; as such, the performance of the current peptide docking tools in the practical setting is unknown. Here, the practical setting implies there are no crystal or other experimental structures for the complex, nor for the receptor and peptide. In this work, we have developed a practical docking protocol that incorporated two famous machine learning models, AlphaFold 2 for structural prediction and ANI-2x for ab initio potential prediction, to achieve a high success rate in modeling protein-peptide complex structures. The docking protocol consists of three major stages. In the first stage, the 3D structure of the receptor is predicted by AlphaFold 2 using the monomer mode, and that of the peptide is predicted by AlphaFold 2 using the multimer mode. We found that it is essential to include the receptor information to generate a high-quality 3D structure of the peptide. In the second stage, rigid protein-peptide docking is performed using ZDOCK software. In the last stage, the top 10 docking poses are relaxed and refined by ANI-2x in conjunction with our in-house geometry optimization algorithm-conjugate gradient with backtracking line search (CG-BS). CG-BS was developed by us to more efficiently perform geometry optimization, which takes the potential and force directly from ANI-2x machine learning models. The docking protocol achieved a very encouraging performance for a set of 62 very challenging protein-peptide systems which had an overall success rate of 34% if only the top 1 docking poses were considered. This success rate increased to 45% if the top 3 docking poses were considered. It is emphasized that this encouraging protein-peptide docking performance was achieved without using any crystal or experimental structures.

Identification and characterization of a target antigen recognized by the monoclonal antibody against Opisthorchis viverrini

Opisthorchis viverrini (Ov) infection caused opisthorchiasis, which posed an important risk for the development of cholangiocarcinoma (CCA). Therefore, it is crucial to focus on the primary prevention and control of opisthorchiasis in order to control CCA effectively in Thailand and other endemic regions. A recent diagnostic method of antigen detection using monoclonal antibody-based enzyme-linked immunosorbent assay (mAb-ELISA) has the potential for rapid mass screening of opisthorchiasis. Nevertheless, the specific antigen(s) in Ov adult worms recognized by mAb have not been determined. In this study, we aimed to identify and characterize the target molecule of our in-house Ov-specific monoclonal antibody (mAb KKU505). The specific antigenic band formed by the reaction of Ov adult worm extract and mAb KKU505 was detected using western blot analysis. The protein band was identified as the myosin heavy chain of Ov using LC-MS/MS analysis. The reactivity of the recombinant full-length myosin heavy chain (rMHC) was comparable to that of the crude Ov antigen when evaluated using mAb-ELISA at similar protein concentrations. Moreover, the binding ability between Ov myosin head domain and mAb KKU505 was confirmed using in silico analysis. The results reported here indicate that rMHC could potentially substitute for Ov crude antigen in antigen detection by mAb-ELISA and as a positive control for Ov-strip in lateral flow assays, thereby avoiding the use of laboratory animals for the production of Ov adult worms.

An engineered canine-mouse chimeric neutralizing antibody provides therapeutic effects against canine parvovirus infection

Canine parvovirus (CPV) is a highly contagious and severe infectious disease that can lead to hemorrhagic enteritis and even acute death in dogs. Despite mouse monoclonal antibodies (mAbs) have been employed in clinical treatment, their application in non-murine species is restricted due to immune rejection. In this study, we screened a mouse mAb (5E7) with high neutralizing activity against CPV using hybridoma technology. Subsequently, the variable regions of the heavy (VH) and light (VL) chains of 5E7 were amplified by PCR and fused with the constant regions of canine IgG antibody to produce canine-mouse chimeric antibody (CM-5E7). The chimeric antibody was successfully expressed in HEK293 cells and exhibited high neutralizing activity against multiple CPV subtypes in vitro. Furthermore, CM-5E7 exhibited effective therapeutic potential in dogs subjected to lethal dose CPV-2c challenge in vivo. Overall, CM-5E7 demonstrated high neutralizing activity against CPV and showed significant efficacy in treating CPV-2c infections, positioning it as a promising candidate therapeutic antibody for the treatment of CPV infection.

In silico bioprospecting of the Neotropical Plant Mandacaru (Cereus) for antimicrobial properties

The mandacaru is a cactus species complex widely known in Brazil, with extensive applications in medicinal, food, and agricultural fields. Although it is used medicinally by traditional populations, to treat several diseases, knowledge about its biomolecules of biotechnological potential is still limited, specifically regarding antimicrobial and healing properties. The bacterial resistance to conventional antibiotics presents a significant challenge in modern medicine. In light of this scenario, bioprospecting mandacaru for biotechnological applications as an antimicrobial has emerged as a new and promising research area. In this study, transcriptomic data from three Cereus species (C. fernambucensis, C. hildmannianus, and C. jamacaru) were combined with bioinformatic approaches, including protein modeling, molecular docking, and molecular dynamics simulations, to identify proteins with therapeutic potential for treating wound infections. Our findings highlighted peptides as particularly promising antimicrobial agents, demonstrating efficacy against a range of pathogens, including Gram-positive and Gram-negative bacteria, as well as fungi. Those peptides showed strong interactions with the streptolydigin and sodium ligands, with the streptolydigin ligand emerging as the most promising for enhancing antimicrobial activity. Molecular dynamics revealed that while CF15 exhibited limited stability, CF267, CF48, CH167, and CH176 displayed superior stability, positioning them as the most promising candidates for further investigation. Future work will focus on synthesizing these peptides and evaluating their antimicrobial properties through in vitro and in vivo analyses, to develop them into potent therapeutic agents.

Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions

The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current computational landscape for predicting protein complex structures, outlining the role of traditional docking approaches as well as focusing on recent advances in AI-driven methods. We discuss key challenges, including protein flexibility, reliance on co-evolutionary signals, modeling of large assemblies, and interactions involving intrinsically disordered regions (IDRs). Recent innovations aimed at improving sampling diversity, integrating experimental data, and enhancing robustness are also highlighted. Although classical methods remain relevant in specific contexts, the continued evolution of AI-based tools offers transformative potential for structural biology. These advances are poised to deepen our understanding of biomolecular interactions and accelerate the design of therapeutic interventions.

A novel anti-PD-L1 DNA aptamer, Apta35 enhances non-small cell lung cancer cell cytotoxicity and apoptosis through lung cancer-activated T lymphocytes

The prevalence of Programmed death ligand 1 (PD-L1) expression in the population of NSCLC patients and blocking the PD1/PD-L1 pathway by inhibiting the PD-1 receptor on immune cells or the PD-L1 ligand on tumour and/or immune cells can inhibit tumour growth. EFBALite algorithm that enables efficient and cost-effective selection of aptamers, expediting the process. Here, we present the development, computational validation, and in vitro analysis of NSCLC of DNA aptamers targeting PD-L1. The Gibbs free energy of two anti-PD-L1 aptamers, Apta35 and Apta90 with -3.06 and - 2.4 kcal/mol respectively. The docking score for Apta35 was -272.3 and 1171.765 for HDOCK and ZDOCK respectively. Further, the Apta35 was taken for the in vitro studies as it was more stable and incubated with NCI-H460. Initially, we confirmed the binding of the TAMRA-labelled Apta35 to the NCI-H460 cell surface through microscopic imaging and further confirmed through FACS analysis. Further experimental results showed that the Apta35 treated along with the act-T cells group reduced the percentage of viability (28 ± 3.5), increased toxicity, and reduced count of NCI-H460 cells when compared with the cells treated only with the act-T cells concerning the treatment to 50 nM concentration. In summary, targeting PD-L1 with a specific aptamer provides an innovative strategy for targeting NSCLC. Apta35 aptamer showed no significant toxicity in the BALB/c nude mice while it was injected every 2 days for a total of 12 days of treatment.

HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data

Accurate modeling of protein-protein complex structures is essential for understanding biological mechanisms. Hydrogen-deuterium exchange (HDX) experiments provide valuable insights into binding interfaces. Incorporating HDX data into protein complex modeling workflows offers a promising approach to improve prediction accuracy. Here, we developed HDXRank, a graph neural network (GNN)-based framework for candidate structure ranking utilizing alignment with HDX experimental data. Trained on a newly curated HDX data set, HDXRank captures nuanced local structural features critical for accurate HDX profile prediction. This versatile framework can be integrated with a variety of protein complex modeling tools, transforming the HDX profile alignment into a model quality metric. HDXRank demonstrates effectiveness at ranking models generated by rigid docking or AlphaFold, successfully prioritizing functionally relevant models and improving prediction quality across all tested protein targets. These findings underscore HDXRank's potential to become a pivotal tool for understanding molecular recognition in complex biological systems.

Screening and identification of protein interacting with goose astrovirus

Introduction

Goose Astrovirus (GoAstV), a recently identified member of the Astroviridae family in China, predominantly affects goslings, resulting in substantial economic losses to the goose farming industry due to its high infection and mortality rates. Currently, the infection mechanism and pathogenesis of GoAstV remain unknown.

Methods

Given this, the Viral Overlay Protein Blot Assay was utilized to identify and characterize proteins on the LMH (Leghorn Male Hepatoma) cell membrane that interact with Goose Astrovirus. The identities of the candidate proteins were determined via LC-MS mass spectrometry analysis, bioinformatics analysis, and UniProt database search. The interaction between HSPA5 and the astrovirus protein was further validated in vitro through Western blot and Coimmunoprecipitation experiments. Finally, bioinformatics tools such as SWISSMODEL, AlphaFold, and ZDOCK were employed to construct and analyze the docking complex model between the candidate protein and GoAstV protein, including their key binding residue sites.

Results

We successfully identified a 70 kDa protein in the plasma membrane protein extracts of LMH cells and confirmed the identity of this candidate protein as HSPA5. Meanwhile, in vitro experiments further validated the interaction between HSPA5 and astrovirus proteins. Subsequently, we successfully predicted the docking complex model of HSPA5 protein with GoAstV protein. Further prediction of the binding residue sites revealed that seven residues of the GoAstV-P2 protein (THR124, ILE22, VAL24, TRP51, PRO66, GLN100, and VAL125) and twelve residues of the HSPA5 protein (ARG2, HIS3, LEU4, LEU6, ALA7, LEU8, LEU9, LEU10, LEU11, ASP411, VAL413, and LEU415) may be involved in the interaction between these two proteins.

Discussion

Our research results have preliminarily elucidated the interaction mechanisms between viral proteins and receptors, facilitating exploration from multiple angles of the roles of candidate protein in the process of GoAstV infecting host cells. This provides a theoretical basis for further identification of GoAstV receptors and clarification of its infection mechanisms.

Applying Computational Protein Design to Engineer Affibodies for Affinity-controlled Delivery of Vascular Endothelial Growth Factor and Platelet-Derived Growth Factor

Vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) play coordinated roles in angiogenesis. However, current biomaterial delivery vehicles for these proteins have a limited ability to precisely control the kinetics of protein release, preventing systematic exploration of their temporal effects. Here, we combined yeast surface display and computational protein design to engineer eight VEGF-specific and PDGF-specific protein binders called affibodies with a broad range of affinities for controlled protein release. Soluble affibodies modulated protein bioactivity as evidenced by changes in VEGF-induced endothelial cell proliferation and luminescent output of a PDGF-responsive cell line. Affibody-conjugated hydrogels enabled tunable protein release over 7 days. VEGF and PDGF released from affibody-conjugated hydrogels exhibited higher bioactivity than proteins released from hydrogels without affibodies, suggesting that these engineered affinity interactions could prolong protein bioactivity. This work underscores the power of computational protein design to enhance biomaterial functionality, creating a platform for tunable protein delivery.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

HawkDock version 2: an updated web server to predict and analyze the structures of protein-protein complexes

Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integrated computational platform for predicting and analyzing protein-protein complexes. Over the past 6 years, HawkDock has successfully processed >234 000 computational tasks. In this study, an updated version of HawkDock was developed with the following advancements: (1) a deep learning-based flexible docking method, GeoDock, has been integrated to improve docking accuracy, particularly for apo-protein structures; (2) the VD-MM/GBSA method, which outperforms conventional MM/GBSA approaches in predicting binding affinities, has been implemented; (3) a new Mutation Analysis Module has been added to systematically evaluate the energetic impacts of amino acid mutations on protein-protein binding; (4) the server has been migrated to a high-performance cluster with Amber upgraded to version 24. Here, we describe the general protocol of HawkDock2, with a particular focus on its new features related to flexible docking, VD-MM/GBSA affinity prediction, and amino acid residue mutations. Comprehensive validation studies have demonstrated the reliability and effectiveness of these new features. HawkDock2 will remain freely accessible to all users at http://cadd.zju.edu.cn/hawkdock/.

CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length

Peptides are recognized as next-generation therapeutic drugs due to their unique properties and are essential for treating human diseases. In recent years, a number of deep generation models for generating peptides have been proposed and have shown great potential. However, these models cannot well control the length of the generated sequence, while the sequence length has a very important impact on the physical and chemical properties and therapeutic effects of peptides. Here, a diffusion model is introduced, capable of controlling the length of generated functional peptide sequences, named CPL-Diff. CPL-Diff can control the length of generated polypeptide sequences using only attention masking. Additionally, CPL-Diff can generate single-functional polypeptide sequences based on given conditional information. Experiments demonstrate that the peptides generated by CPL-Diff exhibit lower perplexity and similarity compared to those produced by the current state-of-the-art models, and further exhibit relevant physicochemical properties similar to real sequences. The interpretability analysis is also performed on CPL-Diff to understand how it controls the length of generated sequences and the decision-making process involved in generating polypeptide sequences, with the aim of providing important theoretical guidance for polypeptide design. The code for CPL-Diff is available at https://github.com/luozhenjie1997/CPL-Diff.

The Wheat Intrinsically Disordered Protein TdRL1 Negatively Regulates the Type One Protein Phosphatase TdPP1

Type 1 protein phosphatases (PP1s) are crucial in various plant cellular processes. Their function is controlled by regulators known as PP1-interacting proteins (PIPs), often intrinsically disordered, such as Inhibitor 2 (I2), conserved across kingdoms. The durum wheat TdRL1 acts as a positive regulator of plant stress tolerance, presumably by inhibiting PP1 activity. In this work, co-immunoprecipitation and bimolecular fluorescence complementation (BiFC) assays demonstrate that the durum wheat TdPP1 interacts with both TdRL1 and At-I2 in vivo. YFP fluorescence restored after TdRL1-TdPP1 interaction decorated specifically the microtubular network of the tobacco co-infiltrated cells. In vitro phosphatase assays revealed that TdRL1 inhibited the activity of wild-type TdPP1 and two mutant forms (T243M and H135A) in a concentration-dependent manner, showing a novel and potent inhibition mechanism. Structural modeling of the TdPP1-inhibitor complexes suggested that both At-I2 and TdRL1 bind to TdPP1 by wrapping their flexible C-terminal tails around it, blocking access to the active site. Remarkably, the model showed that TdRL1 differs from At-I2 in its interaction with TdPP1 by trapping the phosphatase with its N-terminal tail. These findings provide important insights into the regulatory mechanisms governing the activity of PP1s in plants and highlight the potential for targeted inhibition to modulate plant stress responses.

The Role of MCPIP1 in Macrophage Polarization and Cardiac Function Post-Myocardial Infarction

Macrophages play a critical role in both initiating and resolving inflammation following MI (myocardial infarction). Their polarization is essential for maintaining cardiac function. This study aims to explore the role of MCPIP1(Monocyte chemotactic protein-induced protein 1) in regulating macrophage polarization and its impact on heart-spleen interactions during MI recovery. The role of MCPIP1 was investigated using histological staining, RNA sequencing of bone marrow-derived macrophages, co-culture experiments, and validated by western blot. Compared to controls, myeloid MCPIP1-deficient mice had lower survival rates, larger infarction areas, and more severe inflammatory responses after MI. This was due to increased M1 polarization and impaired conversion to the M2 phenotype. Ferroptosis activation in MCPIP1-deficient macrophages was inhibited by Fer-1 and PFT-α, which promoted M2 polarization and fibroblast activation into myofibroblasts. MCPIP1-deficient MI mice also showed splenomegaly and elevated levels of circulating macrophages, indicating excessive extramedullary hematopoiesis. Splenectomy improved survival rates and reduced infarction size in MCPIP1-deficient mice. MCPIP1 suppresses the P53/ferroptosis pathway to regulate macrophage polarization and TGF-β/SMAD3-mediated fibroblast activation. Its deficiency exacerbates inflammation through abnormal splenic macrophage output, impairing cardiac repair. MCPIP1 is a promising therapeutic target for modulating ferroptosis and heart-spleen communication to protect cardiac function following MI.

Epithelial FETUB-mediated the inhibition of NEP activity aggravates asthma

BACKGROUND

Neuropeptide accumulation exacerbates asthma, with reduced neprilysin (NEP) activity implicated. However, this regulatory mechanism remains unexplored.

OBJECTIVE

To identify and characterize epithelial-derived modulators of NEP activity and their role in asthma pathogenesis.

METHODS

Bioinformatics and molecular docking identified fetuin B (FETUB) as a NEP inhibitor. FETUB expression in human lung tissue was assessed by immunohistochemistry, and its levels in exhaled breath condensate (EBC) and serum were quantified by ELISA. Functional assays and a lung-specific FETUB knockdown mouse model using Adeno-associated virus (AAV) vector confirmed its role in NEP inhibition and asthma pathogenesis.

RESULTS

Bioinformatic analysis, protein binding assays, and fluorescence substrate degradation experiments confirmed that FETUB is an inhibitor of NEP. Serum FETUB levels were elevated in asthmatics and positively correlated with serum IgE, eosinophil counts. Similarly, in asthmatic EBC, FETUB levels were significantly higher than in healthy controls and negatively correlated with asthma control test, FEV1 and FEV1%pred. The expression of FETUB was elevated in asthma lung tissue and primarily localized to airway epithelial cells. Combined bioinformatics and experimental data indicated that IL-13 as a key inducer of epithelial FETUB expression. Lung-specific FETUB knockdown restored NEP activity, reduced neuropeptides CGRP and SP, and improved airway inflammation and hyperresponsiveness in asthma.

CONCLUSION

The findings suggest that epithelial-derived FETUB exacerbates airway inflammation and hyperresponsiveness in asthma through the inhibition of NEP activity and the resultant accumulation of CGRP and SP.

MGAT1-Guided complex N-Glycans on CD73 regulate immune evasion in triple-negative breast cancer

Despite the widespread application of immunotherapy, treating immune-cold tumors remains a significant challenge in cancer therapy. Using multiomic spatial analyses and experimental validation, we identify MGAT1, a glycosyltransferase, as a pivotal factor governing tumor immune response. Overexpression of MGAT1 leads to immune evasion due to aberrant elevation of CD73 membrane translocation, which suppresses CD8+ T cell function, especially in immune-cold triple-negative breast cancer (TNBC). Mechanistically, addition of N-acetylglucosamine to CD73 by MGAT1 enables the CD73 dimerization necessary for CD73 loading onto VAMP3, ensuring membrane fusion. We further show that THBS1 is an upstream etiological factor orchestrating the MGAT1-CD73-VAMP3-adenosine axis in suppressing CD8+ T cell antitumor activity. Spatial transcriptomic profiling reveals spatially resolved features of interacting malignant and immune cells pertaining to expression levels of MGAT1 and CD73. In preclinical models of TNBC, W-GTF01, an inhibitor specifically blocked the MGAT1-catalyzed CD73 glycosylation, sensitizing refractory tumors to anti-PD-L1 therapy via restoring capacity to elicit a CD8+ IFNγ-producing T cell response. Collectively, our findings uncover a strategy for targeting the immunosuppressive molecule CD73 by inhibiting MGAT1.

Endogenous protein S100A14 stabilizes glutaminase to render hepatocellular carcinoma resistant to sorafenib

BACKGROUND

Many cases of advanced hepatocellular carcinoma (HCC) are resistant to the widely used drug sorafenib, which worsens prognosis. While many studies have explored how acquired resistance emerges during drug exposure, the mechanism underlying primary resistance before treatment still remain elusive.

METHODS

Single-cell lineage tracing and RNA sequencing were performed to identify primary sorafenib-resistant lineages in HCC. Differential gene expression analysis was employed to identify the biomarkers of drug-resistant lineage cells. Cell viability and colony formation assays were adopted to assess the involvement of S100A14 on sorafenib resistance. Co-immunoprecipitation (CO-IP) and mass spectrometry analysis were conducted to uncover the downstream targets and regulatory mechanisms of S100A14 in primary resistance to sorafenib. In vivo mouse xenograft experiments were carried out to assess the effect of S100A14 or its interacting protein glutaminase (GLS) on primary resistance to sorafenib in HCC.

RESULTS

Single-cell lineage tracing identified a cluster of sorafenib primary resistant cells, and S100A14, a Ca2+-binding protein, was determined to be a critical biomarker for primary resistance to sorafenib. Knockdown of S100A14 significantly increases sorafenib treatment sensitivity in HCC cells. Mechanistically, S100A14 binds to GLS and blocks its phosphorylation at residues Y308 and S314, which in turn inhibits its ubiquitination and subsequent degradation. By stabilizing GLS, S100A14 reduces oxidative stress in HCC cells, thereby antagonizing sorafenib-induced apoptosis. Inhibiting S100A14 or GLS significantly improved sorafenib efficacy against xenograft tumors in vivo.

CONCLUSIONS

Our results demonstrate that S100A14 plays a pivotal role in promoting primary resistance to sorafenib by stabilizing GLS to reduce oxidative stress, and acts as a potential therapeutic target to enhance the efficacy of sorafenib in HCC patients.

Allosteric targeted drug delivery for enhanced blood-brain barrier penetration via mimicking transmembrane domain interactions

Current strategies for active targeting in the brain are entirely based on the effective interaction of the ligand with the orthosteric sites of specific receptors on the blood-brain barrier (BBB), which is highly susceptible to various pathophysiological factors and limits the efficacy of drug delivery. Here, we propose an allosteric targeted drug delivery strategy that targets classical BBB transmembrane receptors by designing peptide ligands that specifically bind to their transmembrane domains. This strategy prevents competitive interference from endogenous ligands and antibodies by using the insulin receptor and integrin αv as model targets, respectively, and can effectively overcome pseudotargets or target loss caused by shedding or mutating the extracellular domain of target receptors. Moreover, these ligands can be spontaneously embedded in the phospholipid layer of lipid carriers using a plug-and-play approach without chemical modification, with excellent tunability and immunocompatibility. Overall, this allosteric targeted drug delivery strategy can be applied to multiple receptor targets and drug carriers and offers promising therapeutic benefits in brain diseases.

Aquaporin 9 downregulation in KRASG12V colorectal cancer and associated with increased proliferation and decreased apoptosis in cancer cells

Patients with colorectal cancer (CRC) carrying KRAS mutations face a challenging prognosis, especially due to their reduced response to EGFR inhibitor therapies. Despite the use of drugs targeting the KRASG12C mutation, the KRASG12V mutation is more common in CRC, and unfortunately, there are currently no effective targeted treatments for it. Our study shows that patients harboring KRASG12V mutation often have larger tumors, increased lymph node metastasis, elevated EGFR expression, and a tendency for right-sided colon tumors. This indicates distinct clinical and pathological traits in CRC patients with KRASG12V. Cellular studies reveal increased proliferation and decreased cell apoptosis in KRASG12V CRC cells. Bioinformatics analysis revealed a notable decrease of aquaporin 9 (AQP9) in KRASG12V CRC, confirmed by immunohistochemistry and Western blot tests. These tests showed a consistent AQP9 decrease in tissue and cell samples, linked to an increased risk of lymph node metastasis in patients with low AQP9. Importantly, AQP9 overexpression was found to hinder CRC cell proliferation and encourage apoptosis, thereby implying a potential therapeutic role for AQP9 modulation. Our study finds a link between ZHX2 and AQP9 in CRC cells, confirmed by histopathological and in vitro evidence. Increased ZHX2 expression elevates AQP9 levels, reduces CRC cell growth, and boosts apoptosis. CO-IP experiments further prove the interaction between ZHX2 and AQP9 proteins. Molecular docking studies reveal that ZHX2 can form stable complexes with AQP9, involving multiple residues. This research enhances our understanding of the molecular mechanisms regulating the growth and death of KRASG12V CRC cells, paving the way for new therapeutic strategies.

HMOX1-LDHB interaction promotes ferroptosis by inducing mitochondrial dysfunction in foamy macrophages during advanced atherosclerosis

Advanced atherosclerosis is the pathological basis for acute cardiovascular events, with significant residual risk of recurrent clinical events despite contemporary treatment. The death of foamy macrophages is a main contributor to plaque progression, but the underlying mechanisms remain unclear. Bulk and single-cell RNA sequencing demonstrated that massive iron accumulation in advanced atherosclerosis promoted foamy macrophage ferroptosis, particularly in low expression of triggering receptor expressed on myeloid cells 2 (TREM2low) foamy macrophages. This cluster exhibits metabolic characteristics with low oxidative phosphorylation (OXPHOS), increasing ferroptosis sensitivity. Mechanically, upregulated heme oxygenase 1 (HMOX1)-lactate dehydrogenase B (LDHB) interaction enables Lon peptidase 1 (LONP1) to degrade mitochondrial transcription factor A (TFAM), leading to mitochondrial dysfunction and ferroptosis. Administration of the mitochondria-targeted reactive oxygen species (ROS) scavenger MitoTEMPO (mitochondrial-targeted TEMPO) or LONP1 inhibitor bortezomib restored mitochondrial homeostasis in foamy macrophages and alleviated atherosclerosis. Collectively, our study elucidates the cellular and molecular mechanism of foamy macrophage ferroptosis, offering potential therapeutic strategies for advanced atherosclerosis.

The UV-B photoreceptor UVR8 interacts with the LOX1 enzyme to promote stomatal closure through the LOX-derived oxylipin pathway

Ultraviolet-B (UV-B) light-induced stomatal closure requires the photoreceptor UV RESISTANCE LOCUS 8 (UVR8) and nitric oxide (NO). However, the signaling pathways by which UV-B light regulates stomatal closure remain elusive. Here, we reveal that UVR8 signaling in the epidermis mediates stomatal closure in a tissue-specific manner in Arabidopsis (Arabidopsis thaliana). UV-B light promotes PHOSPHOLIPASE 1 (PLIP1)/PLIP3-mediated linoleic acid and α-linolenic acid accumulation and induces LIPOXYGENASE 1 (LOX1) expression. LOX1, which catabolizes linoleic acid and α-linolenic acid to produce oxylipin derivatives, acts downstream of UVR8 and upstream of the salicylic acid (SA) pathway associated with stomatal defense. Photoactivated UVR8 interacts with LOX1 and enhances its activity. Protein crystallography demonstrates that A. thaliana LOX1 and its ortholog in soybean (Glycine max) share overall structural similarity and conserved residues in the oxygen cavity, substrate cavity, and metal-binding site that are required for 9-LOX activity. The disruption of UVR8-LOX1 contact sites near the LOX1 oxygen and substrate cavities prevents UVR8-enhanced LOX1 activity and compromises stomatal closure upon UV-B exposure. Overall, our study uncovers a noncanonical UV-B signaling module, consisting of the UVR8 photoreceptor and the cytoplasmic lipoxygenase, that mediates stomatal responses to UV-B light.

Ternary Complex Modeling, Induced Fit Docking and Molecular Dynamics Simulations as a Successful Approach for the Design of VHL-Mediated PROTACs Targeting the Kinase FLT3

Proteolysis targeting chimeras (PROTACs) have proven to be a novel approach for the degradation of disease-causing proteins in drug discovery. One of the E3 ligases for which efficient PROTACs have been described is the Von Hippel-Lindau factor (VHL). However, the development of PROTACs has so far often relied on a minimum of computational tools, so that it is mostly based on a trial-and-error process. Therefore, there is a great need for resource- and time-efficient structure-based or computational approaches to streamline PROTAC design. In this study, we present a combined computational approach that integrates static ternary complex formation, induced-fit docking, and molecular dynamics (MD) simulations. Our methodology was tested using four experimentally derived ternary complex structures of VHL PROTACs, reported for BRD4, SMARCA2, FAK, and WEE1. In addition, we applied the validated approach to model a recently in-house developed FLT3-targeted PROTAC (MA49). The results show that static ternary models generated with a protein-protein docking method implemented in the software MOE have a high predictive power for reproducing the experimental 3D structures. The induced-fit docking of different active PROTACs to their respective models showed the reliability of this model for the development of new VHL-mediated degraders. In particular, the induced-fit docking was sensitive to structural changes in the PROTACs, as evidenced by the failed binding modes of the PROTAC negative controls. Furthermore, MD simulations confirmed the stability of the generated complexes and emphasized the importance of dynamic studies for understanding the relationship between PROTAC structure and function.

Molecular dynamics simulations show how antibodies may rescue HIV-1 mutants incapable of infecting host cells

High mutation and replication rates of HIV-1 result in the continuous generation of variants, allowing it to adapt to changing host environments. Mutations often have deleterious effects, but variants carrying them are rapidly purged. Surprisingly, a particular variant incapable of entering host cells was found to be rescued by host antibodies targeting HIV-1. Understanding the molecular mechanism of this rescue is important to develop and improve antibody-based therapies. To unravel the underlying mechanisms, we performed fully atomistic molecular dynamics simulations of the HIV-1 gp41 trimer responsible for viral entry into host cells, its entry-deficient variant, and its complex with the rescuing antibody. We find that the Q563R mutation, which the entry-deficient variant carries, prevents the native conformation of the gp41 6-helix bundle required for entry and stabilizes an alternative conformation instead. This is the consequence of substantial changes in the secondary structure and interactions between the domains of gp41. Binding of the antibody F240 to gp41 reverses these changes and re-establishes the native conformation, resulting in rescue. To test the generality of this mechanism, we performed simulations with the entry-deficient L565A variant and antibody 3D6. We find that 3D6 binding was able to reverse structural and interaction changes introduced by the mutation and restore the native gp41 conformation. Viral variants may not only escape antibodies but be aided by them in their survival, potentially compromising antibody-based therapies, including vaccination and passive immunization. Our simulation framework could serve as a tool to assess the likelihood of such resistance against specific antibodies.

Using Short Molecular Dynamics Simulations to Determine the Important Features of Interactions in Antibody-Protein Complexes

The last few years have seen the rapid proliferation of machine learning methods to design binding proteins. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when they are experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. Short molecular dynamics simulations of 20 antibody-protein complexes were conducted to identify features of interactions that should occur in binding interfaces. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was found that only the hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and meaningfully contribute to binding between the proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist. Still, interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies than other interactions. Two hundred and twenty real antibody-protein complexes and 8194 decoy complexes were used to train and test a random forest classifier using the features of expected persistent interactions identified in this study and the macromolecular features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. It was compared to a classifier trained only on the expected persistent interaction features and another trained only on the macromolecular features. Inclusion of the expected persistent interaction features reduced the false positive rate of the classifier by two- to five-fold across a range of true positive classification rates.

Protein-Peptide Docking with ESMFold Language Model

Designing peptide therapeutics requires precise peptide docking, which remains a challenge. We assessed the ESMFold language model, originally designed for protein structure prediction, for its effectiveness in protein-peptide docking. Various docking strategies, including polyglycine linkers and sampling-enhancing modifications, were explored. The number of acceptable-quality models among top-ranking results is comparable to traditional methods and generally lower than AlphaFold-Multimer or Alphafold 3, though ESMFold surpasses it in some cases. The combination of result quality and computational efficiency underscores ESMFold's potential value as a component in a consensus approach for high-throughput peptide design.

The core anammox redox reaction system of 12 anammox bacterial genera and their evolution and application implications

Anaerobic ammonium-oxidation (anammox) is a typical redox reaction driven by membrane electron transformation. However, the electron transfer mechanism of the core redox reaction and its evolutionary origins are still not thoroughly identified. In this study, a preliminary analysis was conducted for such interaction based on the 64 anammox bacterial genomes representing 12 genera available currently. The results suggested that enzymes involved in anammox reaction share the similar catalytic and electron transfer modes in different lineages, while the electron-carrying proteins shuttled between membrane and soluble enzymes are very different. A comparatively simple electronic shuttle protein system was encoded in the early-branching groundwater lineages Candidatus (Ca.) Avalokitesvara and Ca. Tripitaka, which was replaced by a sophisticated electron carrier scheme in the late-branching marine and terrestrial groups within family Ca. Brocadiaceae. Remarkably, the increasing availability of nitrite after Great Oxidation Event (GOE) potentially drove the adaptive evolution of the core redox systems by successively recruiting the nitrite reductase (NIR) for nitrite balance, a stable complex of two small cytochrome c proteins (NaxL and NaxS homologues) for electron transfer to HZS, as well as optimizing the structure of nitrite oxidoreductase gamma (NxrC) for electron conservation. In particular, a tubule-inducing nitrite oxidoreductase subunit (NxrT homologue) was further formed for electron transformation after the Neoproterozoic Oxygenation Event (NOE). Finally, based on two full-scale anammox-based wastewater treatment systems (WWTPs), we identified core gene transcriptional activities affecting the abundance of the family Ca. Brocadiaceae and their association with environmental factors. Overall, our study not only provides key information for understanding the dynamic patterns and evolutionary mechanisms of the anammox reactions and the associated electron transfers in conjunction with major geological events, but also provides new insights for future enrichment and effective applications.

New use of an old drug: mechanism of oseltamivir phosphate inhibiting liver cancer through regulation of lipophagy via NEU1

Background

Neuraminidase-1 (NEU1) is an enzyme that breaks down sialic acids on glycoproteins and glycolipids. Aberrant expression of NEU1 has been linked to the progression of numerous malignancies, including liver cancer. Oseltamivir phosphate (OP) is a drug used to treat and prevent influenza, which specifically inhibits NEU1. However, the molecular mechanisms of NEU1 in liver cancer and the potential therapeutic effects of OP remain largely unclear.

Methods

NEU1 expression in liver cancer was evaluated using public databases and validated in our samples. CRISPR/Cas9, CCK-8 assay, transwell assays, oil red O staining, RNA-sequencing, immunofluorescence and co-immunoprecipitation (Co-IP) and in vivo experiments were used to investigate the biological function of NEU1 and the therapeutic effect of OP in liver cancer.

Results

We demonstrated that NEU1 expression was significantly elevated in liver cancer cells and tumor tissues. Patients with liver cancer exhibiting high levels of NEU1 expression tended to have a less favorable prognosis. NEU1 knockdown inhibited liver cancer cells proliferation, invasion and migration. Subsequent experiments demonstrated that NEU1 knockdown reduced lipid accumulation through promoting perilipin 2 (PLIN2)-mediated lipophagy. Notably, OP (NEU1 inhibitor), promoted lipophagy, thereby inhibiting liver cancer proliferation and tumorigenesis. Moreover, liver cancer cells were more sensitive to OP compared to other chemotherapeutics, like 5-fluorouracil and gemcitabine, with a reduced drug resistance.

Conclusion

OP inhibits liver cancer progression by targeting NEU1 and inducing lipophagy through the suppression of PLIN2. Our findings provide new directions on the role of NEU1 in liver cancer and offer latent strategies to address the chemotherapy-induced drug resistance.

Neutrophil elastase binds at the central domain of extracellular Toll-like receptor 4: AI prediction, docking, and validation in disease model

The interaction between Neutrophil Elastase (NE) and Toll-like receptor 4 (TLR4) has attracted substantial scientific attention, particularly regarding its potential role in cardiovascular diseases. Employing AlphaFold2, biomolecular docking, and MMGBSA calculation we aimed to predict their binding and validated the results through a co-immunoprecipitation study in a rat model with isoproterenol (ISO) -induced cardiac hypertrophy. Our findings strongly suggest a specific and plausible interaction between rat NE and rat TLR4, distinct from other neutrophil-derived serine proteases. Notably, AlphaFold2's precision was confirmed through cross-validation with known protein crystal structures, while Consurf analysis emphasized the evolutionary variable to conserve the rat NE - rat TLR4 binding site. HADDOCK, RosettaDock, ZDOCK, MD simulation, MMGBSA calculations, and superimposition with the stabilized structure complex all predicted strong binding between rat NE and rat TLR4. Our animal experiments revealed elevated NE and TLR4 expression in the hypertrophied myocardium following ISO infusion, with data confirming the physical interaction between NE and TLR4. Overall, this study sheds light on the intricate molecular association between NE and TLR4, underlining their potential significance in cardiovascular pathophysiology. Furthermore, it underscores AlphaFold2's reliability as a robust tool for predicting protein-protein interactions and complex structures.

Effectiveness and Efficiency: Label-Aware Hierarchical Subgraph Learning for Protein-Protein Interaction

The study of protein-protein interactions (PPIs) holds immense significance in understanding various biological activities, as well as in drug discovery and disease diagnosis. Existing deep learning methods for PPI prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they often experience a decline in performance in the real world. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes' degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. The huge data growth of PPI leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose a label-aware hierarchical subgraph learning method (laruGL-PPI) that can effectively infer PPIs while being interpretable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments conducted across various scales of PPI datasets have conclusively demonstrated that the laruGL-PPI method surpasses the most advanced PPI prediction techniques currently available, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.

Ex.50.T aptamer impairs tumor-stroma cross-talk in breast cancer by targeting gremlin-1

The tumor microenvironment profoundly influences tumor complexity, particularly in breast cancer, where cancer-associated fibroblasts play pivotal roles in tumor progression and therapy resistance. Extracellular vesicles are involved in mediating communication within the TME, specifically highlighting their role in promoting the transformation of normal fibroblasts into cancer-associated fibroblasts. Recently, we identified an RNA aptamer, namely ex.50.T, that binds with remarkable affinity to extracellular vesicles shed from triple-negative breast cancer cells. Here, through in vitro assays and computational analyses, we demonstrate that the binding of ex.50.T to extracellular vesicles and parental breast cancer cells is mediated by recognition of gremlin-1 (GREM1), a bone morphogenic protein antagonist implicated in breast cancer aggressiveness and metastasis. Functionally, we uncover the role of ex.50.T as an innovative therapeutic agent in the process of tumor microenvironment re-modeling, impeding GREM1 signaling, blocking triple-negative breast cancer extracellular vesicles internalization in recipient cells, and counteracting the transformation of normal fibroblasts into cancer-associated fibroblasts. Altogether, our findings highlight ex.50.T as a novel therapeutical avenue for breast cancer and potentially other GREM1-dependent malignancies, offering insights into disrupting TME dynamics and enhancing cancer treatment strategies.

Competitive binding of small antagonistic peptides to the OsER1 receptor optimizes rice panicle architecture

Rice panicle architecture is a pivotal trait that strongly contributes to grain yield. Small peptide ligands from the OsEPF/EPFL family synergistically control panicle architecture by recognition of the OsER1 receptor and subsequent activation of the OsMKKK10-OsMKK4-OsMPK6 cascade, indicating that specific ligand-receptor pairs orchestrate rice panicle development. However, how small homologous peptides fine-tune organ morphogenesis by targeting a common receptor remains to be clarified. Here, we report that the small peptide OsEPFL5 acts as a ligand of the OsER1 receptor that inactivates the OsMKKK10-OsMKK4-OsMPK6 cascade, suggesting that OsEPFL5 plays a role opposite to that of the OsEPFL6/7/8/9 subfamily in regulating spikelet number per panicle and grain size. Notably, OsEPFL5 competitively replaces binding of OsEPFL6, OsEPFL7, OsEPFL8, or OsEPFL9 to the OsER1 receptor, revealing antagonistic competition between these small homologous peptides. Specifically enhancing the expression of OsEPFL5 can significantly improve grain yield by suppressing functions of the ligand-receptor pairs OsEPFL6-OsER1, OsEPFL7-OsER1, OsEPFL8-OsER1, and OsEPFL9-OsER1, suggesting that competitive binding to the OsER1 receptor by small antagonistic peptides can optimize rice panicle architecture. Our findings clarify how a receptor agonist and antagonist define inductive and inhibitory cues to shape rice panicle architecture, thus providing a new method for rationally breaking yield-trait coupling by manipulating small antagonistic peptides.

FNDC5/irisin mitigates the cardiotoxic impacts of cancer chemotherapeutics by modulating ROS-dependent and -independent mechanisms

Cardiotoxicity remains a major limiting factor in the clinical implementation of anthracycline chemotherapy. Though the etiology of doxorubicin-dependent heart damage has yet to be fully elucidated, the ability of doxorubicin to damage DNA and trigger oxidative stress have been heavily implicated in the pathogenesis of chemotherapy-associated cardiomyopathy. Here, we demonstrate that fibronectin type III domain-containing protein 5 (FNDC5), the precursor protein for myokine irisin, is depleted in the hearts of human cancer patients or mice exposed to chemotherapeutics. In cardiomyocytes, restoration of FNDC5 expression was sufficient to mitigate reactive oxygen species (ROS) accumulation and apoptosis following doxorubicin exposure, effects dependent on the irisin encoding domain of FNDC5 as well as signaling via the putative irisin integrin receptor. Intriguingly, we identified two parallel signaling cascades impacted by FNDC5 in cardiomyocytes: the ROS-driven intrinsic mitochondrial apoptosis pathway and the ROS-independent Ataxia Telangiectasia and Rad3-Related Protein (ATR)/Checkpoint Kinase 1 (Chk1) pathway. In fact, FNDC5 forms a co-precipitable complex with Chk1 alluding to possible intracellular actions for this canonically membrane-associated protein. Whereas FNDC5 overexpression in murine heart was cardioprotective, introduction of FNDC5-targeted shRNA into the myocardium was sufficient to trigger Bax up-regulation, ATR/Chk1 activation, oxidative stress, cardiac fibrosis, loss of ventricular function, and compromised animal survival. The detrimental impact of FNDC5 depletion on heart function could be mitigated via treatment with a Chk1 inhibitor identifying Chk1 hyperactivity as a causative factor in cardiac disease. Though our data point to the potential clinical utility of FNDC5/irisin-targeted agents in the treatment of chemotherapy-induced cardiotoxicity, we also found significant down regulation in FNDC5 expression in the hearts of aged mice that attenuated the cardioprotective impacts of FNDC5 overexpression following doxorubicin exposure. Together our data underscore the importance of FNDC5/irisin in maintenance of cardiac health over the lifespan.

Unlocking the influence of PNPLA3 mutations on lipolysis: Insights into lipid droplet formation and metabolic dynamics in metabolic dysfunction-associated steatotic liver disease

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) covers a range of liver conditions marked by the buildup of fat, spanning from simple fatty liver to more advanced stages like metabolic dysfunction-associated steatohepatitis and cirrhosis.

METHODS

Our in-depth analysis of PNPLA3_WT and mutants (I148M (MT1) and C15S (MT2)) provides insights into their structure-function dynamics in lipid metabolism, especially lipid droplet hydrolysis and ABHD5 binding. Employing molecular docking, binding affinity, MD analysis, dissociation constant, and MM/GBSA analysis, we delineated distinct binding characteristics between wild-type and mutants.

RESULTS

Structural dynamics analysis revealed that unbound mutants exhibited higher flexibility, increased Rg and SASA values, and broader energy landscapes, indicating multiple inactive states. Mutations, especially in PNPLA3_MT1, reduced the exposure of the catalytic serine, potentially impairing enzymatic activity and LD hydrolysis efficiency. Altered interaction patterns and dynamics, particularly a shift in ABHD5 binding regions towards the C-terminal domain, underscore its role in LD metabolism. Energy dynamics analysis of the protein complexes revealed PNPLA3_WT exhibited multiple low-energy macrostates, whereas the mutants displayed narrower energy landscapes, suggesting a more stable functional state. PNPLA3_MT1 demonstrated the highest affinity towards ABHD5, highlighting the complex interplay between protein structure, dynamics, and lipid metabolism regulation.

CONCLUSION

PNPLA3_MT1 mutant exhibits the highest flexibility and significantly reduced catalytic serine accessibility, leading to impaired lipolysis. Contrarily, PNPLA3_WT maintains stable catalytic efficiency and effective LD hydrolysis, with PNPLA3_MT2 displaying intermediate behavior.

GENERAL SIGNIFICANCE

Our research provides valuable insights into the metabolic implications of PNPLA3 mutations, offering a path for potential therapeutic interventions in MASLD.

Anti-CRISPR proteins in Gluconobacter oxydans inactivate FnCas12a by acetylation

Gluconobacter oxydans is an important chassis cell for one-step production of vitamin C. Previous studies reported that CRISPR/Cas12a is naturally inactivated in G. oxydans, but the specific mechanism remains unclear. Here, we identified anti-CRISPR proteins AcrVA6, AcrVA7 and AcrVA8 in G. oxydans. They functioned as acetyltransferases to inactivate FnCas12a by respectively acetylating Lys671, Lys589 and Lys823 of FnCas12a. Lys671 and Lys823 were related residues that recognise the protospacer-adjacent motif, modification of AcrVA6 and AcrVA8 untangled the interaction between FnCas12a and dsDNA, while Lys589 played an important role in binding to the crRNA-target DNA heteroduplex, AcrVA7 prevented the formation of FnCas12a-crRNA binary complexes. In addition, histone deacetylase HDAC11 was found to prevent modification of FnCas12a by AcrVA6. Quantum mechanical calculations showed that ser37 of AcrVA6, as an intermediate between acetyl group and receptor protein, achieves acetylation through ping-pong transfer mechanism. Finally, the acetyltransferase AcrVA6 and the deacetylase HDAC11 served as photoswitches by writing and erasing acetyl groups, respectively, to achieve continuous on-off of FnCas12a. Our study reveals different mechanisms by which acetyltransferase inactivates Cas12a and successfully applies reversible acetylation to the regulation of gene editing tools, providing new insights into the function and application of acetylation.

The insight into the intermolecular interactions between protamine and insulin lispro

Protamine, a mixture of polypeptides, can form complexes with Insulin Lispro (IL) to prolong its hypoglycemic effect, but the binding mechanism remains unclear. The four protamine components were used to study their binding mechanism with IL through RP-HPLC, ITC, SPR and bioinformatics analysis. The results of RP-HPLC indicated that the binding capacity of protamine and its four peptides with IL are not constant. As the concentration of protamine increases, the binding amount keeps increasing, with peptide 2 (P2) exhibiting the highest binding capacity among the four peptides. After forming complexes, the structure of IL changed. ITC results showed that among the four components, P2 has the lowest KD value, with a ΔH of -149 ± 2.24 kJ/mol and -TΔS of 111 kJ/mol, indicating an enthalpy-driven binding mode. SPR results revealed a trend of rapid association and slow dissociation for all components. Bioinformatics analysis, showed that hydrogen bond and electrostatic interaction played an important role in binding, and there was a significant difference between 6 and 12 residues. MD simulations of P1 and P2 showed significant differences in RMSF values. Overall, P2 binds IL most strongly, while P1 has the weakest affinity. This study provides a novel multi-perspective exploration of protamine-IL interactions, with SPR and RP-HPLC supporting regional binding models and enhancing our understanding of their mechanisms.

EZH2-mediated macrophage-to-myofibroblast transition contributes to calcium oxalate crystal-induced kidney fibrosis

Long-term nephrocalcinosis leads to kidney injury, fibrosis, and even chronic kidney disease (CKD). Macrophage-to-myofibroblast transition (MMT) has been identified as a new mechanism in CKD, however, the effect of MMT in calcium oxalate (CaOx)-induced kidney fibrosis remains unclear. In this study, abundant MMT cells are identified by immunofluorescence (IF) and flow cytometry in kidney tissues of patients with CaOx-related CKD, a male mouse model, and CaOx-treated macrophages. Clodronate liposome (CLO)-mediated macrophage depletion attenuates fibrosis in male nephrocalcinosis mice. Transcriptomic sequencing reveals that histone methyltransferase (HMTs), EZH2, is highly expressed in nephrocalcinosis. Ezh2 inducible knock-out or inhibition by GSK-126 attenuates MMT and renal fibrosis. Mechanistically, ChIP and transcriptomic sequencing show that EZH2 inhibition reduces the enrichment of H3K27me3 on the Dusp23 gene promoter and elevates Dusp23 expression. The Co-IP and molecular docking analysis shows that DUSP23 mediates the dephosphorylation of pSMAD3 (Ser423/425). Thus, our study found that EZH2 promotes kidney fibrosis by meditating MMT via the DUSP23/SMAD3 pathway in nephrocalcinosis.

Structure-Based Deep Learning Framework for Modeling Human-Gut Bacterial Protein Interactions

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein-protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.

Targeted inhibition of menin promotes β-catenin-mediated GLP-1 expression and improves glucose tolerance in high-fat diet-induced obese mice

Glucagon-like peptide-1 (GLP-1), derived from enteroendocrine cells, is a pivotal hormone crucial for blood glucose regulation. Menin, encoded by the MEN1 gene and known for its tumor suppressor role, is abundantly expressed in the intestine. Previous research has demonstrated that acute Men1 excision reverses preexisting glucose intolerance in high-fat diet-fed mice. However, its impact on GLP-1 expression in enteroendocrine cells has not been investigated. In the present study, both the knockdown of Men1 and the administration of the MI-463 menin inhibitor increased GLP-1 expression in glucose-stimulated STC-1 cells. Additionally, administering MI-463 to obese mice significantly elevated GLP-1 levels in both ileal epithelial cells and serum. Mechanistically, menin inhibition enhanced the nuclear accumulation of β-catenin, allowing it to bind TCF7L2, thereby increasing glucagon gene (Gcg) transcription. Furthermore, compared with control mice, mice with intestinal epithelial cell-specific Men1 knockdown exhibited significant improvements in glucose tolerance under fat challenge, which was correlated with elevated GLP-1 levels. These findings suggest that menin-mediated regulation of GLP-1 expression may be an important mechanism through which menin inhibiton alleviates type 2 diabetes.

MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation

Proteolysis-targeting chimaeras (PROTACs), which induce proteolysis by recruiting an E3 ligase to dock into a target protein, are acquiring popularity as a novel pharmacological modality because of the unique features of PROTAC, including high potency, low dosage, and effective on undruggable targets. While PROTACs are promising prospects as chemical probes and therapeutic agents, their discovery usually necessitates the synthesis of numerous analogues to explore variations on the chemical linker structure exhaustively. Without extensive trial and error, it is unknown how to link the two protein-recruiting moieties to facilitate the formation of a productive ternary complex. Although molecular docking-based and optimization pipelines have been designed to predict ternary complexes, guiding rational PROTAC design, they have suffered from limited predictive performance in the quality of the ternary structure and their ranks. Here, MEGA PROTAC has been designed to enhance the performance in quality and ranking of ternary structures. MEGA PROTAC employs MEGADOCK to execute docking for protein-protein complexes (PPCs). The docking establishes an initial exploration area for PPCs. A sequential filtration strategy combined with rank aggregation is employed to choose a subset of PPCs for grid search. Once candidate PPCs are selected, a grid search method is used separately for translation and rotation. The remaining proteins have been grouped into clusters, and MEGA PROTAC further filters these clusters based on the energy score of the proteins within each cluster. MEGA PROTAC utilises rank aggregation to choose the best clusters and then employs MEGADOCK to dock PROTAC into the selected PPCs, forming a ternary structure. Finally, MEGA PROTAC was tested on 22 cases to compare with the state-of-the-art method, Bayesian optimisation for ternary complex prediction (BOTCP). MEGA PROTAC outperformed BOTCP on 16 test cases out of 22 cases, achieving a higher maximum DockQ score with an 18% higher mean and 35% higher median. Also, MEGA PROTAC exhibited 75% superior ranks and a reduced cluster number for maximum DockQ score compared to BOTCP. Also, MEGA PROTAC outperforms BOTCP by achieving a twofold improvement in locating the first acceptable DockQ scores, with a more significant proportion of near-native structures within the detected cluster.

Resurrection of the Plant Immune Receptor Sr50 to Overcome Pathogen Immune Evasion

Pathogen-driven plant diseases cause significant crop losses worldwide. The introgression of intracellular nucleotide-binding leucine-rich repeat receptor (NLR) genes into elite crop cultivars is a common strategy for disease control, yet pathogens rapidly evolve to evade NLR-mediated immunity. The NLR gene Sr50 protects wheat against stem rust, a devastating disease caused by the fungal pathogen Puccinia graminis f. sp. tritici (Pgt). However, mutations in AvrSr50 allowed Pgt to evade Sr50 recognition, leading to resistance breakdown. Advances in protein structure modeling can enable targeted NLR engineering to restore recognition of escaped effectors. Here, we combined iterative computational structural analyses and site-directed mutagenesis to engineer Sr50 recognition of AvrSr50QCMJC, a Pgt effector variant that evades wild-type Sr50 detection. Derived by molecular docking, our initial structural model identified the K711D substitution in Sr50, which partially restored AvrSr50QCMJC recognition. Enhancing Sr50K711D expression via strong promoters compensated for weak recognition and restored robust immune responses. Further structural refinements led to the generation of five double and two triple receptor mutants. These engineered mutants, absent in nature, showed robust dual recognition for AvrSr50 and AvrSr50QCMJC in both Nicotiana benthamiana and wheat protoplasts. Notably, the K711D substitution was essential and synergistic with the additional substitutions for AvrSr50QCMJC recognition, demonstrating protein epistasis. Furthermore, this single substitution altered AlphaFold 2 predictions, enabling accurate modeling of the Sr50K711D-AvrSr50 complex structure, consistent with our final structural hypothesis. Collectively, this study outlines a framework for NLR engineering to counteract pathogen adaptation and provides novel Sr50 variants with potential for stem rust resistance.

RNA Helicase DDX3 Interacts with the Capsid Protein of Hepatitis E Virus and Plays a Vital Role in the Viral Replication

DDX3 is an ATP-dependent RNA helicase that is involved in multiple cellular activities, including RNA metabolism and innate immunity. DDX3 is known to assist the replication of some viruses while restricting others through its direct interaction with viral proteins. However, the role of DDX3 in the replication of the hepatitis E virus (HEV) is unknown. In this study, DDX3 was shown to interact with the HEV capsid protein and provide an important role in HEV replication. The DDX3 C-terminal domain was demonstrated to interact with the capsid protein. The depletion of DDX3 led to a significant reduction in HEV replication. Also, the ATPase motif of DDX3 was shown to be required in HEV replication as an ATPase-null mutant DDX3 failed to rescue the viral replication in the DDX3-depleted cells. These results demonstrate a pro-viral role of DDX3 in HEV replication, providing further insights on the virus-cell interactions.

Unearthing novel and multifunctional peptides in peptidome of fermented chhurpi cheese of Indian Himalayan region

Fermented foods of the Indian Himalaya are unexplored functional resources with high nutritional potential. Chhurpi cheese, fermented by defined native proteolytic lactic acid bacteria of Sikkim was assessed for ACE inhibitory, HOCl reducing, and MPO inhibitory, activity across varying stages of gastrointestinal (GI) digestion. The enhanced bioactivity of Lactobacillus delbrueckii WS4 chhurpi was associated with the generation of bioactive and multifunctional peptides during fermentation and GI digestion. Qualitative and quantitative in silico tools were employed for prediction of ACE inhibitory activity of novel chhurpi peptides. Selected peptides, with highest predictive ACE inhibitory potential were synthesized and in vitro validation revealed the ACE inhibitory potential of peptides HPHPHLSFM and LKPTPEGDL. LKPTPEGDL showed the most potent ACE inhibitory activity (IC50 of 25.82 ± 0.26 µmol) which slightly decreased upon GI digestion. The peptides demonstrated a non-competitive type mixed ACE inhibition modality. Furthermore, the two peptides exerted observable HOCl reducing and MPO inhibitory activity, demonstrating their antioxidative potential. HPHPHLSFM exhibited superior HOCl reduction (EC50 of 0.29 ± 0.01 mmol), while LKPTPEGDL demonstrated higher MPO (IC50 of 0.29 ± 0.01 mmol) inhibition. Molecular docking of the two peptides with MPO revealed proline and aspartate near peptidyl C-terminus to bind with enzyme catalytic residues. This study presents the first peptidome analysis of chhurpi produced through controlled fermentation and identifies novel peptides with MPO and ACE inhibitory activity. Furthermore, it marks the first synthesis and in vitro bioactivity validation of bioactive peptides from chhurpi cheese, highlighting its multifunctional potential.

DSN1 Interaction With Centromere-Associated Proteins Promotes Chromosomal Instability in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer. Dosage suppressor of NNF1 (DSN1), a component of the MIS12 kinetochore complex, encodes a kinetochore protein crucial for proper mitotic assembly. The role of DSN1 in HCC remains to be elucidated. In this study, we utilized The Cancer Genome Atlas, the Hepatocellular carcinoma Cell Database, and other databases to analyze DSN1 expression and prognosis in samples from patients with HCC. We investigated the signaling pathways regulated by DSN1 and their implications in HCC. Additionally, we engineered siRNA/shRNA and overexpression vectors for DSN1 and assessed the specific mechanisms of regulatory pathways of DSN1 in hepatoma cell lines and subcutaneous tumor xenograft model. Our findings revealed that DSN1 expression was significantly upregulated in patients with HCC, correlating with decreased survival rates. Elevated DSN1 expression led to the overproduction of cell cycle-related proteins through direct interaction with Centromere Protein T. This interaction contributes to chromosomal instability in patients with HCC, resulting in an aberrant cell cycle and fostering the development and progression of HCC. Increased DSN1 expression is pivotal in HCC initiation and progression. Investigating DSN1 offers valuable insights into the pathogenesis, treatment, and prevention of HCC.

Itraconazole Reversing Acquired Resistance to Osimertinib in NSCLC by Inhibiting the SHH/DUSP13B/p-STAT3 Axis

There is an urgent necessity to devise efficient tactics to tackle the inevitable development of resistance to osimertinib, which is a third-generation epidermal growth factor receptor (EGFR) inhibitor used in treating EGFR-mutant nonsmall cell lung cancer (NSCLC). This study demonstrates that combining itraconazole with osimertinib synergistically reduces the proliferation and migration, enhances the apoptosis of osimertinib-resistant cells, and effectively inhibits the growth of osimertinib-resistant tumors. Mechanistically, itraconazole combined with osimertinib promotes the proteasomal degradation of sonic hedgehog (SHH), resulting in inactivation of the SHH/Dual-specificity phosphatase 13B (DUSP13B)/p-STAT3 and Hedgehog pathways, suppressing Myc proto-oncogene protein (c-Myc). Additionally, DUSP13B interacts with signal transducer and activator of transcription 3 (STAT3) and modulates its phosphorylation. Interestingly, it is observed that SHH overexpression partially rescues the synergistic effects of this combination treatment strategy through the SHH/DUSP13B/p-STAT3 signaling axis. Moreover, it is found that SHH, (GLI1), p-STAT3, and DUSP13B play significant predictive roles in osimertinib resistance. In lung adenocarcinoma, p-STAT3 is positively correlated with SHH but negatively correlated with DUSP13B. Together, these results highlight the crucial role of itraconazole in reversing the acquired resistance to osimertinib and provide a scientific rationale for the therapeutic strategy of combining osimertinib with itraconazole.

The glial UDP-glycosyltransferase Ugt35b regulates longevity by maintaining lipid homeostasis in Drosophila

Lipid droplets (LDs) are dynamic organelles essential for lipid storage and organismal survival. Studies have highlighted the importance of glial function in brain LD formation during aging; however, the genes and mechanisms involved remain elusive. Here, we found that Ugt35b, a member of the uridine diphosphate (UDP)-glycosyltransferases that catalyze the transfer of glycosyl groups to acceptors, is highly expressed in glia and crucial for Drosophila lifespan. By integrating multiomics data, we demonstrated that glial Ugt35b plays key roles in regulating glycerolipid and glycerophospholipid metabolism in the brain. Notably, we found that Ugt35b and Lsd-2 are co-expressed in glia and confirmed their protein interaction in vivo. Knockdown of Ugt35b significantly reduced LD formation by downregulating Lsd-2 expression, while overexpression of Lsd-2 partially rescued the shortened lifespan in glial Ugt35b RNAi flies. Our findings reveal the crucial role of glial Ugt35b in regulating LD formation to maintain brain lipid homeostasis and support Drosophila lifespan.

NCAPH Promotes the Proliferation of Prostate Cancer Cells Via Modulating the E2F1 Mediated PI3K/AKT/mTOR Axis

Prostate cancer (PCa) remains a major challenge in oncology, driving the need for continuous exploration and development of innovative treatment strategies. NCAPH plays a critical role in tumorigenesis and progression across multiple cancer types; however, its specific role in PCa has yet to be fully understood. This study aims to elucidate the biological functions of NCAPH in PCa. Our findings reveal that NCAPH gene expression is upregulated in PCa patients and correlates with poor prognosis. Enrichment analysis, flow cytometry, and correlation analysis demonstrate that NCAPH promotes the PI3K/AKT/mTOR pathway and facilitates cell cycle transition in PCa cells. Additionally, we identified E2F1 as a novel downstream target of NCAPH in PCa cells. Mechanistically, ChIP analysis showed that NCAPH regulates E2F1 transcription by binding to the proximal promoter of E2F1, subsequently stimulating the PI3K/AKT/mTOR pathway and activating downstream targets for cell cycle progression in PCa cells. Notably, combining NCAPH knockdown with an mTOR inhibitor (Everolimus) or a cyclin-dependent kinase inhibitor (Flavopiridol) demonstrated promising anti-tumor effects both in vitro and in vivo. This study highlights the significant pro-tumor role of NCAPH in PCa and suggests its potential as a therapeutic target.