AlphaFold2-multimer guided high-accuracy prediction of typical and atypical ATG8-binding motifs

Structure of the human autophagy factor EPG5 and the molecular basis of its conserved mode of interaction with Atg8-family proteins

The multi-step macroautophagy/autophagy process ends with the cargo-laden autophagosome fusing with the lysosome to deliver the materials to be degraded. The metazoan-specific autophagy factor EPG5 plays a crucial role in this step by enforcing fusion specificity and preventing mistargeting. How EPG5 exerts its critical function and how its deficiency leads to diverse phenotypes of the rare multi-system disorder Vici syndrome are not fully understood. Here, we report the first structure of human EPG5 (HsEPG5) determined by cryo-EM and AlphaFold2 modeling. Our structure revealed that HsEPG5 is constructed from helical bundles analogous to tethering factors in membrane trafficking pathways but contains a unique protruding thumb domain positioned adjacent to the atypical tandem LIR motifs involved in interaction with the GABARAP subfamily of Atg8-family proteins. Our NMR spectroscopic, molecular dynamics simulations and AlphaFold modeling studies showed that the HsEPG5 tandem LIR motifs only bind the canonical LIR docking site (LDS) on GABARAP without engaging in multivalent interaction. Our co-immunoprecipitation analysis further indicated that full-length HsEPG5-GABARAP interaction is mediated primarily by LIR1. Finally, our biochemical affinity isolation, X-ray crystallographic analysis, affinity measurement, and AlphaFold modeling demonstrated that this mode of binding is observed between Caenorhabditis elegans EPG-5 and its Atg8-family proteins LGG-1 and LGG-2. Collectively our work generated novel insights into the structural properties of EPG5 and how it potentially engages with the autophagosome to confer fusion specificity.ABBREVIATIONS: ATG: autophagy related; CSP: chemical shift perturbation; eGFP: enhanced green fluoresent protein; EM: electron microscopy; EPG5: ectopic P-granules 5 autophagy tethering factor; GST: glutathione S-transferase; HP: hydrophobic pocket; HSQC: heteronuclear single-quantum correlation; ITC: isothermal titration calorimetry; LDS: LC3 docking site; LIR: LC3-interacting region; MD: molecular dynamics; NMR: nuclear magnetic resonance; TEV: tobacco etch virus.

The ribonuclease RNase T2 mediates selective autophagy of ribosomes induced by starvation in Saccharomyces cerevisiae

RNase T2 is a conserved ribonuclease, playing essential and diverse roles despite its simple enzymatic activity. Saccharomyces cerevisiae RNase T2, known as Rny1p, is stress-responsive and localizes in the vacuole. Upon starvation, ribosomes are degraded by autophagy, in which Rny1p mediates rRNA degradation. However, whether the ribosomal degradation is selective or nonselective is still being determined in S. cerevisiae. Here, we elucidated novel aspects of ribosome degradation mechanisms and the function of Rny1p in stress response. We discovered that most ribosomes are selectively degraded, whose mechanism differs from the previously reported selective degradation process called "ribophagy." Rsa1p, a factor involved in assembling 60S ribosomal subunits, is revealed to interact with Atg8p and act as a receptor for selective ribosome degradation in the cytosol. The accumulation of rRNA in vacuoles, due to lack of Rny1p, leads to a decrease in nonselective autophagic activity. This is one of the reasons for the inability of Rny1p-deficient strains to adapt to starvation conditions. Rny1p is also reported to be secreted and associated with the cell wall. We revealed that a C-terminal extension of Rny1p, characteristic in some fungal RNase T2, is required to anchor the cell wall. Some nonfungal RNase T2 proteins also have C-terminal extensions. However, their sequences and structures differ from those of fungal RNase T2, suggesting that their biological functions may also be distinct. The diversity of C-terminal extensions across different organisms is thought to be one reason why RNase T2 plays various roles.

Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold's black box

Introduction

LC8 is a hub protein involved in many processes from tumor suppression and cell cycle regulation to neurotransmission and viral infection. Despite recent progress, prediction of binding sites for LC8 is plagued by motif variability and a multitude of weakly binding motifs, especially when binding depends on multivalency. Our binding site prediction algorithm, LC8Pred has proven useful for uncovering new LC8 binders, but is insufficient for finding all LC8 binding sites.

Methods

To address this, we probed the ability of a general structure predictor, AlphaFold, to predict whether a given sequence binds to LC8. Certain combinations of in-built AlphaFold scores were extracted and distributions of scores of binders were compared to scores of nonbinders.

Results

AlphaFold successfully places proteins at the correct interface of LC8. A set of threshold values of built-in AlphaFold scores enables differentiation between known binders and nonbinders with minimal false positive (8%) and acceptable false negative rates (20%). This cutoff, along with a more inclusive cutoff, was used to predict elusive LC8 binding sites in proteins known to bind LC8.

Discussion

Correlations between binding affinities and AlphaFold scores provide insight into the black box and indicate that AlphaFold learned an inaccurate energy function that nevertheless is useful for making inferences and conclusions about physical systems. Binding sites predicted by this method can be prioritized for investigation by comparing to result by LC8Pred, local structure, and evolutionary conservation.

From Lesions to Lessons: Two Decades of Filamentous Plant Pathogen Genomics

Many filamentous microorganisms, such as fungi and oomycetes, have evolved the ability to colonize plants and cause devastating crop diseases. Coevolutionary conflicts with their hosts have shaped the genomes of these plant pathogens. Over the past 20 years, genomics and genomics-enabled technologies have revealed remarkable diversity in genome size, architecture, and gene regulatory mechanisms. Technical and conceptual advances continue to provide novel insights into evolutionary dynamics, diversification of distinct genomic compartments, and facilitated molecular disease diagnostics. In this review, we discuss how genomics has advanced our understanding of genome organization and plant-pathogen coevolution and provide a perspective on future developments in the field. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The graphene-based affinity cryo-EM grid for the endogenous protein structure determination

Following recent advancements in cryo-electron microscopy (cryo-EM) instrumentation and software algorithms, the next bottleneck in achieving high-resolution cryo-EM structures arises from sample preparation. To overcome this, we developed a graphene-based affinity cryo-EM grid, the Graffendor (GFD) grid, to target low-abundance endogenous protein complexes. To maintain grid quality and consistency within a single batch of 36 grids, we established a one-step crosslinking batch-production method using genetically modified ALFA nanobody as affinity probe (GFD-A grid). Using low concentrations of β-galactosidase-2xALFA, we demonstrated the GFD-A grid's efficiency in capturing tagged proteins and resolving its cryo-EM structure at 2.71 Å. To test its application for endogenous proteins, we engineered yeast cells with a C-terminal tandem affinity tag (3xALFA-Tev-3xFlag: ATF) at Pop6, a shared component of RNase MRP and RNase P. Cryo-EM structures of RNase MRP and RNase P were resolved at 3.3 Å and 3.0 Å from cell lysates, and 3.6 Å and 3.9 Å from anti-flag elution, respectively. Notably, additional densities were observed in the structures obtained from cell lysates, which were absent in those from the anti-FLAG eluate. These findings establish the GFD-A grid as a robust platform for investigating endogenous proteins, capable of capturing transient interactions and enhancing the resolution of challenging cryo-EM structures with greater efficiency.

Discovery of Evolutionary Loss of the Ubiquitin-like Autophagy-Related ATG12 System in a Lineage of Apicomplexa

The autophagy-related ubiquitin-like conjugation systems, the ATG8 and ATG12 systems, are universally conserved in eukaryotes. However, the covalent bond in the ATG12 system has recently been shown to be evolutionarily lost in Apicomplexa. Here, we show that all genes associated with the ATG12 system are absent in piroplasmida, a lineage within Apicomplexa. Comparative genomics of ATGs further shows that piroplasm ATG3 has lost the region necessary for ATG12 binding. However, our in vitro functional analysis using recombinant proteins demonstrated that ATG3 retained the ability to interact with ATG8 in Babesia bovis, a model species in piroplasmida. These findings provide evidence that the ATG8 system is functional, while the ATG12 system is completely lost in the common ancestor of piroplasmida and highlight the evolutionary flexibility of the ATG12 system in Apicomplexa.

The receptor binding mechanism of mouse sPLA2 group IIE

Secreted phospholipase A2s (sPLA2s) participate in physiological function by their enzyme and receptor binding activity. Muscle-type phospholipase A2 receptor (M-type PLA2R) is the sPLA2 binding protein with the highest affinity so far, and also inhibits the enzyme activity of sPLA2. There is species specificity and pH dependence for the binding of M-type PLA2R to sPLA2. Mouse sPLA2 Group IIE (mGIIE) has been verified to have a high affinity for mouse M-type PLA2R (M-type mPLA2R) at the nanomolar scale. For further exploration of the receptor binding mechanism of GIIE, in this study, we use Alphafold Multimer to generate complex models of mGIIE with the M-type mPLA2R ectodomain, wild-type CTLD5 domain of mPLA2R, and three CTLD5 mutants, respectively. mPLA2R-mGIIE models exhibit heterogeneous extended mPLA2R conformations with uncovered sPLA2-binding surface of CTLD5 domain. Complexed models of mGIIE with wild-type and mutated mCTLD5 further confirm that helix α1 of mCTLD5, especially essential residues F838 and W842, interact with the substrate pocket of mGIIE and thus inhibit its enzyme activity. Peptides from helix α1 of mCTLD5 are verified to inhibit the enzymatic activity of mGIIE. This AI-guided research would substantially accelerate our understanding of the functional study of GIIE, and provide the lead-peptide for the further inhibitor design of sPLA2.

Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy

G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.

Identification of potential selective autophagy receptors from protein-content profiling of autophagosomes

Selective autophagy receptors (SARs) are central to cellular homeostatic and organellar recycling pathways. Over the last two decades, more than 30 SARs have been discovered and validated using a variety of experimental approaches ranging from cell biology to biochemistry, including high-throughput imaging and screening methods. Yet, the extent of selective autophagy pathways operating under various cellular contexts, for example, under basal and starvation conditions, remains unresolved. Currently, our knowledge of all known SARs and their associated cargo components is fragmentary and limited by experimental data with varying degrees of resolution. Here, we use classical predictive and modeling approaches to integrate high-quality autophagosome content profiling data with disparate datasets. We identify a global set of potential SARs and their associated cargo components active under basal autophagy, starvation-induced, and proteasome-inhibition conditions. We provide a detailed account of cellular components, biochemical pathways, and molecular processes that are degraded via autophagy. Our analysis yields a catalog of new potential SARs that satisfy the characteristics of bonafide, well-characterized SARs. We categorize them by the subcellular compartments they emerge from and classify them based on their likely mode of action. Our structural modeling validates a large subset of predicted interactions with the human ATG8 family of proteins and shows characteristic, conserved LC3-interacting region (LIR)-LIR docking site (LDS) and ubiquitin-interacting motif (UIM)-UIM docking site (UDS) binding modes. Our analysis also revealed the most abundant cargo molecules targeted by these new SARs. Our findings expand the repertoire of SARs and provide unprecedented details into the global autophagic state of HeLa cells. Taken together, our findings provide motivation for the design of new experiments, testing the role of these novel factors in selective autophagy.

GABARAP interacts with EGFR - supporting the unique role of this hAtg8 protein during receptor trafficking

The human Atg8 family member GABARAP is involved in numerous autophagy-related and -unrelated processes. We recently observed that specifically the deficiency of GABARAP enhances epidermal growth factor receptor (EGFR) degradation upon ligand stimulation. Here, we report on two putative LC3-interacting regions (LIRs) within EGFR, the first of which (LIR1) is selected as a GABARAP binding site in silico. Indeed, in vitro interaction studies reveal preferential binding of LIR1 to GABARAP and GABARAPL1. Our X-ray data demonstrate interaction of core LIR1 residues FLPV with both hydrophobic pockets of GABARAP suggesting canonical binding. Although LIR1 occupies the LIR docking site, GABARAP Y49 and L50 appear dispensable in this case. Our data support the hypothesis that GABARAP affects the fate of EGFR at least in part through direct binding.

Unlocking protein-protein interactions in plants: a comprehensive review of established and emerging techniques

Protein-protein interactions orchestrate plant development and serve as crucial elements for cellular and environmental communication. Understanding these interactions offers a gateway to unravel complex protein networks that will allow a better understanding of nature. Methods for the characterization of protein-protein interactions have been around over 30 years, yet the complexity of some of these interactions has fueled the development of new techniques that provide a better understanding of the underlying dynamics. In many cases, the application of these techniques is limited by the nature of the available sample. While some methods require an in vivo set-up, others solely depend on protein sequences to study protein-protein interactions via an in silico set-up. The vast number of techniques available to date calls for a way to select the appropriate tools for the study of specific interactions. Here, we classify widely spread tools and new emerging techniques for the characterization of protein-protein interactions based on sample requirements while providing insights into the information that they can potentially deliver. We provide a comprehensive overview of commonly used techniques and elaborate on the most recent developments, showcasing their implementation in plant research.

Structural insights into Frizzled3 through nanobody modulators

The Wnt receptor Frizzled3 (FZD3) is important for brain axonal development and cancer progression. We report structures of FZD3 in complex with extracellular and intracellular binding nanobodies (Nb). The crystal structure of Nb8 in complex with the FZD3 cysteine-rich domain (CRD) reveals that the nanobody binds at the base of the lipid-binding groove and can compete with Wnt5a. Nb8 fused with the Dickkopf-1 C-terminal domain behaves as a FZD3-specific Wnt surrogate, activating β-catenin signalling. The cryo-EM structure of FZD3 in complex with Nb9 reveals partially resolved density for the CRD, which exhibits positional flexibility, and a transmembrane conformation that resembles active GPCRs. Nb9 binds to the cytoplasmic region of FZD3 at the putative Dishevelled (DVL) or G protein-binding site, competes with DVL binding, and inhibits GαS coupling. In combination, our FZD3 structures with nanobody modulators map extracellular and intracellular interaction surfaces of functional, and potentially therapeutic, relevance.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Selective Autophagy of Macromolecular Complexes: What Does It Take to be Taken?

Proteins are known to perform an astonishing array of functions thanks to their ability to cooperate and modulate each other's properties. Inside cells, proteins can assemble into large multi-subunit complexes to carry out complex cellular functions. The correct assembly and maintenance of the functional state of macromolecular protein complexes is crucial for human health. Failure to do so leads to loss of function and potential accumulation of harmful materials, which is associated with a variety of human diseases such as neurodegeneration and cancer. Autophagy engulfs cytosolic material in autophagosomes, and therefore is best suited to eliminate intact macromolecular complexes without disassembling them, which could interfere with de novo assembly. In this review, we discuss the role of autophagy in the selective degradation of macromolecular complexes. We highlight the current state of knowledge for different macromolecular complexes and their selective autophagic degradation. We emphasize the gaps in our understanding of what it takes for these large macromolecular complexes to be degraded and point to future work that may shed light on the regulation of the selective degradation of macromolecular complexes by autophagy.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

Peptidomimetics design and characterization: Bridging experimental and computer-based approaches

Peptidomimetics, designed to mimic peptide biological activity with more drug-like properties, are increasingly pivotal in medicinal chemistry. They offer enhanced systemic delivery, cell penetration, target specificity, and protection against peptidases when compared to their native peptide counterparts. Already utilized in treating diverse diseases like neurodegenerative disorders, cancer and infectious diseases, their future in medicine seems bright, with many peptidomimetics in clinical trials or development stages. Peptidomimetics are well-suited for addressing disturbed protein-protein interactions (PPIs), which often underlie various pathologies. Structural biology and computational methods like molecular dynamics simulations facilitate rational design, whereas machine learning algorithms accelerate protein structure prediction, enabling efficient drug development. Experimental validation via various spectroscopic, biophysical, and biochemical assays confirms computational predictions and guides further optimization. Peptidomimetics, with their tailored constrained structures, represent a frontier in drug design focused on targeting PPIs. In this overview, we present a comprehensive landscape of peptidomimetics, encompassing perspectives on involvement in pathologies, chemical strategies, and methodologies for their characterization, spanning in silico, in vitro and in cell approaches. With increasing interest from pharmaceutical sectors, peptidomimetics hold promise for revolutionizing therapeutic approaches, marking a new era of precision drug discovery.

A RabGAP negatively regulates plant autophagy and immune trafficking

Plants rely on autophagy and membrane trafficking to tolerate stress, combat infections, and maintain cellular homeostasis. However, the molecular interplay between autophagy and membrane trafficking is poorly understood. Using an AI-assisted approach, we identified Rab3GAP-like (Rab3GAPL) as a key membrane trafficking node that controls plant autophagy negatively. Rab3GAPL suppresses autophagy by binding to ATG8, the core autophagy adaptor, and deactivating Rab8a, a small GTPase essential for autophagosome formation and defense-related secretion. Rab3GAPL reduces autophagic flux in three model plant species, suggesting that its negative regulatory role in autophagy is conserved in land plants. Beyond autophagy regulation, Rab3GAPL modulates focal immunity against the oomycete pathogen Phytophthora infestans by preventing defense-related secretion. Altogether, our results suggest that Rab3GAPL acts as a molecular rheostat to coordinate autophagic flux and defense-related secretion by restraining Rab8a-mediated trafficking. This unprecedented interplay between a RabGAP-Rab pair and ATG8 sheds new light on the intricate membrane transport mechanisms underlying plant autophagy and immunity.

New insights into GATOR2-dependent interactions and its conformational changes in amino acid sensing

Eukaryotic cells coordinate growth under different environmental conditions via mechanistic target of rapamycin complex 1 (mTORC1). In the amino-acid-sensing signalling pathway, the GATOR2 complex, containing five evolutionarily conserved subunits (WDR59, Mios, WDR24, Seh1L and Sec13), is required to regulate mTORC1 activity by interacting with upstream CASTOR1 (arginine sensor) and Sestrin2 (leucine sensor and downstream GATOR1 complex). GATOR2 complex utilizes β-propellers to engage with CASTOR1, Sestrin2 and GATOR1, removal of these β-propellers results in substantial loss of mTORC1 capacity. However, structural information regarding the interface between amino acid sensors and GATOR2 remains elusive. With the recent progress of the AI-based tool AlphaFold2 (AF2) for protein structure prediction, structural models were predicted for Sentrin2-WDR24-Seh1L and CASTOR1-Mios β-propeller. Furthermore, the effectiveness of relevant residues within the interface was examined using biochemical experiments combined with molecular dynamics (MD) simulations. Notably, fluorescence resonance energy transfer (FRET) analysis detected the structural transition of GATOR2 in response to amino acid signals, and the deletion of Mios β-propeller severely impeded that change at distinct arginine levels. These findings provide structural perspectives on the association between GATOR2 and amino acid sensors and can facilitate future research on structure determination and function.

Linear motifs regulating protein secretion, sorting and autophagy in Leishmania parasites are diverged with respect to their host equivalents

The pathogenic, tropical Leishmania flagellates belong to an early-branching eukaryotic lineage (Kinetoplastida) with several unique features. Unfortunately, they are poorly understood from a molecular biology perspective, making development of mechanistically novel and selective drugs difficult. Here, we explore three functionally critical targeting short linear motif systems as well as their receptors in depth, using a combination of structural modeling, evolutionary sequence divergence and deep learning. Secretory signal peptides, endoplasmic reticulum (ER) retention motifs (KDEL motifs), and autophagy signals (motifs interacting with ATG8 family members) are ancient and essential components of cellular life. Although expected to be conserved amongst the kinetoplastids, we observe that all three systems show a varying degree of divergence from their better studied equivalents in animals, plants, or fungi. We not only describe their behaviour, but also build models that allow the prediction of localization and potential functions for several uncharacterized Leishmania proteins. The unusually Ala/Val-rich secretory signal peptides, endoplasmic reticulum resident proteins ending in Asp-Leu-COOH and atypical ATG8-like proteins are all unique molecular features of kinetoplastid parasites. Several of their critical protein-protein interactions could serve as targets of selective antimicrobial agents against Leishmaniasis due to their systematic divergence from the host.

Molecular Modulators and Receptors of Selective Autophagy: Disease Implication and Identification Strategies

Autophagy is a highly conserved physiological process that maintains cellular homeostasis by recycling cellular contents. Selective autophagy is based on the specificity of cargo recognition and has been implicated in various human diseases, including neurodegenerative diseases and cancer. Selective autophagy receptors and modulators play key roles in this process. Identifying these receptors and modulators and their roles is critical for understanding the machinery and physiological function of selective autophagy and providing therapeutic value for diseases. Using modern researching tools and novel screening technologies, an increasing number of selective autophagy receptors and modulators have been identified. A variety of Strategies and approaches, including protein-protein interactions (PPIs)-based identification and genome-wide screening, have been used to identify selective autophagy receptors and modulators. Understanding the strengths and challenges of these approaches not only promotes the discovery of even more such receptors and modulators but also provides a useful reference for the identification of regulatory proteins or genes involved in other cellular mechanisms. In this review, we summarize the functions, disease association, and identification strategies of selective autophagy receptors and modulators.

CALEOSIN 1 interaction with AUTOPHAGY-RELATED PROTEIN 8 facilitates lipid droplet microautophagy in seedlings

Lipid droplets (LDs) of seed tissues are storage organelles for triacylglycerols (TAGs) that provide the energy and carbon for seedling establishment. In the major route of LD degradation (lipolysis), TAGs are mobilized by lipases. However, LDs may also be degraded via lipophagy, a type of selective autophagy, which mediates LD delivery to vacuoles or lysosomes. The exact mechanisms of LD degradation and the mobilization of their content in plants remain unresolved. Here, we provide evidence that LDs are degraded via a process morphologically resembling microlipophagy in Arabidopsis (Arabidopsis thaliana) seedlings. We observed the entry and presence of LDs in the central vacuole as well as their breakdown. Moreover, we show co-localization of AUTOPHAGY-RELATED PROTEIN 8b (ATG8b) and LDs during seed germination and localization of lipidated ATG8 (ATG8-PE) to the LD fraction. We further demonstrate that structural LD proteins from the caleosin family, CALEOSIN 1 (CLO1), CALEOSIN 2 (CLO2), and CALEOSIN 3 (CLO3), interact with ATG8 proteins and possess putative ATG8-interacting motifs (AIMs). Deletion of the AIM localized directly before the proline knot disrupts the interaction of CLO1 with ATG8b, suggesting a possible role of this region in the interaction between these proteins. Collectively, we provide insights into LD degradation by microlipophagy in germinating seeds with a particular focus on the role of structural LD proteins in this process.

Dynamic Dimerization of Chemokine Receptors and Potential Inhibitory Role of Their Truncated Isoforms Revealed through Combinatorial Prediction

Chemokine receptors play crucial roles in fundamental biological processes. Their malfunction may result in many diseases, including cancer, autoimmune diseases, and HIV. The oligomerization of chemokine receptors holds significant functional implications that directly affect their signaling patterns and pharmacological responses. However, the oligomerization patterns of many chemokine receptors remain poorly understood. Furthermore, several chemokine receptors have highly truncated isoforms whose functional role is not yet clear. Here, we computationally show homo- and heterodimerization patterns of four human chemokine receptors, namely CXCR2, CXCR7, CCR2, and CCR7, along with their interaction patterns with their respective truncated isoforms. By combining the neural network-based AlphaFold2 and physics-based protein-protein docking tool ClusPro, we predicted 15 groups of complex structures and assessed the binding affinities in the context of atomistic molecular dynamics simulations. Our results are in agreement with previous experimental observations and support the dynamic and diverse nature of chemokine receptor dimerization, suggesting possible patterns of higher-order oligomerization. Additionally, we uncover the strong potential of truncated isoforms to block homo- and heterodimerization of chemokine receptors, also in a dynamic manner. Our study provides insights into the dimerization patterns of chemokine receptors and the functional significance of their truncated isoforms.

AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface

Adapted plant pathogens from various microbial kingdoms produce hundreds of unrelated small secreted proteins (SSPs) with elusive roles. Here, we used AlphaFold-Multimer (AFM) to screen 1879 SSPs of seven tomato pathogens for interacting with six defence-related hydrolases of tomato. This screen of 11,274 protein pairs identified 15 non-annotated SSPs that are predicted to obstruct the active site of chitinases and proteases with an intrinsic fold. Four SSPs were experimentally verified to be inhibitors of pathogenesis-related subtilase P69B, including extracellular protein-36 (Ecp36) and secreted-into-xylem-15 (Six15) of the fungal pathogens Cladosporium fulvum and Fusarium oxysporum, respectively. Together with a P69B inhibitor from the bacterial pathogen Xanthomonas perforans and Kazal-like inhibitors of the oomycete pathogen Phytophthora infestans, P69B emerges as an effector hub targeted by different microbial kingdoms, consistent with a diversification of P69B orthologs and paralogs. This study demonstrates the power of artificial intelligence to predict cross-kingdom interactions at the plant-pathogen interface.

Family-wide analysis of integrin structures predicted by AlphaFold2

Recent advances in protein structure prediction using AlphaFold2, known for its high efficiency and accuracy, have opened new avenues for comprehensive analysis of all structures within a single protein family. In this study, we evaluated the capabilities of AphaFold2 in analyzing integrin structures. Integrins are heterodimeric cell surface receptors composed of a combination of 18 α and 8 β subunits, resulting in a family of 24 different members. Both α and β subunits consist of a large extracellular domain, a short transmembrane domain, and typically, a short cytoplasmic tail. Integrins play a pivotal role in a wide range of cellular functions by recognizing diverse ligands. Despite significant advances in integrin structural studies in recent decades, high-resolution structures have only been determined for a limited subsets of integrin members, thus limiting our understanding of the entire integrin family. Here, we first analyzed the single-chain structures of 18 α and 8 β integrins in the AlphaFold2 protein structure database. We then employed the newly developed AlphaFold2-multimer program to predict the α/β heterodimer structures of all 24 human integrins. The predicted structures show a high level of accuracy for the subdomains of both α and β subunits, offering high-resolution structure insights for all integrin heterodimers. Our comprehensive structural analysis of the entire integrin family unveils a potentially diverse range of conformations among the 24 members, providing a valuable structure database for studies related to integrin structure and function. We further discussed the potential applications and limitations of the AlphaFold2-derived integrin structures.

Family-wide analysis of integrin structures predicted by AlphaFold2

Recent advances in protein structure prediction using AlphaFold2, known for its high efficiency and accuracy, have opened new avenues for comprehensive analysis of all structures within a single protein family. In this study, we evaluated the capabilities of AphaFold2 in analyzing integrin structures. Integrins are heterodimeric cell surface receptors composed of a combination of 18 α and 8 β subunits, resulting in a family of 24 different members. Both α and β subunits consist of a large extracellular domain, a short transmembrane domain, and typically, a short cytoplasmic tail. Integrins play a pivotal role in a wide range of cellular functions by recognizing diverse ligands. Despite significant advances in integrin structural studies in recent decades, high-resolution structures have only been determined for a limited subsets of integrin members, thus limiting our understanding of the entire integrin family. Here, we first analyzed the single-chain structures of 18 α and 8 β integrins in the AlphaFold2 protein structure database. We then employed the newly developed AlphaFold2-multimer program to predict the α/β heterodimer structures of all 24 human integrins. The predicted structures show a high level of accuracy for the subdomains of both α and β subunits, offering high-resolution structure insights for all integrin heterodimers. Our comprehensive structural analysis of the entire integrin family unveils a potentially diverse range of conformations among the 24 members, providing a valuable structure database for studies related to integrin structure and function. We further discussed the potential applications and limitations of the AlphaFold2-derived integrin structures.

Intercellular mitochondrial component transfer triggers ischemic cardiac fibrosis

Myocardial fibrosis is the villain of sudden cardiac death. Myocardial ischemia/reperfusion (MI/R) injury induces cardiomyocyte damage or even death, which in turn stimulates fibroblast activation and fibrosis, but the intercellular communication mechanism remains unknown. Recent studies have shown that small extracellular vesicles (sEVs) significantly contribute to intercellular communication. Whether and how sEV might mediate post-MI/R cardiomyocyte/fibroblasts communication remain unknown. Here, in vivo and in vitro MI/R models were established. We demonstrate that sEVs derived from cardiomyocyte (Myo-sEVs) carry mitochondrial components, which enter fibroblasts to initiate myocardial fibrosis. Based on bioinformatics screening and experimental verification, the activating molecule in Beclin1-regulated autophagy protein 1 (autophagy/beclin-1 regulator 1, Ambra1) was found to be a critical component of these sEV and might be a new marker for Myo-sEVs. Interestingly, release of Ambra1+-Myo-sEVs was caused by secretory rather than canonical autophagy after MI/R injury and thereby escaped degradation. In ischemic and peripheral areas, Ambra1+-Myo-sEVs were internalized by fibroblasts, and the delivered mtDNA components to activate the fibroblast cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway to promote fibroblast activation and proliferation. In addition, our data show that Ambra1 is expressed on the EV surface and cardiac-specific Ambra1 down regulation inhibits the Ambra1+-Myo-sEVs release and fibroblast uptake, effectively inhibiting ischemic myocardial fibrosis. This finding newly provides the evidence that myocardial secretory autophagy plays a role in intercellular communication during cardiac fibrosis. Ambra1 is a newly characterized molecule with bioactivity and might be a marker for Myo-sEVs, providing new therapeutic targets for cardiac remodeling.

How to convert host plants into nonhosts

Recent research demonstrates that undermining interactions between pathogen effectors and their host target proteins can reduce infection. As more effector-target pairs are identified, their structures and interaction surfaces exposed, and there is the possibility of making multiple edits to diverse plant genomes, the desire to convert crops to nonhosts could become reality.

Semisynthetic LC3 Probes for Autophagy Pathways Reveal a Noncanonical LC3 Interacting Region Motif Crucial for the Enzymatic Activity of Human ATG3

Macroautophagy is one of two major degradation systems in eukaryotic cells. Regulation and control of autophagy are often achieved through the presence of short peptide sequences called LC3 interacting regions (LIR) in autophagy-involved proteins. Using a combination of new protein-derived activity-based probes prepared from recombinant LC3 proteins, along with protein modeling and X-ray crystallography of the ATG3-LIR peptide complex, we identified a noncanonical LIR motif in the human E2 enzyme responsible for LC3 lipidation, ATG3. The LIR motif is present in the flexible region of ATG3 and adopts an uncommon β-sheet structure binding to the backside of LC3. We show that the β-sheet conformation is crucial for its interaction with LC3 and used this insight to design synthetic macrocyclic peptide-binders to ATG3. CRISPR-enabled in cellulo studies provide evidence that LIR^ATG3 is required for LC3 lipidation and ATG3∼LC3 thioester formation. Removal of LIR^ATG3 negatively impacts the rate of thioester transfer from ATG7 to ATG3.

The future is bright, the future is biotechnology

As PLOS Biology celebrates its 20th anniversary, our April issue focuses on biotechnology with articles covering different aspects of the field, from genome editing to synthetic biology. With them, we emphasize our interest in expanding our presence in biotechnology research.