Mex-3 RNA binding family member A (MEX3A)/circMPP6 complex promotes colorectal cancer progression by inhibiting autophagy.

RNA-binding proteins (RBPs)-RNA networks have contributed to cancer development. Circular RNAs (circRNAs) are considered as protein recruiters; nevertheless, the patterns of circRNA-protein interactions in colorectal cancer (CRC) are still lacking. Processing bodies (PBs) formed through liquid-liquid phase separation (LLPS) are membrane-less organelles (MLOs) consisting of RBPs and RNA. Previous evidence suggests a connection between PBs dynamics and cancer progression. Despite the increasingly acknowledged crucial role of RBPs and RNA in the accumulation and maintenance of MLOs, there remains a lack of specific research on the interactions between PBs-related RBPs and circRNAs in CRC. Herein, we identify that MEX-3 RNA binding family member A (MEX3A), frequently upregulated in CRC tissues, predicts poorer patient survival. Elevated MEX3A accelerates malignance and inhibits autophagy of CRC cells. Importantly, MEX3A undergoes intrinsically disordered regions (IDRs)-dependent LLPS in the cytoplasm. Specifically, circMPP6 acts as a scaffold to facilitate the interaction between MEX3A and PBs proteins. The MEX3A/circMPP6 complex modulates PBs dynamic and promotes UPF-mediated phosphodiesterase 5A (PDE5A) mRNA degradation, consequently leading to the aggressive properties of CRC cells. Clinically, CRC patients exhibiting high MEX3A expression and low PDE5A expression have the poorest overall survival. Our findings reveal a collaboration between MEX3A and circMPP6 in the regulation of mRNA decay through triggering the PBs aggregation, which provides prognostic markers and/or therapeutic targets for CRC.

A quest for cytosolic sequons and their functions.

Evolution shapes protein sequences for their functions. Here, we studied the moonlighting functions of the N-linked sequon NXS/T, where X is not P, in human nucleocytosolic proteins. By comparing membrane and secreted proteins in which sequons are well known for N-glycosylation, we discovered that cyto-sequons can participate in nucleic acid binding, particularly in zinc finger proteins. Our global studies further discovered that sequon occurrence is largely proportional to protein length. The contribution of sequons to protein functions, including both N-glycosylation and nucleic acid binding, can be regulated through their density as well as the biased usage between NXS and NXT. In proteins where other PTMs or structural features are rich, such as phosphorylation, transmembrane ɑ-helices, and disulfide bridges, sequon occurrence is scarce. The information acquired here should help understand the relationship between protein sequence and function and assist future protein design and engineering.

Role of autophagy and proteostasis in neurodegenerative diseases: Exploring the therapeutic interventions.

Neurodegenerative disorders are devastating disorders characterized by gradual loss of neurons and cognition or mobility impairment. The common pathological features of these diseases are associated with the accumulation of misfolded or aggregation of proteins. The pivotal roles of autophagy and proteostasis in maintaining cellular health and preventing the accumulation of misfolded proteins, which are associated with neurodegenerative diseases like Huntington's disease (HD), Alzheimer's disease (AD), and Parkinson's disease (PD). This article presents an in-depth examination of the interplay between autophagy and proteostasis, highlighting how these processes cooperatively contribute to cellular homeostasis and prevent pathogenic protein aggregate accumulation. Furthermore, the review emphasises the potential therapeutic implications of targeting autophagy and proteostasis to mitigate neurodegenerative diseases. While advancements in research hold promise for developing novel treatments, the article also addresses the challenges and complexities associated with modulating these intricate cellular pathways. Ultimately, advancing understanding of the underlying mechanism of autophagy and proteostasis in neurodegenerative disorders provides valuable insights into potential therapeutic avenues and future research directions.

Profiling Complex RAS-Effector Interactions Using NMR Spectroscopy.

Knowledge of how effectors interact with RAS GTPases is key to understanding how these switch-like proteins function in cells. Effectors bind specifically to GTP-loaded RAS using RAS association (RA) or RAS binding domains (RBDs) that show wide-ranging affinities and thermodynamic characteristics. Both normal development and RAS-induced tumorigenesis depend on multiple distinct effector proteins that are frequently co-expressed and co-localized, suggesting an antagonistic nature to signaling whereby multiple proteins compete for a limited pool of activated GTPase. NMR spectroscopy offers a powerful approach to multiplex effectors and/or regulatory enzymes and quantifies their interaction with RAS, expanding our biophysical and systems-level understanding of RAS signaling in a more integrated and physiologically relevant setting. Here we describe a method to directly quantitate GTPase binding to competing effectors, using wild-type KRAS complex with ARAF and PLCε1 as a model. Unlabeled RBD/RA domains are added simultaneously to isotopically labeled RAS, and peak intensities at chemical shifts characteristic of individually bound domains provide quantitation. Similar competition-based assays can be run with small molecule interactors, GEF/GAP domains, or regulatory enzymes that drive posttranslational modifications. Such efforts bring in vitro interaction experiments in line with more complex cellular environments.

Immature neutrophils in cord blood exert increased expression of genes associated with antimicrobial function.

Introduction: The immune systems of both the mother and the newborn face significant challenges during birth. Proper immune regulation after birth is essential for the survival of neonates. Numerous studies have demonstrated that the neonatal immune system is relatively immature, particularly in its adaptive arm, placing the primary responsibility for immune surveillance on innate immunity. Methods: Given the significant role of neutrophils in protecting the neonate after birth, we conducted a study investigating the properties of neutrophils in newborn cord blood using various methodological approaches. Results: Our findings demonstrate the presence of immature low-density neutrophils in the cord blood, which are likely responsible for the observed elevated expression of genes coding for proteins essential to antimicrobial response, including myeloperoxidase, neutrophils elastase, and defensins. Discussion: We propose that these cells function normally and support the protection of newborns early after birth. Furthermore, our results suggest that the mode of delivery might significantly influence the programming of neutrophil function. The presented findings emphasize the importance of distinct neutrophil subpopulations in neonatal immunity and their potential impact on early postnatal health.

Proto-neural networks from thermal proteins.

Proteinoids are synthetic polymers that have structural similarities to natural proteins, and their formation is achieved through the application of heat to amino acid combinations in a dehydrated environment. The thermal proteins, initially synthesised by Sidney Fox during the 1960s, has the ability to undergo self-assembly, resulting in the formation of microspheres that resemble cells. These microspheres have fascinating biomimetic characteristics. In recent studies, substantial advancements have been made in elucidating the electrical signalling phenomena shown by proteinoids, hence showcasing their promising prospects in the field of neuro-inspired computing. This study demonstrates the advancement of experimental prototypes that employ proteinoids in the construction of fundamental neural network structures. The article provides an overview of significant achievements in proteinoid systems, such as the demonstration of electrical excitability, emulation of synaptic functions, capabilities in pattern recognition, and adaptability of network structures. This study examines the similarities and differences between proteinoid networks and spontaneous neural computation. We examine the persistent challenges associated with deciphering the underlying mechanisms of emergent proteinoid-based intelligence. Additionally, we explore the potential for developing bio-inspired computing systems using synthetic thermal proteins in forthcoming times. The results of this study offer a theoretical foundation for the advancement of adaptive, self-assembling electronic systems that operate using artificial bio-neural principles.

Perspectives on Protein-Nanoparticle Interactions at the In Vivo Level.

The distinct features of nanoparticles have provided a vast opportunity of developing new diagnosis and therapy strategies for miscellaneous diseases. Although a few nanomedicines are available in the market or in the translation stage, many important issues are still unsolved. When entering the body, nanomaterials will be quickly coated by proteins from their surroundings, forming a corona on their surface, the so-called protein corona. Studies have shown that the protein corona has many important biological implications, particularly at the in vivo level. For example, they can promote the immune system to rapidly clear these outer materials and prevent nanoparticles from playing their designed role in therapy. In this Perspective, the available techniques for characterizing protein-nanoparticle interactions are critically summarized. Effects of nanoparticle properties and environmental factors on protein corona formation, which can further regulate the in vivo fate of nanoparticles, are highlighted and discussed. Moreover, recent progress on the biomedical application of protein corona-engineered nanoparticles is introduced, and future directions for this important yet challenging research area are also briefly discussed.

The MBO2/FAP58 heterodimer stabilizes assembly of inner arm dynein b and reveals axoneme asymmetries involved in ciliary waveform.

Cilia generate three-dimensional waveforms required for cell motility and transport of fluid, mucus, and particles over the cell surface. This movement is driven by multiple dynein motors attached to nine outer doublet microtubules that form the axoneme. The outer and inner arm dyneins are organized into 96-nm repeats tandemly arrayed along the length of the doublets. Motility is regulated in part by projections from the two central pair microtubules that contact radial spokes located near the base of the inner dynein arms in each repeat. Although much is known about the structures and protein complexes within the axoneme, many questions remain about the regulatory mechanisms that allow the cilia to modify their waveforms in response to internal or external stimuli. Here, we used Chlamydomonas mbo (move backwards only) mutants with altered waveforms to identify at least two conserved proteins, MBO2/CCDC146 and FAP58/CCDC147, that form part of a L-shaped structure that varies between doublet microtubules. Comparative proteomics identified additional missing proteins that are altered in other motility mutants, revealing overlapping protein defects. Cryo-electron tomography and epitope tagging revealed that the L-shaped, MBO2/FAP58 structure interconnects inner dynein arms with multiple regulatory complexes, consistent with its function in modifying the ciliary waveform.

Advancing the allergenicity assessment of new proteins using a text mining resource.

With a society increasingly demanding alternative protein food sources, new strategies for evaluating protein safety issues, such as allergenic potential, are needed. Large-scale and systemic studies on allergenic proteins are hindered by the limited and non-harmonized clinical information available for these substances in dedicated databases. A missing key information is that representing the symptomatology of the allergens, especially given in terms of standard vocabularies, that would allow connecting with other biomedical resources to carry out different studies related to human health. In this work, we have generated the first resource with a comprehensive annotation of allergens' symptomatology, using a text-mining approach that extracts significant co-mentions between these entities from the scientific literature (PubMed, ∼36 million abstracts). The method identifies statistically significant co-mentions between the textual descriptions of the two types of entities in the literature as indication of relationship. 1,180 clinical signs extracted from the Human Phenotype Ontology, the Medical Subject Heading terms of PubMed together with other allergen-specific symptoms, were linked to 1,036 unique allergens annotated in two main allergen-related public databases via 14,009 relationships. This novel resource, publicly available through an interactive web interface, could serve as a starting point for future manually curated compilation of allergen symptomatology.

Proteomic analysis of fipronil-induced molecular defects in spermatozoa.

The phenylpyrazole insecticide fipronil has wide-ranging applications from agriculture to public health to control undesirable organisms. However, several studies have reported the residual environmental hazards of fipronil and demonstrated its harmful effects even in mammalian reproduction. Therefore, this study was conducted to demonstrate the mode of action of fipronil on mouse spermatozoa. We treated fipronil to spermatozoa and performed comprehensive function evaluations. Moreover, proteomic analyses were conducted to identify the alteration of protein expression levels in spermatozoa. Most of sperm motility and kinematic parameters and intracellular ATP levels were diminished, and the spontaneous acrosome reaction was promoted after treatment with fipronil. Proteomic analyses revealed altered expression levels of 14 proteins after treatment. These proteins have been reported to be associated with sperm-specific pathways, prominently the cytoskeleton of the sperm, "9 + 2" axoneme composition, metabolism, and fertility. Collectively, our results showed that fipronil alters sperm functional-related proteins and therefore influences male fertility. This study elucidates the possible reproductive toxic hazards associated with male infertility through aberrant suppression of sperm proteins.

Detection and classification of the integrative conjugative elements of Lactococcus lactis.

Lactococcus lactis is widely applied by the dairy industry for the fermentation of milk into products such as cheese. Adaptation of L. lactis to the dairy environment often depends on functions encoded by mobile genetic elements (MGEs) such as plasmids. Other L. lactis MGEs that contribute to industrially relevant traits like antimicrobial production and carbohydrate utilization capacities belong to the integrative conjugative elements (ICE). Here we investigate the prevalence of ICEs in L. lactis using an automated search engine that detects colocalized, ICE-associated core-functions (involved in conjugation or mobilization) in lactococcal genomes. This approach enabled the detection of 36 candidate-ICEs in 69 L. lactis genomes. By phylogenetic analysis of conserved protein functions encoded in all lactococcal ICEs, these 36 ICEs could be classified in three main ICE-families that encompass 7 distinguishable ICE-integrases and are characterized by apparent modular-exchangeability and plasticity. Finally, we demonstrate that phylogenetic analysis of the conjugation-associated VirB4 ATPase function differentiates ICE- and plasmid-derived conjugation systems, indicating that conjugal transfer of lactococcal ICEs and plasmids involves genetically distinct machineries. Our genomic analysis and sequence-based classification of lactococcal ICEs creates a comprehensive overview of the conserved functional repertoires encoded by this family of MGEs in L. lactis, which can facilitate the future exploitation of the functional traits they encode by ICE mobilization to appropriate starter culture strains.

ReLo is a simple and rapid colocalization assay to identify and characterize direct protein-protein interactions.

The characterization of protein-protein interactions (PPIs) is fundamental to the understanding of biochemical processes. Many methods have been established to identify and study direct PPIs; however, screening and investigating PPIs involving large or poorly soluble proteins remains challenging. Here, we introduce ReLo, a simple, rapid, and versatile cell culture-based method for detecting and investigating interactions in a cellular context. Our experiments demonstrate that ReLo specifically detects direct binary PPIs. Furthermore, we show that ReLo bridging experiments can also be used to determine the binding topology of subunits within multiprotein complexes. In addition, ReLo facilitates the identification of protein domains that mediate complex formation, allows screening for interfering point mutations, and it is sensitive to drugs that mediate or disrupt an interaction. In summary, ReLo is a simple and rapid alternative for the study of PPIs, especially when studying structurally complex proteins or when established methods fail.

The GFPT2-O-GlcNAcylation-YBX1 axis promotes IL-18 secretion to regulate the tumor immune microenvironment in pancreatic cancer.

The immunosuppressive microenvironment caused by several intrinsic and extrinsic mechanism has brought great challenges to the immunotherapy of pancreatic cancer. We identified GFPT2, the key enzyme in hexosamine biosynthesis pathway (HBP), as an immune-related prognostic gene in pancreatic cancer using transcriptome sequencing and further confirmed that GFPT2 promoted macrophage M2 polarization and malignant phenotype of pancreatic cancer. HBP is a glucose metabolism pathway leading to the generation of uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), which is further utilized for protein O-GlcNAcylation. We confirmed GFPT2-mediated O-GlcNAcylation played an important role in regulating immune microenvironment. Through cellular proteomics, we identified IL-18 as a key downstream of GFPT2 in regulating the immune microenvironment. Through CO-IP and protein mass spectrum, we confirmed that YBX1 was O-GlcNAcylated and nuclear translocated by GFPT2-mediated O-GlcNAcylation. Then, YBX1 functioned as a transcription factor to promote IL-18 transcription. Our study elucidated the relationship between the metabolic pathway of HBP in cancer cells and the immune microenvironment, which might provide some insights into the combination therapy of HBP vulnerability and immunotherapy in pancreatic cancer.

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces.

The Protein Structure Transformer (PeSTo), a geometric transformer, has exhibited exceptional performance in predicting protein-protein binding interfaces and distinguishing interfaces with nucleic acids, lipids, small molecules, and ions. In this study, we introduce PeSTo-Carbs, an extension of PeSTo specifically engineered to predict protein-carbohydrate binding interfaces. We evaluate the performance of this approach using independent test sets and compare them with those of previous methods. Furthermore, we highlight the model's capability to specialize in predicting interfaces involving cyclodextrins, a biologically and pharmaceutically significant class of carbohydrates. Our method consistently achieves remarkable accuracy despite the scarcity of available structural data for cyclodextrins.

Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations.

Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.

Protein disulfide isomerase cleaves allosteric disulfides in histidine-rich glycoprotein to regulate thrombosis.

The essence of difference between hemostasis and thrombosis is that the clotting reaction is a highly fine-tuned process. Vascular protein disulfide isomerase (PDI) represents a critical mechanism regulating the functions of hemostatic proteins. Herein we show that histidine-rich glycoprotein (HRG) is a substrate of PDI. Reduction of HRG by PDI enhances the procoagulant and anticoagulant activities of HRG by neutralization of endothelial heparan sulfate (HS) and inhibition of factor XII (FXIIa) activity, respectively. Murine HRG deficiency (Hrg-/-) leads to delayed onset but enhanced formation of thrombus compared to WT. However, in the combined FXII deficiency (F12-/-) and HRG deficiency (by siRNA or Hrg-/-), there is further thrombosis reduction compared to F12-/- alone, confirming HRG's procoagulant activity independent of FXIIa. Mutation of target disulfides of PDI leads to a gain-of-function mutant of HRG that promotes its activities during coagulation. Thus, PDI-HRG pathway fine-tunes thrombosis by promoting its rapid initiation via neutralization of HS and preventing excessive propagation via inhibition of FXIIa.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites.

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Functional protein dynamics in a crystal.

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

SadNet: a novel multimodal fusion network for protein-ligand binding affinity prediction.

Protein-ligand binding affinity prediction plays an important role in the field of drug discovery. Existing deep learning-based approaches have significantly improved the efficiency of protein-ligand binding affinity prediction through their excellent inductive bias capability. However, these methods only focus on fragmented three-dimensional data, which truncates the integrity of pocket data, leading to the neglect of potential long-range interactions. In this paper, we propose a dual-stream framework, with amino acid sequence assisting the atomic data fusion for graph neural network (termed SadNet), to fuse both 3D atomic data and sequence data for more accurate prediction results. In detail, SadNet consists of a pocket module and a sequence module. The sequence module expands the "receptive field" of the pocket module through a mid-term virtual node fusion. To better integrate sequence-level information from the sequence module and 3D structural information from the pocket module, we incorporate structural information for each amino acid within the sequence module. Besides, to better understand the intrinsic relationship between sequences and 3D atomic information, our SadNet utilizes information stacking from both the early stage and later stage. Experimental results on publicly available benchmark datasets demonstrate the superiority of the proposed dual-stream approach over the state-of-the-art alternatives. The code of this work is available online at https://github.com/wardhong/SadNet.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces.

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 ß-lactamase.