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Elucidating protein-protein interaction (PPI) networks and their structural features within cells is central to understanding fundamental biology and associations of cell phenotypes with human pathologies. Owing to technological advancements during the last decade, cross-linking mass spectrometry (XL-MS) has become an enabling technology for delineating interaction landscapes of proteomes as they exist in living systems. XL-MS is unique due to its capability to simultaneously capture PPIs from native environments and uncover interaction contacts though identification of cross-linked peptides, thereby permitting the determination of both identity and connectivity of PPIs in cells. In combination with high resolution structural tools such as cryo-electron microscopy and AI-assisted prediction, XL-MS has contributed significantly to elucidating architectures of large protein assemblies. This review highlights the latest developments in XL-MS technologies and their applications in proteome-wide analysis to advance structural systems biology.

Abstract Background The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. Results Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.

Introduction: Equipped with a photosynthetic apparatus that uses the energy of solar radiation to fuel biosynthesis of organic compounds, chloroplasts are the metabolic factories of mature leaf cells. The first steps of energy conversion are catalyzed by a collection of protein complexes, which can dynamically interact with each other for optimizing metabolic efficiency under changing environmental conditions. Materials and methods: For a deeper insight into the organization of protein assemblies and their roles in chloroplast adaption to changing environmental conditions, an improved complexome profiling protocol employing a MS- cleavable cross-linker is used to stabilize labile protein assemblies during the organelle isolation procedure. Results and discussion: Changes in protein:protein interaction patterns of chloroplast proteins in response to four different light intensities are reported. High molecular mass assemblies of central chloroplast electron transfer chain components as well as the PSII repair machinery react to different light intensities. In addition, the chloroplast encoded RNA-polymerase complex was found to migrate at a molecular mass of -8 MDa, well above its previously reported molecular mass. Complexome profiling data produced during the course of this study can be interrogated by interested readers via a web-based online resource (https://complexomemap.de/projectsinteraction-chloroplasts). [ABSTRACT FROM AUTHOR]

Rho GTPases, master spatial regulators of a wide range of cellular processes, are orchestrated by complex formation with guanine nucleotide dissociation inhibitors (RhoGDIs). These have been thought to possess an unstructured N‐terminus that inhibits nucleotide exchange of their client upon binding/folding. Via NMR analyses, molecular dynamics simulations, and biochemical assays, we reveal instead pertinent structural properties transiently maintained both, in the presence and absence of the client, imposed onto the terminus context‐specifically by modulating interactions with the surface of the folded C‐terminal domain. These observations revise the long‐standing textbook picture of the GTPases' mechanism of membrane extraction. Rather than by a disorder‐to‐order transition upon binding of an inhibitory peptide, the intricate and highly selective extraction process of RhoGTPases is orchestrated via a dynamic ensemble bearing preformed transient structural properties, suitably modulated by the specific surrounding along the multi‐step process. [ABSTRACT FROM AUTHOR]

Cotton provides the most abundant natural fiber for the textile industry. The mature cotton fiber largely consists of secondary cell walls with the highest proportion of cellulose and a small amount of hemicellulose and lignin. To dissect the roles of hemicellulosic polysaccharides during fiber development, four IRREGULAR XYLEM 15 (IRX15) genes, GhIRX15-1/-2/-3/-4, were functionally characterized in cotton. These genes encode DUF579 domain-containing proteins, which are homologs of AtIRX15 involved in xylan biosynthesis. The four GhIRX15 genes were predominantly expressed during fiber secondary wall thickening, and the encoded proteins were localized to the Golgi apparatus. Each GhIRX15 gene could restore the xylan deficient phenotype in the Arabidopsis irx15irx15l double mutant. Silencing of GhIRX15s in cotton resulted in shorter mature fibers with a thinner cell wall and reduced cellulose content as compared to the wild type. Intriguingly, GhIRX15-2 and GhIRX15-4 formed homodimers and heterodimers. In addition, the GhIRX15s showed physical interaction with glycosyltransferases GhGT43C, GhGT47A and GhGT47B, which are responsible for synthesis of the xylan backbone and reducing end sequence. Moreover, the GhIRX15s can form heterocomplexes with enzymes involved in xylan modification and side chain synthesis, such as GhGUX1/2, GhGXM1/2 and GhTBL1. These findings suggest that GhIRX15s participate in fiber xylan biosynthesis and modulate fiber development via forming large multiprotein complexes. [ABSTRACT FROM AUTHOR]

Signaling proteins in eukaryotes usually comprise a catalytic domain coupled to one or several interaction domains, such as SH2 and SH3 domains. An additional class of proteins critically involved in cellular communication are adapter or scaffold proteins, which fulfill their purely non-enzymatic functions by organizing protein–protein interactions. Intriguingly, certain signaling enzymes, e.g., kinases and phosphatases, have been demonstrated to promote particular cellular functions by means of their interaction domains only. In this review, we will refer to such a function as "the adapter function of an enzyme". Though many stories can be told, we will concentrate on several proteins executing critical adapter functions in cells of the immune system, such as Bruton´s tyrosine kinase (BTK), phosphatidylinositol 3-kinase (PI3K), and SH2-containing inositol phosphatase 1 (SHIP1), as well as in cancer cells, such as proteins of the rat sarcoma/extracellular signal-regulated kinase (RAS/ERK) mitogen-activated protein kinase (MAPK) pathway. We will also discuss how these adaptor functions of enzymes determine or even undermine the efficacy of targeted therapy compounds, such as ATP-competitive kinase inhibitors. Thereby, we are highlighting the need to develop pharmacological approaches, such as proteolysis-targeting chimeras (PROTACs), that eliminate the entire protein, and thus both enzymatic and adapter functions of the signaling protein. We also review how genetic knock-out and knock-in approaches can be leveraged to identify adaptor functions of signaling proteins. [ABSTRACT FROM AUTHOR]

Mass-spectrometry based assays in structural biology studies measure either intact or digested proteins. Typically, different mass spectrometers are dedicated for such measurements: those optimized for rapid analysis of peptides or those designed for high molecular weight analysis. A commercial trapped ion mobility-quadrupole-time-of-flight (TIMS-Q-TOF) platform is widely utilized for proteomics and metabolomics, with ion mobility providing a separation dimension in addition to liquid chromatography. The ability to perform high-quality native mass spectrometry of protein complexes, however, remains largely uninvestigated. Here, we evaluate a commercial TIMS-Q-TOF platform for analyzing noncovalent protein complexes by utilizing the instrument's full range of ion mobility, MS, and MS/MS (both in-source activation and collision cell CID) capabilities. The TIMS analyzer is able to be tuned gently to yield collision cross sections of native-like complexes comparable to those previously reported on various instrument platforms. In-source activation and collision cell CID were robust for both small and large complexes. TIMS-CID was performed on protein complexes streptavidin (53 kDa), avidin (68 kDa), and cholera toxin B (CTB, 58 kDa). Complexes pyruvate kinase (237 kDa) and GroEL (801 kDa) were beyond the trapping capabilities of the commercial TIMS analyzer, but TOF mass spectra could be acquired. The presented results indicate that the commercial TIMS-Q-TOF platform can be used for both omics and native mass spectrometry applications; however, modifications to the commercial RF drivers for both the TIMS analyzer and quadrupole (currently limited to m/z 3000) are necessary to mobility analyze protein complexes greater than about 60 kDa. [ABSTRACT FROM AUTHOR]

Background: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. Results: Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships. [ABSTRACT FROM AUTHOR]

Most functional nanosystems in living organisms are constructed using multimeric assemblies that provide multiple advantages over their monomeric counterparts such as cooperative or anti‐cooperative responses, integration of multiple signals and self‐regulation. Inspired by these natural nanosystems, chemists have been synthesizing self‐assembled supramolecular systems over the last 50 years with increasing complexity with applications ranging from biosensing, drug delivery, synthetic biology, and system chemistry. Although many advances have been made concerning the design principles of novel molecular architectures and chemistries, little is still known, however, about how to program their dynamic of assembly so that they can assemble at the required concentration and with the right sensitivity. Here, we used synthetic DNA assemblies and double‐mutant cycle analysis to explore the thermodynamic basis to program the cooperativity of molecular assemblies. The results presented here exemplify how programmable molecular assemblies can be efficiently built by fusing interacting domains and optimizing their compaction. They may also provide the rational basis for understanding the thermodynamic and mechanistic principles driving the evolution of multimeric biological complexes. [ABSTRACT FROM AUTHOR]

HopQ1, a type three effector from Pseudomonas syringae upon phosphorylation coopts plant 14-3-3 proteins to control its stability and subcellular localization. Mass spectrometry of the cytoplasm-restricted effector revealed that HopQ1 already in this subcellular compartment undergoes phosphorylation at serine 51 within the canonical 14-3-3 binding motif and within the second putative 14-3-3 binding site, 24RTPSES29. Our analyses revealed that the stoichiometry of the HopQ1:14-3-3a complex is 1:2 indicating that both binding sites of HopQ1 are involved in the interaction. Notably, 24RTPSES29 comprises a putative nuclear translocation signal (NTS). Although a peptide containing NTS mediates nuclear import of a Cargo protein suggesting its role in the nuclear trafficking of HopQ1, a deletion of 25TPS27 does not change HopQ1 distribution. In contrast, elimination of 14-3-3 binding site, accelerates nuclear trafficking the effector. Collectively, we show that formation of the HopQ1:14-3-3 complex occurs in the host cytoplasm and slows down the effector translocation into the nucleus. These results provide a mechanism that maintains the proper nucleocytoplasmic partitioning of HopQ1, and at the same time is responsible for the relocation of 14-3-3s from the nucleus to cytoplasm in the presence of the effector. [ABSTRACT FROM AUTHOR]

IntroductionEquipped with a photosynthetic apparatus that uses the energy of solar radiation to fuel biosynthesis of organic compounds, chloroplasts are the metabolic factories of mature leaf cells. The first steps of energy conversion are catalyzed by a collection of protein complexes, which can dynamically interact with each other for optimizing metabolic efficiency under changing environmental conditions.Materials and methodsFor a deeper insight into the organization of protein assemblies and their roles in chloroplast adaption to changing environmental conditions, an improved complexome profiling protocol employing a MS-cleavable cross-linker is used to stabilize labile protein assemblies during the organelle isolation procedure.Results and discussionChanges in protein:protein interaction patterns of chloroplast proteins in response to four different light intensities are reported. High molecular mass assemblies of central chloroplast electron transfer chain components as well as the PSII repair machinery react to different light intensities. In addition, the chloroplast encoded RNA-polymerase complex was found to migrate at a molecular mass of ~8 MDa, well above its previously reported molecular mass. Complexome profiling data produced during the course of this study can be interrogated by interested readers via a web-based online resource (https://complexomemap.de/projectsinteraction-chloroplasts).

Complex detection in protein-protein interaction (PPI) networks is one of the major issues facing scientific study in biological networks. In PPINs, proteins are distributed differently as groups (complexes). These groups can be identified as having a great internal density in the number of edges inside the groups while having the least possible number of edges between these groups. The most common methods for finding such complexes are evolutionary algorithms (EAs), which have been used widely in literature for this objective. Despite the reliability of these complicated detection models, they are mostly based on topological (graph) qualities, and the biological implications of the PPI networks have been rarely explored. In this research, EA with mutation-based gene ontology is developed, particularly in the mutation part where the functional annotation of the protein has been considered using gene ontology structure. The experimental results prove the reliability of the proposed method using standard validation measures. It also outperforms the state-of-the-art method in terms of the prediction ability and quality of the complexes found [ABSTRACT FROM AUTHOR]

The possible internal dynamics of non‐isotope‐labeled small‐molecule ligands inside a target protein is inherently difficult to capture. Whereas high crystallographic temperature factors can denote either static disorder or motion, even moieties with very low B‐factors can be subject to vivid motion between symmetry‐related sites. Here we report the experimental identification of internal μs timescale dynamics of a high‐affinity, natural‐abundance ligand tightly bound to the enzyme human carbonic anhydrase II (hCAII) even within a crystalline lattice. The rotamer jumps of the ligand's benzene group manifest themselves both, in solution and fast magic‐angle spinning solid‐state NMR 1H R1ρ relaxation dispersion, for which we obtain further mechanistic insights from molecular‐dynamics (MD) simulations. The experimental confirmation of rotameric jumps in bound ligands within proteins in solution or the crystalline state may improve understanding of host‐guest interactions in biology and supra‐molecular chemistry and may facilitate medicinal chemistry for future drug campaigns. [ABSTRACT FROM AUTHOR]

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein–protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein–protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome. [ABSTRACT FROM AUTHOR]

Genetic variation is the major mechanism behind adaptation and evolutionary change. As most proteins operate through interactions with other proteins, changes in protein complex composition and subunit sequence provide potentially new functions. Comparative genomics can reveal expansions, losses and sequence divergence within protein‐coding genes, but in silico analysis cannot detect subunit substitutions or replacements of entire protein complexes. Insights into these fundamental evolutionary processes require broad and extensive comparative analyses, from both in silico and experimental evidence. Here, we combine data from both approaches and consider the gamut of possible protein complex compositional changes that arise during evolution, citing examples of complete conservation to partial and total replacement by functional analogues. We focus in part on complexes in trypanosomes as they represent one of the better studied non‐animal/non‐fungal lineages, but extend insights across the eukaryotes by extensive comparative genomic analysis. We argue that gene loss plays an important role in diversification of protein complexes and hence enhancement of eukaryotic diversity. [ABSTRACT FROM AUTHOR]

Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their threedimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIFjury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody–antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies. [ABSTRACT FROM AUTHOR]

Predicting model quality is a fundamental component of any modeling procedure, and blind assessment of these methods constitutes a crucial aspect of the Critical Assessment of Protein Structure Prediction (CASP) experiment. Historically, the main focus was on assessing methods that predict global and per-residue accuracies in tertiary structure models. This focus shifted with the community's increased efforts in modeling complexes and assemblies. We asked the community to process the models from the CASP15 assembly category and provide estimates of the accuracy of the predicted quaternary structure, both globally and at the local interface level. Besides identifying remarkable accuracy of modeling groups in assessing their own predictions, we set up a benchmarking pipeline to highlight different aspects of quaternary structure models and introduced a simple consensus EMA method as baseline. While participating methods showed commendable performance, the baseline was difficult to surpass. It is important to point out that prediction performance varies for the individual CASP targets, highlighting potential areas of improvement and challenges ahead. [ABSTRACT FROM AUTHOR]

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem. [ABSTRACT FROM AUTHOR]

HopQ1, a type three effector from Pseudomonas syringae upon phosphorylation coopts plant 14-3-3 proteins to control its stability and subcellular localization. Mass spectrometry of the cytoplasm-restricted effector revealed that HopQ1 already in this subcellular compartment undergoes phosphorylation at serine 51 within the canonical 14-3-3 binding motif and within the second putative 14-3-3 binding site, 24RTPSES29. Our analyses revealed that the stoichiometry of the HopQ1:14-3-3a complex is 1:2 indicating that both binding sites of HopQ1 are involved in the interaction. Notably, 24RTPSES29 comprises a putative nuclear translocation signal (NTS). Although a peptide containing NTS mediates nuclear import of a Cargo protein suggesting its role in the nuclear trafficking of HopQ1, a deletion of 25TPS27 does not change HopQ1 distribution. In contrast, elimination of 14-3-3 binding site, accelerates nuclear trafficking the effector. Collectively, we show that formation of the HopQ1:14-3-3 complex occurs in the host cytoplasm and slows down the effector translocation into the nucleus. These results provide a mechanism that maintains the proper nucleocytoplasmic partitioning of HopQ1, and at the same time is responsible for the relocation of 14-3-3s from the nucleus to cytoplasm in the presence of the effector.

The quaternary structure with specific stoichiometry is pivotal to the specific function of protein complexes. However, determining the structure of many protein complexes experimentally remains a major bottleneck. Structural bioinformatics approaches, such as the deep learning algorithm Alphafold2‐multimer (AF2‐multimer), leverage the co‐evolution of amino acids and sequence‐structure relationships for accurate de novo structure and contact prediction. Pseudo‐likelihood maximization direct coupling analysis (plmDCA) has been used to detect co‐evolving residue pairs by statistical modeling. Here, we provide evidence that combining both methods can be used for de novo prediction of the quaternary structure and stoichiometry of a protein complex. We achieve this by augmenting the existing AF2‐multimer confidence metrics with an interpretable score to identify the complex with an optimal fraction of native contacts of co‐evolving residue pairs at intermolecular interfaces. We use this strategy to predict the quaternary structure and non‐trivial stoichiometries of Bacillus subtilis spore germination protein complexes with unknown structures. Co‐evolution at intermolecular interfaces may therefore synergize with AI‐based de novo quaternary structure prediction of structurally uncharacterized bacterial protein complexes. [ABSTRACT FROM AUTHOR]

Background: Previously, we reported that cysteine‐rich secretory protein 2 is involved in high molecular weight complexes in boar spermatozoa. These cysteine‐rich secretory protein 2protein complexes are formed at the last phase of sperm formation in the testis and play a role in sperm shaping and functioning. Objectives: This study aimed to identify cysteine‐rich secretory protein 2 interacting partners. These binding partner interactions were investigated under different conditions, namely, non‐capacitating conditions, after the induction of in vitro sperm capacitation and subsequently during an ionophore A23187‐induced acrosome reaction. Materials and Methods: The incubated pig sperm samples were subjected to protein extraction. Extracted proteins were subjected to blue native gel electrophoresis and native immunoblots. Immunoreactive gel bands were excised and subjected to liquid chromatography–mass spectrometry (LC‐MS) analysis for protein identification. Protein extracts were also subjected to CRISP2 immunoprecipitation and analyzed by LC‐MS for protein identification. The most prominent cystein‐rich secretory protein 2 interacting proteins that appeared in both independent LC‐MS analyses were studied with a functional in situ proximity interaction assay to validate their property to interact with cystein‐rich secretory protein 2 in pig sperm. Results: Blue native gel electrophoresis and native immunoblots revealed that cystein‐rich secretory protein 2 was present within a ∼150 kDa protein complex under all three conditions. Interrogation of cystein‐rich secretory‐protein 2‐immunoreactive bands from blue native gels as well as cystein‐rich secretory protein 2 immunoprecipitated products using mass spectrometry consistently revealed that, beyond cystein‐rich secretory protein 2, acrosin and acrosin binding protein were among the most abundant interacting proteins and did interact under all three conditions. Co‐immunoprecipitation and immunoblotting indicated that cystein‐rich secretory protein 2 interacted with pro‐acrosin (∼53 kDa) and Aacrosin binding protein under all three conditions and additionally to acrosin (∼35 kDa) after capacitation and the acrosome reaction. The colocalization of these interacting proteins with cystein‐rich secretory protein 2 was assessed via in situ proximity ligation assays. The colocalization signal of cystein‐rich secretory protein 2 and acrosin in the acrosome seemed dispersed after capacitation but was consistently present in the sperm tail under all conditions. The fluorescent foci of cystein‐rich secretory protein 2 and acrsin binding protein colocalization appeared to be redistributed within the sperm head from the anterior acrosome to the post‐acrosomal sheath region upon capacitation. Discussion and Conclusion: These results suggest that CRISP2 may act as a scaffold for protein complex formation and dissociation to ensure the correct positioning of proteins required for the acrosome reaction and zona pellucida penetration. [ABSTRACT FROM AUTHOR]

Background: Protein complexes provide valuable biological information, but can be difficult to handle. Therefore, technical advancements designed to improve their manipulation are always useful. Methods: We investigated the opportunity to exploit native agarose gels and the contact blot method for the transfer of native proteins to membranes as means for optimizing the conditions for obtaining stable complexes. As a simple model of protein–protein interactions, an antigen–ligand complex was used in which both proteins were fused to reporters. Results: At each step, it was possible to visualize both the antigen, fused to a fluorescent protein, and the ligand, fused to a monomeric ascorbate peroxidase (APEX) and, as such, a way to tune the protocol. The conditions for the complex formation were adapted by modifying the buffer conditions, the concentration of the proteins and of the cross-linkers. Conclusions: The procedure is rapid, inexpensive, and the several detection opportunities allow for both the monitoring of complex stability and the preservation of the functionality of its components, which is critical for understanding their biomedical implications and supporting drug discovery. The overall protocol represents a handy alternative to gel filtration, uses very standard and ubiquitous equipment, and can be implemented rapidly and without specific training. [ABSTRACT FROM AUTHOR]

The research deals with an evolutionary-based mutation with functional annotation to identify protein complexes within PPI networks. An important field of research in computational biology is the difficult and fundamental challenge of revealing complexes in protein interaction networks. The complex detection models that have been developed to tackle challenges are mostly dependent on topological properties and rarely use the biological properties of PPI networks. This research aims to push the evolutionary algorithm to its maximum by employing gene ontology (GO) to communicate across proteins based on biological information similarity for direct genes. The outcomes show that the suggested method can be utilized to improve the predictability of the complexes identified. The GO functional annotation of proteins as a heuristic guide is injected into the framework of single-objective evolutionary algorithms (EAs), while the complex detection community score (CS) model works as a fitness function in EAs. In the experiments, we analyzed the performance of our proposed algorithm when applied to the publicly accessible yeast protein networks. The results show a considerable improvement in the detection ability of complexes in the PPI network. [ABSTRACT FROM AUTHOR]

Progress in structural biology research has led to a high demand for powerful and yet complementary analytical tools for structural characterization of proteins and protein complexes. This demand has significantly increased interest in native mass spectrometry (nMS), particularly native top‐down mass spectrometry (nTDMS) in the past decade. This review highlights recent advances in nTDMS for structural research of biological assemblies, with a particular focus on the extra multi‐layers of information enabled by TDMS. We include a short introduction of sample preparation and ionization to nMS, tandem fragmentation techniques as well as mass analyzers and software/analysis pipelines used for nTDMS. We highlight unique structural information offered by nTDMS and examples of its broad range of applications in proteins, protein‐ligand interactions (metal, cofactor/drug, DNA/RNA, and protein), therapeutic antibodies and antigen‐antibody complexes, membrane proteins, macromolecular machineries (ribosome, nucleosome, proteosome, and viruses), to endogenous protein complexes. The challenges, potential, along with perspectives of nTDMS methods for the analysis of proteins and protein assemblies in recombinant and biological samples are discussed. [ABSTRACT FROM AUTHOR]

Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein–protein interaction (PPI) network, whereas some methods attempt to integrate biological information to enhance the features of the protein network for complex identification. Existing protein complex identification methods are unable to fully integrate network topology information and biological attribute information. Most of these methods are based on homogeneous networks and cannot distinguish the importance of different attributes and protein nodes. To address these issues, a GO attribute Heterogeneous Attention network Embedding (GHAE) method based on heterogeneous protein information networks is proposed. First, GHAE incorporates Gene Ontology (GO) information into the PPI network, constructing a heterogeneous protein information network. Then, GHAE uses a dual attention mechanism and heterogeneous graph convolutional representation learning method to learn protein features and to identify protein complexes. The experimental results show that building heterogeneous protein information networks can fully integrate valuable biological information. The heterogeneous graph embedding learning method can simultaneously mine the features of protein and GO attributes, thereby improving the performance of protein complex identification. [ABSTRACT FROM AUTHOR]

Cross-linking mass spectrometry (XL-MS) is a powerful tool for studying protein-protein interactions and elucidating architectures of protein complexes. While residue-specific XL-MS studies have been very successful, accessibility of interaction regions nontargetable by specific chemistries remain difficult. Photochemistry has shown great potential in capturing those regions because of nonspecific reactivity, but low yields and high complexities of photocross-linked products have hindered their identification, limiting current studies predominantly to single proteins. Here, we describe the development of three novel MS-cleavable heterobifunctional cross-linkers, namely SDASO (Succinimidyl diazirine sulfoxide), to enable fast and accurate identification of photocross-linked peptides by MSn. The MSn-based workflow allowed SDASO XL-MS analysis of the yeast 26S proteasome, demonstrating the feasibility of photocross-linking of large protein complexes for the first time. Comparative analyses have revealed that SDASO cross-linking is robust and captures interactions complementary to residue-specific reagents, providing the foundation for future applications of photocross-linking in complex XL-MS studies.

Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org).

The aim of this study was to investigate the interaction of the N-terminal domain of the valyl-tRNA synthetase with α, β, and γ subunits of the eEF1B translation elongation factor complex. Methods: for this purpose, all 4 proteins were synthesized in bacterial cells and purified to homogeneity by a combination of chromatographic methods. The interaction of the eEF1B complex subunits with the N-terminal domain of the valyl-tRNA synthetase was verified by gel filtration and in vitro pull-down assays. Protein fractions collected at these stages were analyzed by SDS-PAGE. Results: according to the gel filtration results, eEF1Bα and eEF1Bγ subunits do not form a stable complex with the valine-tRNA synthetase domain. The potential for complexation of the eEF1Bβ subunit was evaluated by pull-down assay, which showed that this protein does interact with the valyl-tRNA synthetase. Conclusions: we concluded that the eEF1Bα and eEF1Bγ subunits do not interact with the valyl-tRNA synthetase compared to the eEF1Bβ protein. The N-terminal domain of the valyl-tRNA synthetase is necessary and sufficient for this interaction.

Background and aims The MBW complex consist of proteins belonging to three major families (MYB, bHLH and WDR) involved in various processes throughout plant development: epidermal cell development, mucilage secretory cells and flavonoid biosynthesis. Recently, it has been reported that TT8 , encoding a bHLH transcription factor, is involved in the biosynthesis of flavonoids in the seed coat and it also plays a role in bypassing the postzygotic barrier resulting from an unbalance in genetic loads of the parental lines. Here, we focus on the functional evolution, in seed development, of the bHLH proteins that are part of the MBW complex, complemented with a literature review. Methods Phylogenetic analyses performed across seed plants and expression analyses in the reproductive tissues of four selected angiosperms (Arabidopsis thaliana , Brassica napus , Capsella rubella and Solanum lycopersicum) allow us to hypothesize on the evolution of its functions. Key Results TT8 expression in the innermost layer of the seed coat is conserved in the selected angiosperms. However, except for Arabidopsis , TT8 is also expressed in ovules, carpels and fruits. The homologues belonging to the sister clade of TT8 , EGL3 / GL3 , involved in trichome development, are expressed in the outermost layer of the seed coat, suggesting potential roles in mucilage. Conclusions The ancestral function of these genes appears to be flavonoid biosynthesis, and the conservation of TT8 expression patterns in the innermost layer of the seed coat in angiosperms suggests that their function in postzygotic barriers might also be conserved. Moreover, the literature review and the results of the present study suggest a sophisticated association, linking the mechanisms of action of these genes to the cross-communication activity between the different tissues of the seed. Thus, it provides avenues to study the mechanisms of action of TT8 in the postzygotic triploid block, which is crucial because it impacts seed development in unbalanced crosses. [ABSTRACT FROM AUTHOR]

Meiotic recombination is initiated by the formation of DNA double‐strand breaks (DSBs), essential for fertility and genetic diversity. In the mouse, DSBs are formed by the catalytic TOPOVIL complex consisting of SPO11 and TOPOVIBL. To preserve genome integrity, the activity of the TOPOVIL complex is finely controlled by several meiotic factors including REC114, MEI4, and IHO1, but the underlying mechanism is poorly understood. Here, we report that mouse REC114 forms homodimers, that it associates with MEI4 as a 2:1 heterotrimer that further dimerizes, and that IHO1 forms coiled‐coil‐based tetramers. Using AlphaFold2 modeling combined with biochemical characterization, we uncovered the molecular details of these assemblies. Finally, we show that IHO1 directly interacts with the PH domain of REC114 by recognizing the same surface as TOPOVIBL and another meiotic factor ANKRD31. These results provide strong evidence for the existence of a ternary IHO1‐REC114‐MEI4 complex and suggest that REC114 could act as a potential regulatory platform mediating mutually exclusive interactions with several partners. Synopsis: REC114, MEI4, and IHO1 are established regulators of DNA double‐strand breaks during meiotic recombination. This study reveals that these three factors form a stable complex and provides structural and biophysical insights into its architecture.REC114 dimerizes via its C terminus and interacts with the N terminus of MEI4 to form complexes with 2:1 and 4:2 stoichiometry.IHO1 forms homotetramers via a parallel four‐stranded coiled‐coil.IHO1 and REC114‐MEI4 form a stable complex requiring a highly conserved N terminus of IHO1 and the Pleckstrin homology domain of REC114.REC114 interaction with IHO1 is mutually exclusive with its binding to ANKRD31 and TOPOVIBL. [ABSTRACT FROM AUTHOR]

The detection and analysis of protein complexes is essential for understanding the functional mechanism and cellular integrity. Recently, several techniques for detecting and analysing protein complexes from Protein–Protein Interaction (PPI) dataset have been developed. Most of those techniques are inefficient in terms of detecting, overlapping complexes, exclusion of attachment protein in complex core, inability to detect inherent structures of underlying complexes, have high false-positive rates and an enrichment analysis. To address these limitations, we introduce a special structural-based weighted network approach for the analysis of protein complexes based on a Weighted Edge, Core-Attachment and Local Modularity structures (WECALM). Experimental results indicate that WECALM performs relatively better than existing algorithms in terms of accuracy, computational time, and p-value. A functional enrichment analysis also shows that WECALM is able to identify a large number of biologically significant protein complexes. Overall, WECALM outperforms other approaches by striking a better balance of accuracy and efficiency in the detection of protein complexes. [ABSTRACT FROM AUTHOR]

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

Tight-junctions (TJs) are multi-protein complexes between adjacent endothelial or epithelial cells. In the blood-brain-barrier (BBB), they seal the paracellular space and the Claudin-5 (Cldn5) protein forms their backbone. Despite the fundamental role in brain homeostasis, little is known on Cldn5-based TJ assemblies. Different structural models were suggested, with Cldn5 protomers generating paracellular pores that restrict the passage of ions and small molecules. Recently, the first Cldn5 pathogenic mutation, G60R, was identified and shown to induce Cl−-selective channels and Na+ barriers in BBB TJs, providing an excellent opportunity to validate the structural models. Here, we used molecular dynamics to study the permeation of ions and water through two distinct G60R-Cldn5 paracellular architectures. Only the so-called Pore I reproduces the functional modification observed in experiments, displaying a free energy (FE) minimum for Cl− and a barrier for Na+ consistent with anionic selectivity. We also studied the artificial Q57D and Q63D mutations in the constriction region, Q57 being conserved in Cldns except for cation permeable homologs. In both cases, we obtain FE profiles consistent with facilitated passage of cations. Our calculations provide the first in-silico description of a Cldn5 pathogenic mutation, further assessing the TJ Pore I model and yielding new insight on BBB’s paracellular selectivity.

Nearly all the life activities are regulated by the interactions of the protein with other biomolecules, including protein-protein interactions (PPIs) and protein-ligand interactions (PLIs), therefore understanding interaction and structural characterization of formed complexes represent a key task for modern bioscience. Conventional biophysical techniques like circular dichroism spectroscopy (CD), fluorescence resonance energy transfer (FRET), surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC), are limited in studying protein complexes structures and interactions, considering sample purity is a major issue for obtaining reliable data with conventional methods mentioned above. Additional technologies are required to complement structural biology and interactomics research, while mass spectrometry (MS) can deal with impurities or even mixtures in principle, therefore native mass spectrometry (nMS) is demonstrated as a powerful tool to bridge the gap between structural biology and interactomics research. As the most widespread soft ionization technique, electrospray ionization is utilized in native mass spectrometry, which employs volatile aqueous solution, near physiological pH and gentle desolvation conditions to make the transfer of weakly bound noncovalent complexes into the gas phase while preserving their integrity and native structure possible. In this way, stoichiometry, binding partners, thermodynamics, structural dynamics, and topology information on transferred noncovalent complexes can be acquired. A recent breakthrough in native MS sample preparation demonstrated direct analysis of overexpressed proteins from crude cell lysates, which significantly simplified sample purification procedures. Innovations in mass spectrometry instruments, such as extended m/z range quadrupole stages or modification of Orbitrap instruments allow access to high mass with little compromise in resolving power. This has promoted the native analysis of protein complexes and diverse protein interaction networks to elucidate their architecture, as well as enabled the identification of small differences in proteoforms. While coupling with either ion mobility spectrometry (IMS) or capillary electrophoresis (CE), the analytical ability of native MS can be greatly extended. In this review, the technologies in native mass spectrometry and several cases in recent reports from five aspects were introduced, such as sample preparation, ion source, instrumentation, ion mobility mass spectrometry and coupling with capillary electrophoresis, challenges for the future development of native mass spectrometry.

Abstract High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor.

Single particle cryo-electron microscopy (cryo-EM) has matured into a robust method for the determination of biological macromolecule structures in the past decade, complementing X-ray crystallography and nuclear magnetic resonance. Constant methodological improvements in both cryo-EM hardware and image processing software continue to contribute to an exponential growth in the number of structures solved annually. In this review, we provide a historical view of the many steps that were required to make cryo-EM a successful method for the determination of high-resolution protein complex structures. We further discuss aspects of cryo-EM methodology that are the greatest pitfalls challenging successful structure determination to date. Lastly, we highlight and propose potential future developments that would improve the method even further in the near future. [ABSTRACT FROM AUTHOR]

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes. [ABSTRACT FROM AUTHOR]

The synthesis of novel copolymers using one-step reversible addition-fragmentation chain transfer (RAFT) copolymerization of biocompatible methacrylic acid (MAA), lauryl methacrylate (LMA), and difunctional ethylene glycol dimethacrylate (EGDMA) as a branching agent is reported. The obtained amphiphilic hyperbranched H-P(MAA-co-LMA) copolymers are molecularly characterized by size exclusion chromatography (SEC), FTIR, and 1H-NMR spectroscopy, and subsequently investigated in terms of their self-assembly behavior in aqueous media. The formation of nanoaggregates of varying size, mass, and homogeneity, depending on the copolymer composition and solution conditions such as concentration or pH variation, is demonstrated by light scattering and spectroscopic techniques. Furthermore, drug encapsulation properties are studied by incorporating the low bioavailability drug, curcumin, in the nano-aggregate hydrophobic domains, which can also act as a bioimaging agent. The interaction of polyelectrolyte MAA units with model proteins is described to examine protein complexation capacity relevant to enzyme immobilization strategies, as well as explore copolymer self-assembly in simulated physiological media. The results confirm that these copolymer nanosystems could provide competent biocarriers for imaging and drug or protein delivery/enzyme immobilization applications. [ABSTRACT FROM AUTHOR]

The structure–function paradigm, i.e. the concept that it is the three-dimensional structure of a protein that determines its function, has been partially modified by the discovery that a significant portion of the eukaryotic proteome is disordered and that this disorder is often functional. The presence of disorder is the origin of several issues, but the most relevant, at least from the biomedical point of view, is the difficulty of designing drugs in absence of a well-defined conformation of the target. To make the problem worse, we have to consider that often the disorder concerns proteins involved in diseases very relevant for human health, as cancer or neurodegenerative disorders. This review tries to summarize the state of the art of our knowledge on the subject and to describe the tools used to detect disorder and how drug design techniques used for well-folded proteins have been adjusted to this more challenging situation. Abbreviations: AD: Alzheimer's disease; CAID: Critical assessment of intrinsic protein disorder; CASP: Critical assessment of protein structure prediction; CD: circular dichroism; Cryo-EM: cryo-electron microscopy; DIBS: differential binding score; FRET: Förster resonance energy transfer; HD: Huntington's disease; IDR: Intrinsically disordered regions; IDP: intrinsically disordered proteins; LDR: long intrinsically disordered regions; MG: Molten globule; MoRF: Molecular recognition feature; NMR: Nuclear magnetic resonance; PDB: Protein Data Bank; PD: Parkinson's disease; POMS: polyoxometalates; SAXS: Small-angle X-ray scattering; SLiMS: short linear motifs; TFs: Transcription factors. [ABSTRACT FROM AUTHOR]

It has been recently suggested that a significant fraction of homomer protein–protein interfaces evolve neutrally, without contributing to function, due to a hydrophobic bias in missense mutations. However, the fraction of such gratuitous complexes is currently unknown. Here, we quantified the fraction of homodimers where multimerization is unlikely to contribute to their biochemical function. We show that: 1) ligand binding-site structure predicts whether a homomer is functional or not; the vast majority of homodimers with multichain binding-sites (MBS) are likely to be functional, while in homodimers with single-chain binding-sites (SBS) and small to medium interfaces, quaternary structure is unlikely to be functional in a significant fraction—35%, even up to 42%—of complexes; 2) the hydrophobicity of interfaces changes little with the strength of selection, and the amino acid composition of interfaces is shaped by the "hydrophobic ratchet" in both types, but they are not in a strict equilibrium with mutations; particularly cysteines are much more abundant in mutations than in interfaces or surfaces; 3) in MBS homomers, the interfaces are conserved, while in a high fraction of SBS homomers, the interface is not more conserved than the solvent-accessible surface; and 4) MBS homomer interfaces coevolve more strongly with ligand binding sites than the interfaces of SBS homomers, and MBS complexes have higher capacity to transfer information from ligands across the interfaces than SBS homomers, explaining the enrichment of allostery in the former. [ABSTRACT FROM AUTHOR]

The MYC protooncogene functions as a universal amplifier of transcription through interaction with numerous factors and complexes that regulate almost every cellular process. However, a comprehensive model that explains MYC's actions and the interplay governing the complicated dynamics of components of the transcription and replication machinery is still lacking. Here, we review the potency of MYC as an oncogenic driver and how it regulates the broad spectrum of complexes (effectors and regulators). We propose a 'hand-over model' for differential partitioning and trafficking of unstructured MYC via a loose interaction network between various gene-regulatory complexes and factors. Additionally, the article discusses how unstructured-MYC energetically favors efficient modulation of the energy landscape of the transcription cycle. As an oncogenic driver, MYC amplifies global transcription by driving pause release and the early transcription, especially of highly expressed genes. It stimulates replication by facilitating assembly of replication complexes at origins and by associating with replication forks. It facilitates these processes by joining with a wide variety of gene regulatory complexes. Beyond helping to recruit the transcription machinery to promoters, MYC directly up- or down-modulates the catalytic activities of its interaction partners. For example, through assembly with topoisomerases 1 and 2 in the topoisome, MYC dramatically augments the cell's capacity to confront the topological and conformational challenges of DNA and chromatin under high-output mechanical stress. To orchestrate its activities, MYC levels must be strictly tuned by a series of interdependent enzymes that are responsible for its post-translational modifications, stabilization, destabilization, and trafficking. To coordinate the flux of MYC with its associated complexes, its unstructured regions associate with effectors and complexes at transcription start sites (TSSs) at different stages throughout the transcription cycle. To increase overall transcription output, MYC alters the residence times of components of the transcription machinery and as different complexes are sequentially ferried in and out of promoter regions. The conformational plasticity of MYC's effector regions allow it to adapt to a variety of partners without a prior energetic expense of unfolding or remodeling. [ABSTRACT FROM AUTHOR]

Stabilized by solid particles, Pickering foams have a much longer stability than traditional foams and have continued to gain interest in different applications in the last two decades. Protein-based particles with the typical amphiphilic structure are particularly suitable for developing Pickering foams due to their natural origin, low cost, excellent nutritional values, and potential health benefits. This review summarizes Pickering foams stabilized by protein particles and their complex particles conjugate with several biopolymers. Then, the main mechanisms controlling the foam stability are discussed to understand the complex behavior of Pickering foams better. Finally, the main applications and challenges of protein-stabilized Pickering foams are highlighted. To date, animal and plant proteins and their complexes with other biopolymers have been shown to stabilize Pickering foams effectively. The stabilization mechanism of Pickering foam includes irreversible adsorption and formation of a tightly-packed interfacial layer by adsorbed protein-based particles for preventing bubble shrinkage and gas diffusion, and accumulation at the Plateau borders or formation of gel-like network in the continuous phase by non-adsorbed protein-based particles for reducing drainage, as well as depletion stabilization. Each type of protein-based particle may exert excellent advantages in different applications. This review will provide theoretical guidance for efficiently developing innovative protein-stabilized Pickering foams for future applications in functional aerated foods. [Display omitted] • Reviews Pickering foams stabilized by different protein particles. • Highlights different type of protein complex used for stabilizing Pickering foams. • Elucidates underlying stabilization mechanisms for Pickering foams. • Discusses main applications and challenges of protein-stabilized Pickering foams. [ABSTRACT FROM AUTHOR]

Structural information of protein complexes is fundamental for the rational drug design and improvement of vaccines and biosensors. Also, protein misassembly can have severe biological consequences. Here we discuss the challenges of studying protein complexes and show examples of systems characterized using NMR.

Protein degradation is known to be a key component of expression regulation for individual genes, but its global impact on gene expression has been difficult to determine. We analyzed a parallel gene expression dataset of yeast meiotic differentiation, identifying instances of coordinated protein-level decreases to identify new cases of regulated meiotic protein degradation, including of ribosomes and targets of the meiosis-specific anaphase-promoting complex adaptor Ama1. Comparison of protein and translation measurements over time also revealed that, although meiotic cells are capable of synthesizing protein complex members at precisely matched levels, they typically do not. Instead, the members of most protein complexes are synthesized imprecisely, but their protein levels are matched, indicating that wild-type eukaryotic cells routinely use post-translational adjustment of protein complex partner levels to achieve proper stoichiometry. Outlier cases, in which specific complex components show divergent protein-level trends, suggest timed regulation of these complexes.

G protein-coupled receptors (GPCRs) are an important family of membrane proteins responsible for many physiological functions in human body. High resolution GPCR structures are required to understand their molecular mechanisms and perform rational drug design, as GPCRs play a crucial role in a variety of diseases. That is difficult to obtain for the wild-type proteins because of their low stability. In this review, we discuss how this problem can be solved by using protein design strategies developed to obtain homogeneous stabilized GPCR samples for crystallization and cryoelectron microscopy. [ABSTRACT FROM AUTHOR]

This manuscript describes the application of a fixed-length ultrasonic spectrometer to determine the kinetics of heat- and photo-induced damage to biological membranes and protein complexes and provides examples of the test measurements. We implemented a measurement scheme using the digital analysis of harmonic signals. To conduct the research, the fixed-length ultrasonic spectrometer was modernized: the speed was increased; lighting was supplied to the sample cells; the possibility of changing the gas atmosphere and mixing the sample was given. Using solutions containing natural concentrations of deuterium oxide, a high sensitivity of the spectrometer was shown. The spectrometer performed well in the measurement of phase state of dimyristoylphosphatidylcholine liposomes, both in the absence and in the presence of additions, which are capable of changing the lipid properties (sodium dodecyl sulfate, palmitic acid, and calcium ions). The heat- and photo-induced changes in the state of photosystem II core complexes were demonstrated using a fixed-length ultrasonic spectrometer. Transitions at 35.5 °C, 43.5 °C, 56.5 °C, and 66.7 °C were revealed. It is proposed that the transitions reflect the disassembly of the complexes and protein denaturation. Thus, the present study demonstrates that a fixed-length ultrasonic spectrometer can be applied to determine the kinetics of heat- and photo-induced damage to biological membranes and protein complexes. [ABSTRACT FROM AUTHOR]

Background: Protein complexes provide valuable biological information, but can be difficult to handle. Therefore, technical advancements designed to improve their manipulation are always useful. Methods: We investigated the opportunity to exploit native agarose gels and the contact blot method for the transfer of native proteins to membranes as means for optimizing the conditions for obtaining stable complexes. As a simple model of protein–protein interactions, an antigen–ligand complex was used in which both proteins were fused to reporters. Results: At each step, it was possible to visualize both the antigen, fused to a fluorescent protein, and the ligand, fused to a monomeric ascorbate peroxidase (APEX) and, as such, a way to tune the protocol. The conditions for the complex formation were adapted by modifying the buffer conditions, the concentration of the proteins and of the cross-linkers. Conclusions: The procedure is rapid, inexpensive, and the several detection opportunities allow for both the monitoring of complex stability and the preservation of the functionality of its components, which is critical for understanding their biomedical implications and supporting drug discovery. The overall protocol represents a handy alternative to gel filtration, uses very standard and ubiquitous equipment, and can be implemented rapidly and without specific training.

Proteins, as the material basis of life, are the ultimate controller and direct performer of life activities. Most proteins perform their biological functions by binding to other proteins to form complexes. The identification of protein complexes can help people to better understand the organization and functions of the complexes, as well as the mechanisms of cells. At present, the rapid development of high-throughput experimental technology has led to huge amounts of protein-protein interaction (PPI) data. Many methods for computationally predicting protein complexes based on PPI data have been proposed. Different methods have their own characteristics and advantages, and they also have inherent drawbacks. Firstly, this paper classifies and comprehensively analyzes and reviews the existing protein complex prediction methods. Then, it introduces the commonly used evaluation indicators and main data sets in complex prediction, compares and analyzes the prediction performance of several representative methods. Finally, it summarizes state-of-the-art complex prediction methods, highlights future research directions, and puts forward several issues to be resolved in the future. It is hoped that through the analysis and comparison of various methods, this paper can provide some valuable guidance and future directions for users and researchers on using the existing methods and developing new methods of protein complex prediction.