A novel phycocyanin-Chla/c_2-protein complex isolated from chloroplasts of Chroomonas placoidea

Nine pigment-protein complexes were separated and characterized from intact Chroomonasplacoidea chloroplasts by IEF. The bands Ⅰ-Ⅵ with their isoelectric points （pI） values from 4 to 6 were phycocyanin components; bands Ⅷ and Ⅸ （pI = 2.8-3.6） were chlorophyll-protein complexes. According to absorption and fluorescence spectra, band VII was designated as a novel phycocyanin-Chla/c2-protein complex （pI ≈ 3.4-3.7）. These results indicated that phycocyanin is structurally and functionally coupled with chlorophyll-protein complex in C. placoidea, and probably interacted with electrostatic force in combination.

Influence of bioactive sulphated polysaccharide-protein complexes on hepatocarcinogenesis, angiogenesis and immunomodulatory activities

Objective:To explore the in vivo anticancer,anti-angiogenesis and immunomodulatory efficacies of the bioactive polysaccharide isolated from cold aqueous extract of Jania rubens(JCEM) and Pterocladia capillacea(PCEM) as well as hot aqueous extract of Enteromorpha intestinalis(EHEM) against hepatocellular carcinoma rat model(HCC) and to study their chemical composition.Methods:The sugars and amino acids composition of the bioactive polysaccharides of JCEM,PCEM and EHEM were determined using gas liquid chromatography and amino acid analyzer,respectively.These polysaccharide extracts(20 mg/kg b.wt.for 5 weeks) were assessed on hepatocarcinogenesis in rats and α-fetoprotein(AFP),carcinoembryonic antigen(CEA),glypican-3(GPC-3),hepatocyte growth factor(HGF) and vascular endothelial growth factor(VEGF) and Ig G levels were evaluated.Results:The GLC analysis of JCEM,PCEM and EHEM polysaccharide revealed the presence of 10,9 and10 sugars,in addition the amino acid analyser enable identification of 16,15 and 15 amino acids,respectively.These polysaccharide extracts of JCEM,PCEM and EHEM produced significant decrease in serum AFP,CEA,GPC-3,HGF and VEGF compared with untreated HCC group.JCEM,PCEM and EHEM had an immunostimulatory responses by increasing the IgG levels as compared by naive value(1.23,1.53 and 1.17 folds),respectively.The bioactive polysaccharides in HCC induced rats improved the humoral immune response.The photomicrographs of liver tissue sections of the groups of HCC treated with polysaccharide extracts of Jania rubens and Enteromorpha intestinalis showed intact histological structure.Moreover,fractions HE1,HE4,HE7 obtained from polysaccharide of EHEM showed moderate cytotoxic activity against Hep G2 in vitro with IC_(50) 73.1,42.6,76.2 μg/mL.However,fractions of PCEM and JCEM show no or weak cytotoxicity against Hep G2 in vitro where the cytotoxic activity of their crude polysaccharide extract proved synergetic effect.Conclusions:The pronounced antitumor activity of sulphated polysaccharide-protein complexes of JCEM and EHEM is due to direct cytotoxic activity,anti-hepatocarcinogensis,and anti-angiogenesis.In addition,JCEM,PCEM and EHEM had an immunostimulatory response and improved the humoral immune response in HCC induced rats.

CHARACTERIZATION OF MOLECULAR MASS OF SIX WATER-SOLUBLE POLYSACCHARIDE -PROTEIN COMPLEXES FROM GANODERMA TSUGAE MYCELIUM

Six water-soluble polysaccharide-protein complexes coded as GM1, GM2, GM3, GM4, GM5 and GM6 wereisolated from the mycelium of Ganoderma tsugae by extracting with 0.2 mol/L phosphate buffer solution at 25, 40 and80℃, water at 120℃, 0.5 mol/L aqueous NaOH solution at 25 and 65℃, consecutively. Their chemical components wereanalyzed by using IR, GC, HPLC and ^(13)C-NMR, and some new results were obtained. The four samples GM1, GM2, GM3and GM4 are heteropolysaccharide-prote in complexes, in which, α- (1→3) linked D-glucose is the major monosaccharidewhile galactose, mannose and ribose are the secondary ones. GM5 and GM6 are β-(1→3)-D-glucan-protein complexes. Theprotein content increased from 32% to 69% with the progress of isolation. Weight-average molecu1ar mass M_w and theintrinsic viscosity [η] of the GM samples in 0.5 mol/L aqueous NaCl solution at 25℃ were measured systematically by laserlight scartering (LLS), size exclusion chromatography (SEC) combined with LLS, and viscometry. The M_w of GM1 to GM6are 35.5, 46.8, 58.9, 41.6, 3.3 and 22.0×10~4, respectively. The conformation and molecular mass of the two fractions of sample GM5 were characterized satisfactorily by SEC-LLS without further fractionation.

Discrete Differential Geometry and the Structural Study of Protein Complexes

This paper proposes a novel four-dimensional approach to the structural study of protein complexes. In the approach, the surface of a protein molecule is to be described using the intersection of a pair of four-dimensional triangular cones (with multiple top vertexes). As a mathematical toy model of protein complexes, we consider complexes of closed trajectories of n-simplices (n=2,3,4...), where the design problem of protein complexes corresponds to an extended version of the Hamiltonian cycle problem. The problem is to find “a set of” closed trajectories of n-simplices which fills the n-dimensional region defined by a given pair of n+1 -dimensional triangular cones. Here we give a solution to the extended Hamiltonian cycle problem in the case of n=2 using the discrete differential geometry of triangles (i.e., 2-simplices).

Factors That Affect the Computational Prediction of Hot Spots in Protein-Protein Complexes

Protein-protein complexes play an important role in the physiology and the pathology of cellular functions, and therefore are attractive therapeutic targets. A small subset of residues known as “hot spots”, accounts for most of the protein-protein binding free energy. Computational methods play a critical role in identifying the hotspots on the proteinprotein interface. In this paper, we use a computational alanine scanning method with all-atom force fields for predicting hotspots for 313 mutations in 16 protein complexes of known structures. We studied the effect of force fields, solvation models, and conformational sampling on the hotspot predictions. We compared the calculated change in the protein-protein interaction energies upon mutation of the residues in and near the protein-protein interface, to the experimental change in free energies. The AMBER force field (FF) predicted 86% of the hotspots among the three commonly used FF for proteins, namely, AMBER FF, Charmm27 FF, and OPLS-2005 FF. However, AMBER FF also showed a high rate of false positives, while the Charmm27 FF yielded 74% correct predictions of the hotspot residues with low false positives. Van der Waals and hydrogen bonding energy show the largest energy contribution with a high rate of prediction accuracy, while the desolvation energy was found to contribute little to improve the hot spot prediction. Using a conformational ensemble including limited backbone movement instead of one static structure leads to better predicttion of hotpsots.

The endoplasmic reticulum membrane protein complex subunit Emc6 is essential for rhodopsin localization and photoreceptor cell survival

The endoplasmic reticulum(ER)membrane protein complex(EMC)is responsible for monitoring the biogenesis and synthetic quality of membrane proteins with tail-anchored or multiple transmembrane domains.The EMC subunit EMC6 is one of the core members of EMC and forms an enclosed hydrophilic vestibule in cooperation with EMC3.Despite studies demonstrating that deletion of EMC3 led to rhodopsin mislocalization in rod photoreceptors of mice,the precise mechanism leading to the failure of rhodopsin trafficking remains unclear.Here,we generated the first rod photoreceptor-specific knockout of Emc6(RKO)and cone photoreceptor-specific knockout of Emc6(CKO)mouse models.Deficiency of Emc6 in rod photoreceptors led to progressive shortening of outer segments(OS),impaired visual function,mislocalization and reduced expression of rhodopsin,and increased gliosis in rod photoreceptors.In addition,CKO mice displayed the progressive death of cone photoreceptors and abnormal localization of cone opsin protein.Subsequently,proteomics analysis of the RKO mouse retina illustrated that several cilium-related proteins,particularly anoctamin-2(ANO2)and transmembrane protein 67(TMEM67),were significantly down-regulated prior to OS degeneration.Detrimental rod photoreceptor cilia and mislocalized membrane disc proteins were evident in RKO mice.Our data revealed that in addition to monitoring the synthesis of rhodopsin-dominated membrane disc proteins,EMC6 also impacted rod photoreceptors'ciliogenesis by regulating the synthesis of membrane proteins associated with cilia,contributing to the mislocalization of membrane disc proteins.

SSINCC: Simple separation of interacting nucleoprotein complex components

Protein-DNA binding assays have been used in a va-riety of applications from fundamental studies re-garding the binding process itself to serve as probes for the detection, quantification and separation of target analytes. Here we describe a novel method of analyzing and identifying intermolecular DNA interactions that allows for the simple separation of interacting nucleoprotein complex components (SSINCC), focusing specifically on DNA-DNA interactions using P1 plasmid active partition system nucleoprotein complexes as a model to demonstrate DNA sequence specificity and tolerance of composite factor complexity. Traditional and recent assays of protein-DNA interaction are summarized and compared with SSINC. Although SSINC is examined here employing P1 partition nucleoprotein complex as an example of DNA-DNA intermolecular association, universal applications of this methodology to nucleo-protein complex studies can be envisioned.

HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells.For a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein complexes.Unfortunately,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological mechanisms.To fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to date.We integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized complexes.Compared with the existing methods,our method achieved outstanding performance on both testing and independent datasets.Furthermore,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting proteins.These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological findings.The HPCAtlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

CPL:Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.

Changes in Thermostability of Photosystem Ⅱ and Leaf Lipid Composition of Rice Mutant with Deficiency of Light-harvesting Chlorophyll a/b Protein Complexes

We studied the difference in thermostability of photosystem Ⅱ （PSII） and leaf lipid composition between a T-DNA insertion mutant rice （Oryza sativa L.） VG28 and its wild type Zhonghuau. Native green gel and SDS-PAGE electrophoreses revealed that the mutant VG28 lacked all light-harvesting chlorophyll a/b protein complexes. Both the mutant and wild type were sensitive to high temperatures, and the maximal efficiency of PSII photochemistry （FJ Fm） and oxygen-evolving activity of PSII in leaves significantly decreased with increasing temperature. However, the PSII activity of the mutant was markedly more sensitive to high temperatures than that of the wild type. Lipid composition analysis showed that the mutant had less phosphatidylglycerol and sulfoquinovosyl diacylglycerol compared with the wild type. Fatty acid analysis revealed that the mutant had an obvious decrease in the content of 16：1t and a marked increase in the content of 18：3 compared with the wild type. The effects of lipid composition and unsaturation of membrane lipids on the thermostability of PSII are discussed.

The ER membrane protein complex subunit Emc3 controls angiogenesis via the FZD4/WNT signaling axis

The endoplasmic reticulum(ER) membrane protein complex(EMC) regulates the synthesis and quality control of membrane proteins with multiple transmembrane domains. One of the membrane spanning subunits, EMC3, is a core member of the EMC complex that provides essential hydrophilic vestibule for substrate insertion. Here, we show that the EMC subunit Emc3 plays critical roles in the retinal vascular angiogenesis by regulating Norrin/Wnt signaling. Postnatal endothelial cell(EC)-specific deletion of Emc3 led to retarded retinal vascular development with a hyperpruned vascular network, the appearance of bluntended, aneurysm-like tip endothelial cells(ECs) with reduced numbers of filopodia and leakage of erythrocytes at the vascular front. Diminished tube formation and cell proliferation were also observed in EMC3 depleted human retinal endothelial cells(HRECs). We then discovered a critical role for EMC3 in expression of FZD4 receptor of β-catenin signaling using RNA sequencing, real-time quantitative PCR(RT-q PCR) and luciferase reporter assay. Moreover, augmentation of Wnt activity via lithium chloride(Li Cl) treatment remarkably enhanced β-catenin signaling and cell proliferation of HRECs. Additionally, Li Cl partially reversed the angiogenesis defects in Emc3-c KO mice. Our data reveal that Emc3 plays essential roles in angiogenesis through direct control of FZD4 expression and Norrin/β-catenin signaling.

Mining Protein Complexes from PPI Networks Using the Minimum Vertex Cut

Evidence shows that biological systems are composed of separable functional modules. Identifying protein complexes is essential for understanding the principles of cellular functions. Many methods have been proposed to mine protein complexes from protein-protein interaction networks. However, the performances of these algorithms are not good enough since the protein-protein interactions detected from experiments are not complete and have noise. This paper presents an analysis of the topological properties of protein complexes to show that although proteins from the same complex are more highly connected than proteins from different complexes, many protein complexes are not very dense （density ≥0.8）. A method is then given to mine protein complexes that are relatively dense （density ≥0.4）. In the first step, a topology property is used to identify proteins that are probably in a same complex. Then, a possible boundary is calculated based on a minimum vertex cut for the protein complex. The final complex is formed by the proteins within the boundary. The method is validated on a yeast protein-protein interaction network. The results show that this method has better performance in terms of sensitivity and specificity compared with other methods. The functional consistency is also good.

Review of multimer protein–protein interaction complex topology and structure prediction

Protein–protein interactions (PPI) are important for many biological processes. Theoretical understanding of the structurally determining factors of interaction sites will help to understand the underlying mechanism of protein–protein interactions. At the same time, understanding the complex structure of proteins helps to explore their function. And accurately predicting protein complexes from PPI networks helps us understand the relationship between proteins. In the past few decades, scholars have proposed many methods for predicting protein interactions and protein complex structures. In this review, we first briefly introduce the methods and servers for predicting protein interaction sites and interface residue pairs, and then introduce the protein complex structure prediction methods including template-based prediction and template-free prediction. Subsequently, this paper introduces the methods of predicting protein complexes from the PPI network and the method of predicting missing links in the PPI network. Finally, it briefly summarizes the application of machine/deep learning models in protein structure prediction and action site prediction.

Framework to Identify Protein Complexes Based on Similarity Preclustering

Proteins interact with each other to form protein complexes, and cell functionality depends on both protein interactions and these complexes. Based on the assumption that protein complexes are highly connected and correspond to the dense regions in Protein-protein Interaction Networks（PINs）, many methods have been proposed to identify the dense regions in PINs. Because protein complexes may be formed by proteins with similar properties,such as topological and functional properties, in this paper, we propose a protein complex identification framework（KCluster）. In KCluster, a PIN is divided into K subnetworks using a K-means algorithm, and each subnetwork comprises proteins of similar degrees. We adopt a strategy based on the expected number of common neighbors to detect the protein complexes in each subnetwork. Moreover, we identify the protein complexes spanning two subnetworks by combining closely linked protein complexes from different subnetworks. Finally, we refine the predicted protein complexes using protein subcellular localization information. We apply KCluster and nine existing methods to identify protein complexes from a highly reliable yeast PIN. The results show that KCluster achieves higher Sn and Sp values and f-measures than other nine methods. Furthermore, the number of perfect matches predicted by KCluster is significantly higher than that of other nine methods.

Discovering Protein Complexes from Protein-Protein Interaction Data by Dense Subgraph

High-throughput techniques,such as the yeast-two-hybrid system,produce mass protein-protein interaction data. The new technique makes it possible to predict protein complexes by com-putation. A novel method,named DSDA,has been put forward to predict protein complexes via dense subgraph because the proteins among a protein complex have a much tighter relation among them than with others. This method chooses a node with its neighbors to form the initial subgraph,and chooses a node which has the tightest relation with the subgraph according to greedy strategy,then the chosen node is added into the initial subgraph until the subgraph density is below the threshold value. The ob-tained subgraph is then removed from the network and the process continues until no subgraph can be detected. Compared with other algorithms,DSDA can predict not only non-overlap protein com-plexes but also overlap protein complexes. The experiment results show that DSDA predict as many protein complexes as possible. And in Y78K network the accuracy of DSDA is as twice times as that of RNSC and MCL.

A framework combines supervised learning and dense subgraphs discovery to predict protein complexes

Rapidly identifying protein complexes is significant to elucidate the mechanisms of macromolecular interactions and to further investigate the overlapping clinical manifestations of diseases.To date,existing computational methods majorly focus on developing unsupervised graph clustering algorithms,sometimes in combination with prior biological insights,to detect protein complexes from protein-protein interaction(PPI)networks.However,the outputs of these methods are potentially structural or functional modules within PPI networks.These modules do not necessarily correspond to the actual protein complexes that are formed via spatiotemporal aggregation of subunits.In this study,we propose a computational framework that combines supervised learning and dense subgraphs discovery to predict protein complexes.The proposed framework consists of two steps.The first step reconstructs genome-scale protein co-complex networks via training a supervised learning model of l2-regularized logistic regression on experimentally derived co-complexed protein pairs;and the second step infers hierarchical and balanced clusters as complexes from the co-complex networks via effective but computationally intensive k-clique graph clustering method or efficient maximum modularity clustering(MMC)algorithm.Empirical studies of cross validation and independent test show that both steps achieve encouraging performance.The proposed framework is fundamentally novel and excels over existing methods in that the complexes inferred from protein co-complex networks are more biologically relevant than those inferred from PPI networks,providing a new avenue for identifying novel protein complexes.

Combination of NMR spectroscopy and X-ray crystallography offers unique advantages for elucidation of the structural basis of protein complex assembly

NMR spectroscopy and X-ray crystallography are two premium methods for determining the atomic structures of macro-biomolecular complexes.Each method has unique strengths and weaknesses.While the two techniques are highly complementary,they have generally been used separately to address the structure and functions of biomolecular complexes.In this review,we emphasize that the combination of NMR spectroscopy and X-ray crystallography offers unique power for elucidating the structures of complicated protein assemblies.We demonstrate,using several recent examples from our own laboratory,that the exquisite sensitivity of NMR spectroscopy in detecting the conformational properties of individual atoms in proteins and their complexes,without any prior knowledge of conformation,is highly valuable for obtaining the high quality crystals necessary for structure determination by X-ray crystallography.Thus NMR spectroscopy,in addition to answering many unique structural biology questions that can be addressed specifically by that technique,can be exceedingly powerful in modern structural biology when combined with other techniques including X-ray crystallography and cryo-electron microscopy.

A light-harvesting siphonax-anthin-chlorophyll a/b-protein complex of marine green alga, Bryopsis corticulans

A light-harvesting chlorophyll a/b-protein complex (LHCP) was isolated directly from thylakoid mem-branes of marine green alga, Bryopsis corticulans, by two consecutive runs of liquid chromatography. The trimeric form of the light-harvesting complex has been obtained by sucrose gradient ultracentrifugation. The result of SDS- PAGE shows that the light-harvesting complex is composed of at least five apoproteins in which a protein with apparent molecular weight of about 31 kD was never found in the ma-jor light-harvesting complex (LHC Ⅱ) from higher plants. The isolated Bryopsis corticulans light-harvesting complex contains a specific carotenoid, siphonaxanthin, as well as chlorophyll (Chl) a, Chl b, neoxanthin and violaxanthin. Si-phonaxanthin which is present in the light-harvesting sipho-naxanthin-chlorophyll a/b-protein complex of Bryopsis corticulans is responsible for enhanced absorption in the blue-green region (530 nm). Efficient energy transfer from both siphonaxanthin and Chl b to Chl a in Bryopsis corticu-lans LHCP, which has similar absorption and fluorescence emission spectra to those of the lutein-chlorophyll a/b-protein of higher plants, proved that molecular arrangement of the light-harvesting pigments was highly ordered in the Bryopsis corticulans LHCP. The siphonaxanthin-chlorophyll a/b-pro- teins allow enhanced absorption of blue-green light, the pre-dominant light available in deep ocean waters or shaded subtidal marine habitats.

A holistic molecular docking approach for predicting protein-protein complex structure

A holistic protein-protein molecular docking approach,HoDock,was established,composed of such steps as binding site prediction,initial complex structure sampling,refined complex structure sampling,structure clustering,scoring and final structure selection.This article explains the detailed steps and applications for CAPRI Target 39.The CAPRI result showed that three predicted binding site residues,A191HIS,B512ARG and B531ARG,were correct,and there were five submitted structures with a high fraction of correct receptor-ligand interface residues,indicating that this docking approach may improve prediction accuracy for protein-protein complex structures.

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeast Saccharomyces cerevisiae, large-scale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indicates that comprehensive integration and analysis of public large-scale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.