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Статті в журналах з теми "Protein – protein interactions (PPI)":
1
Kusova, Aleksandra M., Aleksandr E. Sitnitsky, Vladimir N. Uversky, and Yuriy F. Zuev. "Effect of Protein–Protein Interactions on Translational Diffusion of Spheroidal Proteins." International Journal of Molecular Sciences 23, no. 16 (August 17, 2022): 9240. http://dx.doi.org/10.3390/ijms23169240.
Анотація:
One of the commonly accepted approaches to estimate protein–protein interactions (PPI) in aqueous solutions is the analysis of their translational diffusion. The present review article observes a phenomenological approach to analyze PPI effects via concentration dependencies of self- and collective translational diffusion coefficient for several spheroidal proteins derived from the pulsed field gradient NMR (PFG NMR) and dynamic light scattering (DLS), respectively. These proteins are rigid globular α-chymotrypsin (ChTr) and human serum albumin (HSA), and partly disordered α-casein (α-CN) and β-lactoglobulin (β-Lg). The PPI analysis enabled us to reveal the dominance of intermolecular repulsion at low ionic strength of solution (0.003–0.01 M) for all studied proteins. The increase in the ionic strength to 0.1–1.0 M leads to the screening of protein charges, resulting in the decrease of the protein electrostatic potential. The increase of the van der Waals potential for ChTr and α-CN characterizes their propensity towards unstable weak attractive interactions. The decrease of van der Waals interactions for β-Lg is probably associated with the formation of stable oligomers by this protein. The PPI, estimated with the help of interaction potential and idealized spherical molecular geometry, are in good agreement with experimental data.
2
CHUA, HON NIAN, KANG NING, WING-KIN SUNG, HON WAI LEONG, and LIMSOON WONG. "USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION." Journal of Bioinformatics and Computational Biology 06, no. 03 (June 2008): 435–66. http://dx.doi.org/10.1142/s0219720008003497.
Анотація:
Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein–protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein–protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.
3
Abdullah, Syahid, Wisnu Ananta Kusuma, and Sony Hartono Wijaya. "Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning." Jurnal Teknologi dan Sistem Komputer 10, no. 1 (January 4, 2022): 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.
Анотація:
Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.
4
Dong, Yun Yuan, and Xian Chun Zhang. "Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network." Advanced Materials Research 934 (May 2014): 159–64. http://dx.doi.org/10.4028/www.scientific.net/amr.934.159.
Анотація:
Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.
5
Poot Velez, Albros Hermes, Fernando Fontove, and Gabriel Del Rio. "Protein–Protein Interactions Efficiently Modeled by Residue Cluster Classes." International Journal of Molecular Sciences 21, no. 13 (July 6, 2020): 4787. http://dx.doi.org/10.3390/ijms21134787.
Анотація:
Predicting protein–protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm–parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96–99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.
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Orasch, Oliver, Noah Weber, Michael Müller, Amir Amanzadi, Chiara Gasbarri, and Christopher Trummer. "Protein–Protein Interaction Prediction for Targeted Protein Degradation." International Journal of Molecular Sciences 23, no. 13 (June 24, 2022): 7033. http://dx.doi.org/10.3390/ijms23137033.
Анотація:
Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.
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Velasco-García, Roberto, and Rocío Vargas-Martínez. "The study of protein–protein interactions in bacteria." Canadian Journal of Microbiology 58, no. 11 (November 2012): 1241–57. http://dx.doi.org/10.1139/w2012-104.
Анотація:
Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.
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Ban Bolly, Hendrikus Masang, Yulius Hermanto, Ahmad Faried, Muhammad Zafrullah Arifin, Trajanus Laurens Yembise, and Firman Fuad Wirakusumah. "Protein-protein Interaction Analysis of Contributing Molecules in Dura mater Healing Process." International Journal of ChemTech Research 13, no. 3 (2020): 73–82. http://dx.doi.org/10.20902/jctr.2019.130302.
Анотація:
Background: Dura mater is a special tissue that fulfills a critical function in brain anatomy and physiology. This tissue contains numerous cells, stem cells, and growth factors. This research investigates the protein interaction contributing to dura mater healing process. Methods: We use the available analysis software to perform the protein-protein interaction (PPI) analysis (http://gpsprot.org/index.php). GPS Protein is an interactive platform for visualizing human protein interaction by integrating HIPPIE and CORUM databases. We excluded HIV-1 proteomic and RNAi databases, instead focused on human PPI (Confidence level 0.75). Two proteins were inputted as query to identify the potential protein network in Dura mater healing according to previous studies, i.e. fibroblast growth factor-2 (FGF2) and transforming growth factor beta-1 (TGFβ1). Results: PPI results shows a high level (confidence level > 0.75) of protein-protein interaction of TGFβ1 to 197 other proteins (Confidence level ranges: 0.49 - 0.87), and PPI of FGF2 to 26 other proteins (Confidence level ranges: 0.0-0.97). TGFβ1 interactions showed the important interactions to some remodeling proteins. TGFβ1 encoded regulates cell proliferation, differentiation, growth, expression modulation and the activation of other growth factors. It also induces epithelial-to-mesenchymal transition (EMT) and cell migration. Conclusion: This bioinformatics approach is the more efficient and cheaper method for analyzing the molecular aspect of protein that has a special contribution in Dura mater healing process. These results could beneficial in focusing further researches for more complex laboratory examinations.
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Kaur, Rajpreet, Poonam Khullar, and Anita Gupta. "Protein-Protein Interactions Followed by in-Situ Synthesis of Gold Nanoparticles." ECS Transactions 107, no. 1 (April 24, 2022): 16375–90. http://dx.doi.org/10.1149/10701.16375ecst.
Анотація:
Protein-protein interaction (PPI) plays an important role in various biochemical processes. Various binary mixtures of industrially important zein have been blended with rice and wheat proteins to expand their biological applicability. Zein-rice, zein-hard wheat, and zein-soft wheat mixtures show different behavior toward in vitro synthesis of Au NPs. This can also be explained by the TEM, FESEM, and DLS studies of PPI-Au NPs mixtures. Zein-rice-Au NPs mixture showed their mutual favorable interaction with high colloidal stability and high reduction ability towards nucleating Au NPs. This may be due to both hydrophobic and hydrophilic interaction of zein-rice PPI, which resulted in regular and shape-controlled Au NPs. In the case of zein-wheat PPI interaction, lesser hydrophobic interaction resulted in the incomplete unfolding of wheat protein and demixing of wheat protein with zein protein to anisotropic and irregular Au NPs. The study of these interactions will lead to the development of therapeutic centers toward different diseases and their environmental applications.
10
Yang, Lei, and Xianglong Tang. "Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/523634.
Анотація:
Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.
Дисертації з теми "Protein – protein interactions (PPI)":
1
Weimann, Mareike. "A proteome-wide screen utilizing second generation sequencing for the identification of lysine and arginine methyltransferase protein interactions." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16581.
Анотація:
Proteinmethylierung spielt eine immer größere Rolle in der Regulierung zellulärer Prozesse. Die Entwicklung effizienter proteomweiter Methoden zur Detektion von Methylierung auf Proteinen ist limitiert und technisch schwierig. In dieser Arbeit haben wir einen neuen Hefe-Zwei-Hybrid-Ansatz (Y2H) entwickelt, der Proteine, die miteinander wechselwirken, mit Hilfe von Sequenzierungen der zweiten Generation identifiziert (Y2H-Seq). Der neue Y2H-Seq-Ansatz wurde systematisch mit dem Y2H-Seq-Ansatz verglichen. Dafür wurde ein Bait-Set von 8 Protein-Arginin-Methyltransferasen, 17 Protein-Lysin-Methyltransferasen und 10 Demethylasen gegen 14,268 Prey-Proteine getestet. Der Y2H-Seq-Ansatz ist weniger arbeitsintensiv, hat eine höhere Sensitivität als der Standard Y2H-Matrix-Ansatz und ist deshalb besonders geeignet, um schwache Interaktionen zwischen Substraten und Protein-Methyltransferasen zu detektieren. Insgesamt wurden 523 Wechselwirkungen zwischen 22 Bait-Proteinen und 324 Prey-Pr oteinen etabliert, darunter 11 bekannte Methyltransferasen-Substrate. Netzwerkanalysen zeigen, dass Methyltransferasen bevorzugt mit Transkriptionsregulatoren, DNA- und RNA-Bindeproteinen wechselwirken. Diese Daten repräsentieren das erste proteomweite Wechselwirkungsnetzwerk über Protein-Methyltransferasen und dienen als Ressource für neue potentielle Methylierungssubstrate. In einem in vitro Methylierungsassay wurden exemplarisch mit Hilfe massenspektrometrischer Analysen die methylierten Aminosäurereste einiger Kandidatenproteine bestimmt. Von neun getesteten Proteinen waren sieben methyliert, zu denen gehören SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP und WDR42A. Wahrscheinlich sind viele Methylierungssubstrate im Netzwerk vorhanden. Das vorgestellte Protein-Protein-Wechselwirkungsnetzwerk zeigt, dass Proteinmethylierung sehr unterschiedliche zelluläre Prozesse beeinflusst und ermöglicht die Aufstellung neuer Hypothesen über die Regulierung Molekularer Mechanismen durch Methylierung.Protein methylation on arginine and lysine residues is a largely unexplored posttranslational modification which regulates diverse cellular processes. The development of efficient proteome-wide approaches for detecting protein methylation is limited and technically challenging. We developed a novel workload reduced yeast-two hybrid (Y2H) approach to detect protein-protein interactions utilizing second generation sequencing. The novel Y2H-seq approach was systematically evaluated against our state of the art Y2H-matrix screening approach and used to screen 8 protein arginine methyltransferases, 17 protein lysine methyltransferases and 10 demethylases against a set of 14,268 proteins. Comparison of the two approaches revealed a higher sensitivity of the new Y2H-seq approach. The increased sampling rate of the Y2H-seq approach is advantageous when assaying transient interactions between substrates and methyltransferases. Overall 523 interactions between 22 bait proteins and 324 prey proteins were identified including 11 proteins known to be methylated. Network analysis revealed enrichment of transcription regulator activity, DNA- and RNA-binding function of proteins interacting with protein methyltransferases. The dataset represents the first proteome-wide interaction network of enzymes involved in methylation and provides a comprehensively annotated resource of potential new methylation substrates. An in vitro methylation assay coupled to mass spectrometry revealed amino acid methylation of candidate proteins. Seven of nine proteins tested were methylated including SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP and WDR42A indicating that the interaction network is likely to contain many putative methyltransferase substrate pairs. The presented protein-protein interaction network demonstrates that protein methylation is involved in diverse cellular processes and can inform hypothesis driven investigation into molecular mechanisms regulated through methylation.
2
Gilker, Eva Adeline Gilker. "INTERACTIONS AND LOCALIZATION OF PROTEIN PHOSPHATASES, YWHA PROTEINS AND CELL CYCLE CONTROL PROTEINS IN MEIOSIS." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532699317257539.
3
Peri, C. "INVESTIGATING AND PREDICTING THE DETERMINANTS OF PROTEIN-PROTEIN INTERACTIONS THROUGH COMPUTATIONAL-STRUCTURAL BIOLOGY APPROACHES: IMPLICATIONS FOR STRUCTURAL VACCINOLOGY." Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/243392.
Анотація:
Clarifying the physico-chemical principles of protein-protein interactions is critically important to understand the relationships between biological structures and functions in all biochemical mechanisms. In this project we aim to develop, validate and apply new computational-theoretical methods to study and predict the binding regions of proteins starting from 3D structural information and from the analysis of the conformational and physico-chemical properties of the constituting amino acids. In particular, this project entails the integrated analysis of the energetic properties of different datasets of proteins solved at high resolution. In this context, we have focused on four main subjects with different, yet highly intertwined, objectives. The first subject will address the application of an energy-based computational predictor for the identification of possible antibody-binding surfaces (epitopes) of protein antigens from the pathogen Burkholderia pseudomallei, responsible for human melioidosis. The second will focus on the expansion of the same rationale, adapting the method towards different applications, and including as a novel functionality the prediction of MHC-II coupled epitopes to elicit the intervention of T helper cells. The third objective concerns the design and characterization of peptides and peptidomimetics to optimize the properties of the identified epitopes as better vaccine candidates. The fourth one will pursue the investigation of the energetic determinants of interacting proteins in a more general context (not limited to immunogenic epitopes), aiming at the identification of an energy-based property describing the interaction event at the atomistic level of resolution. This part of the project is aimed at the development of a computational tool based on such property to help improve the understanding of the determinants of protein interactions and help predict their binding interfaces and orientation. All four subjects have been investigated in the broad spectrum of activities of an academic consortium, devoted to the identification of antigens from B. pseudomallei showing sufficient immunogenic potential to be considered as components for a vaccine against the pathogen. The computational methods developed and tested within this framework have theoretical as well as practical implications, from the physico-chemical study and characterization of protein-protein interactions, to the design of biologically active molecules.
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Ravindranath, Velaga M. "Elucidating the role of mitoferrin (Mfrn), iron regulatory proteins (IRP1 and IRP2) and hephaestin (Heph) in iron metabolism by tagSNP and protein-protein interaction (PPI) analysis." Thesis, London Metropolitan University, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639414.
Анотація:
Precisely how Hephaestin (Heph) facilitate iron release from cells is poorly understood. The work in this thesis tried to establish the role of different iron metabolic proteins, Mitoferrin (Mfrn), IRPs and Heph in iron homeostasis. Analysis of 18 tagSNPs in the Mfrn gene was carried out in an AsianCaucasian population to establish any correlation between the Mfrn tagSNPs, haemoglobin levels and birth weight in the presence of covariates such as sex of the fetus, gestational age and mother's booking weight. Two-way ANCOVA analysis was carried out to check if the covariates have any influence on the dependent variable in the presence of fixed factors. From the ANCOVA analysis of Mfrn tagSN Ps it can be concluded that neither the haemoglobin levels nor the birth weight are dependent on the genotype, fetal sex, nor on their interaction. Owing to the significance in identifying the interacting partners of IRPs and Heph to understand more about their role in iron metabolism, protein-protein interaction studies were also carried out. IRPs and Heph genes were successfully cloned with One-Strep tag. Full length clones were sequence confirmed for any variation after PCR. Before carrying out immunoprecipitation to identify the interacting partners, transfection efficiency, viability and the role of magnetic particles on K562 cells was performed by using IRPs and Heph cloned with One-Strep tag. Lipofectamine-L TX plus transfection had more viable cells and higher efficiency compared with magnetic-assisted transfection . Also, this study confirms that magnetic nanoparticles do not have any adverse or significant effect on IRPs during the transfection. An unsuccessful attempt was made to identify the interacting partners of IRPs and Heph by immunoprecipitation. The current thesis work also involved identification of a potential ferroxidase . Ceruloplasmin (Cp) was used as a postive control. Non-denaturing gel eletrophoresis of the K562, MDA-MB-231 and PNT2-C2 cell fractions confirmed the presence of the extra band establishing the ubiquitous nature of the band. Mass spectrometry analysis identified the excised band as Calreticulin (CALR). This is the first report of calreticulin having ferroxidase activity.
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Johansson, Joakim. "Modifying a Protein-Protein Interaction Identifier with a Topology and Sequence-Order Independent Structural Comparison Method." Thesis, Linköpings universitet, Bioinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-147777.
Анотація:
Using computational methods to identify protein-protein interactions (PPIs) supports experimental techniques by using less time and less resources. Identifying PPIs can be made through a template-based approach that describes how unstudied proteins interact by aligning a common structural template that exists in both interacting proteins. A pipeline that uses this is InterPred, that combines homology modelling and massive template comparison to construct coarse interaction models. These models are reviewed by a machine learning classifier that classifies models that shows traits of being true, which can be further refined with a docking technique. However, InterPred is dependent on using complex structural information, that might not be available from unstudied proteins, while it is suggested that PPIs are dependent of the shape and interface of proteins. A method that aligns structures based on the interface attributes is InterComp, which uses topological and sequence-order independent structural comparison. Implementing this method into InterPred will lead to restricting structural information to the interface of proteins, which could lead to discovery of undetected PPI models. The result showed that the modified pipeline was not comparable based on the receiver operating characteristic (ROC) performance. However, the modified pipeline could identify new potential PPIs that were undetected by InterPred.
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Worseck, Josephine Maria. "Characterization of phosphorylation-dependent interactions involving neurofibromin 2 (NF2, merlin) isoforms and the Parkinson protein 7 (PARK7, DJ1)." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16533.
Анотація:
Veränderungen in phosphorylierungsabhängigen Signalwegen, Akkumulation von Proteinaggregaten im Gehirn und neuronaler Zelltod sind Neurodegenerationskennzeichen und Indikatoren für überlappende molekulare Mechanismen. Um Einblicke in die involvierten Signalwege zu erhalten, wurde mit Hilfe eines modifizierten Hefe-Zwei-Hybrid (Y2H)-Systems für 71 Proteine, die mit neurologischen Erkrankungen assoziiert sind, proteomweit nach Protein-Protein Interaktionen (PPIs) gesucht. Für 21 dieser Proteine wurden PPIs identifiziert. Das Gesamtnetzwerk besteht aus 79 Proteinen und 90 PPIs von denen 5 phosphorylierungsabhängig sind. Ein Teil dieser PPIs wurde in unabhängigen Interaktionsassays mit einer Validierungsrate von 66 % getestet. Der netzwerkbasierte Versuch verbindet erfolgreich neurologische Erkrankungen untereinander aber auch mit zellulären Prozessen. Ser/Thr-Kinase abhängige PPIs verknüpfen zum Beispiel das Parkinson Protein 7 (PARK7, DJ1) mit den E3 Ligase Komponenten ASB3 und RNF31 (HOIP). Die Funktion dieser Proteine bekräftigt den Zusammenhang zwischen dem Ubiquitin-Proteasom-System und der Parkinson Krankheit (PD). Neurofibromin 2 (NF2, merlin) Isoformen und PARK7 interagieren mit der regulatorischen PI3K Untereinheit p55-gamma (PIK3R3). Diese PPIs basieren auf Tyr-Kinase Aktivität im modifizierten Y2H System und funktionellen PIK3R3 pTyr-Erkennungsmodulen (SH2 Domänen) in co-IP und Venus PCA Versuchen. Dies verknüpft den PI3K/AKT Überlebenssignalweg mit zwei unterschiedlichen neurologischen Erkrankungsphenotypen: dem PD assoziierten neuronalen Zelltod und der Neurofibromatose Typ 2-assoziierten Tumorentstehung. Die vergleichende Beobachtung von PIK3R3, AOF2 (KDM1A, LSD1) Interaktionen auf NF2 Isoformlevel offenbart eine Bevorzugung von Isoform 7 bei zytoplasmatischer Lokalisation, wohingegen Isoform 1 PPIs an der Membran lokalisiert sind. Das modifizierungsabhängige und isoformspezifische PPI Netzwerk ermöglichte neue Hypothesen zu molekularen Pathomechanismen.Alterations in phosphorylation-dependent signalling pathways, accumulation of aggregated proteins in the brain and neuronal apoptosis are common to neurodegeneration and implicate overlapping molecular mechanism. To gain insight into involved pathways, a modified yeast-two hybrid (Y2H) system was applied to screen 71 proteins associated with neurological disorders in a proteome-wide manner. For 21 of these proteins interactions were identified including 5 phosphorylation-dependent ones. In total, the network connected 79 proteins through 90 protein-protein interactions (PPIs). A fraction of these Y2H PPIs was tested in secondary interaction assays with a validation rate of 66 %. The described network-based approach successfully identified proteins associated with more than one disorder and cellular functions connected to specific disorders. In particular, the network revealed Ser/Thr kinase-dependent PPIs between the Parkinson protein 7 (PARK7, DJ1) and the E3 ligase components ASB3 and RNF31 (HOIP). The function of these proteins further substantiates the established connection between Parkinson’s disease (PD) and ubiquitination-mediated proteasome (dis)functions. Neurofibromin 2 (NF2, merlin) isoforms and PARK7 were identified as PI3K regulatory subunit p55-gamma (PIK3R3) interactors. These PPIs required Tyr kinase coexpression in the modified Y2H system and functional PIK3R3 pTyr-recognition modules (SH2 domains) in co-IP and Venus PCA experiments. This finding implicates the PI3K/AKT survival pathway in PD-associated neuronal apoptosis and Neurofibromatosis type 2-associated tumour formation. Investigation of PIK3R3, AOF2 (KDM1A, LSD1) and EMILIN1 PPIs on NF2 isoform level revealed preferential isoform 7 binding and cytoplasmic or membrane localisation of these PPIs for isoform 7 or 1, respectively. The generated modification-dependent and isoform-specific PPI network triggered many hypotheses on the molecular mechanisms implicated in neurological disorders.
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Johansson-Åkhe, Isak. "PePIP : a Pipeline for Peptide-Protein Interaction-site Prediction." Thesis, Linköpings universitet, Institutionen för fysik, kemi och biologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138411.
Анотація:
Protein-peptide interactions play a major role in several biological processes, such as cellproliferation and cancer cell life-cycles. Accurate computational methods for predictingprotein-protein interactions exist, but few of these method can be extended to predictinginteractions between a protein and a particularly small or intrinsically disordered peptide. In this thesis, PePIP is presented. PePIP is a pipeline for predicting where on a given proteina given peptide will most probably bind. The pipeline utilizes structural aligning to perusethe Protein Data Bank for possible templates for the interaction to be predicted, using thelarger chain as the query. The possible templates are then evaluated as to whether they canrepresent the query protein and peptide using a Random Forest classifier machine learningalgorithm, and the best templates are found by using the evaluation from the Random Forest in combination with hierarchical clustering. These final templates are then combined to givea prediction of binding site. PePIP is proven to be highly accurate when testing on a set of 502 experimentally determinedprotein-peptide structures, suggesting a binding site on the correct part of the protein- surfaceroughly 4 out of 5 times.
8
Aveiro, Susana Seabra. "The p22HBP heme binding protein: an NMR study of the dynamics and heme-protein interactions." Doctoral thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/14278.
Анотація:
Doutoramento em BioquímicaThe work presented in this Thesis investigates the dynamics and molecular interactions of p22HBP and the p22HBP-tetrapyrrole complex. Specifically, the key residues involved when a tetrapyrrole binds to p22HBP were sought. Previous molecular modelling studies identified three possible charged residues R56, K64 and K177 as possibly being important in tetrapyrrole binding via electrostatic interactions with the propionate groups of the tetrapyrrole. A number of variants of murine p22HBP were therefore prepared and fluorescence quenching and NMR used to verify the integrity of the variants and their interaction with tetrapyrrole. The same molecular modelling studies identified a mobile loop Y171-R180 in p22HBP that decreased in mobility on tetrapyrrole binding, therefore to confirm this mobility change dynamics studies based on NMR relaxation experiments were carried out. Finally in order to obtain a non heme-binding form of human p22HBP a chimeric p22HBP was designed and constructed. This construct, and the resulting protein, will be important for future siRNA knockdown studies where rescue or recovery of function experiments are required to prove the knockdown results. Chapter one discusses the current state of the art in terms of the biological, structural and functional aspects of p22HBP. The main objectives of the Thesis are also introduced here. Chapter two presents a detailed description of the different expression vectors (pNJ2 and pet28-a) and procedures used for overexpression and purification of murine p22HBP and its variants and human p22HBP. All expression and purification systems used gave good yields and allowed isotopic labeling to be carried out. The fluorescence quenching results for tetrapyrrole binding to murine p22HBP and variants are presented in chapter three along with the dissociation constants that were found to be in the nanomolar range for wild type murine and human p22HBP. The same studies were performed for murine p22HBP variants, with hydrophobic and polar changes being introduced at R56, K64 and K177. The dissociation constants were found to double in some cases but no significant changes in the strength of hemin-protein interactions were observed. The tetrapyrrole interaction with p22HBP was also followed by NMR spectroscopy, where chemical shift mapping was used to identify binding pocket location. All the variants and wild type human p22HBP were found to bind at the same location. Chapter 4 contains the data from 2D and 3D experiments carried out on 15N/13C labelled human p22HBP that was used to obtain backbone assignments. Comparison with wild type murine p22HBP assignments, PPIX titrations and theoretical calculations based on chemical shifts (Talos+) allowed 82% of the backbone resonances to be assigned. The results from the relaxation experiments used to probe the dynamics of the mobile loop in p22HBP on binding to tetrapyrrole are presented in chapter 5. The overall protein was found to tumble isotropically in the free and bound forms however the results to probe mobility changes in the 171-180 loop on tetrapyrrole binding proved inconclusive as only residue could be assigned and this did not seem to become significantly less mobile. The final chapter describes the design and construction of a chimeric p22HBP. For these purpose, the alfa1-helix sequence of human p22HBP in the phHBP1 plasmid was replaced by its homologous sequence in hSOUL, a non heme-binding protein with identical 3D structure. The results however indicated that either the incorrect sequence was introduced into the plasmid or the purification procedure was inadequate.
O trabalho apresentado nesta Tese focou-se na dinâmica e nas interações moleculares da p22HBP e do complexo p22HBP-tetrapirrol, nomeadamente nos resíduos chave envolvidos nesta interação. Estudos prévios de modelação molecular identificaram três possíveis resíduos chave R56, K64 e K177 como sendo importantes na interação com os tetrapirróis, através de interações eletrostáticas com os grupos propionato do tetrapirrol. Foram desenhados e construídos variantes da p22HBP murina e foram desenvolvidos estudos de extinção de fluorescência e RMN para avaliar a integridade dos variantes e a sua interação com os tetrapirróis. Os mesmos estudos de modelação molecular identificaram ainda uma zona flexível (Y171-R180) na p22HBP que diminui a mobilidade com a interação do tetrapirrol. Para confirmar esta alteração de mobilidade, foram realizados estudos de dinâmica, baseados em RMN. Por fim, com o intuito de obter uma versão não funcional da p22HBP humana, foi planeada e construída uma versão quimérica da p22HBP humana. No futuro, esta nova versão da p22HBP quimérica, será importante para os estudos de knockdown envolvendo siRNA. O capítulo um introduz uma revisão dos aspetos biológicos da p22HBP nomeadamente os estudos estruturais e as possíveis funções que foram identificadas. Os principais objetivos da tese são também apresentados neste capítulo. No capítulo dois é apresentada uma descrição detalhada dos diferentes vectores de sobreexpressão (pNJ2 e pet28-a) e dos métodos de sobreexpressão e purificação da p22HBP murina e respectivos variantes, bem como da p22HBP humana. Todos os sistemas de sobreexpressão e purificação utilizados obtiveram bons rendimentos e permitiram a marcação isotópica das proteínas. No capítulo 3 são apresentados os resultados de extinção de fluorescência para a interação da p22HBP murina e humana com hemina através das constantes de dissociação determinadas na ordem dos nanomolar. Os mesmos estudos foram realizados para os variantes da p22HBP murina, com alterações hidrofóbicas e de polaridade nos resíduos R56, K64 e K177. Em alguns casos, as constantes de dissociação determinadas são mais elevadas, embora não se tenham verificado alterações significativas na força da interação proteína-hemo. As interações tetrapirrólicas com a p22HBP foram também estudadas por espectroscopia de RMN, onde foram mapeadas as diferenças nos desvios químicos para identificar a localização da zona de interação. A localização da zona de interação dos variantes da p22HBP e a p2HBP humana mantém-se igual à p22HBP murina. No capítulo 4 encontram-se os resultados das experiências 2D e 3D realizadas na p22HBP humana, isotopicamente marcada com 15N/13C, para identificar as ressonâncias da cadeia principal. 82% dos sistemas de spin da cadeia principal foram identificados através da comparação com a p22HBP murina, das titulações com PPIX e de cálculos teóricos baseados nos desvios químicos (Talos+). No capítulo 5 são apresentados os resultados das experiências de relaxação, usados para comprovarem a dinâmica do loop na p22HBP aquando da interação com o tetrapirrol. A proteína no seu todo move-se de uma forma isotrópica na forma livre e ligada. No entanto os resultados para comprovar as alterações de mobilidade no loop 171-180 na presença de hemo, foram inconclusivos uma vez que só a um resíduo foi atribuído um sistema de spin, e não foi indicativo da perda significativa de mobilidade. O último capítulo descreve o planeamento e a construção da p22HBP quimérica. Para tal, a sequência que codifica a hélix alfa 1 da p22HBP humana, no plasmídeo phHBP1, foi substituída pela sequência homóloga da SOUL humana, uma proteína com uma estrutura 3D semelhante mas não liga ao hemo. Os resultados no entanto demonstraram que ou a sequência não foi introduzida corretamente no plasmídeo ou o sistema de purificação não foi adequado.
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Simões, Sérgio Nery. "Uma abordagem de integração de dados de redes PPI e expressão gênica para priorizar genes relacionados a doenças complexas." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-17112015-172846/.
Анотація:
Doenças complexas são caracterizadas por serem poligênicas e multifatoriais, o que representa um desafio em relação à busca de genes relacionados a elas. Com o advento das tecnologias de sequenciamento em larga escala do genoma e das medições de expressão gênica (transcritoma), bem como o conhecimento de interações proteína-proteína, doenças complexas têm sido sistematicamente investigadas. Particularmente, baseando-se no paradigma Network Medicine, as redes de interação proteína-proteína (PPI -- Protein-Protein Interaction) têm sido utilizadas para priorizar genes relacionados às doenças complexas segundo suas características topológicas. Entretanto, as redes PPI são afetadas pelo viés da literatura, em que as proteínas mais estudadas tendem a ter mais conexões, degradando a qualidade dos resultados. Adicionalmente, métodos que utilizam somente redes PPI fornecem apenas resultados estáticos e não-específicos, uma vez que as topologias destas redes não são específicas de uma determinada doença. Neste trabalho, desenvolvemos uma metodologia para priorizar genes e vias biológicas relacionados à uma dada doença complexa, através de uma abordagem integrativa de dados de redes PPI, transcritômica e genômica, visando aumentar a replicabilidade dos diferentes estudos e a descoberta de novos genes associados à doença. Após a integração das redes PPI com dados de expressão gênica, aplicamos as hipóteses da Network Medicine à rede resultante para conectar genes sementes (relacionados à doença, definidos a partir de estudos de associação) através de caminhos mínimos que possuam maior co-expressão entre seus genes. Dados de expressão em duas condições (controle e doença) são usados separadamente para obter duas redes, em que cada nó (gene) dessas redes é pontuado segundo fatores topológicos e de co-expressão. Baseado nesta pontuação, desenvolvemos dois escores de ranqueamento: um que prioriza genes com maior alteração entre suas pontuações em cada condição, e outro que privilegia genes com a maior soma destas pontuações. A aplicação do método a três estudos envolvendo dados de expressão de esquizofrenia recuperou com sucesso genes diferencialmente co-expressos em duas condições, e ao mesmo tempo evitou o viés da literatura. Além disso, houve uma melhoria substancial na replicação dos resultados pelo método aplicado aos três estudos, que por métodos convencionais não alcançavam replicabilidade satisfatória.Complex diseases are characterized as being poligenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing and gene expression measurements (transcriptome), as well as the knowledge of protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which the most studied proteins tend to have more connections, degrading the quality of the results. Additionally, methods using only PPI networks can provide just static and non-specific results, since the topologies of these networks are not specific of a given disease. In this work, we developed a methodology to prioritize genes and biological pathways related to a given complex disease, through an approach that integrates data from PPI networks, transcriptomics and genomics, aiming to increase replicability of different studies and to discover new genes associated to the disease. The methodology integrates PPI network and gene expression data, and then applies the Network Medicine Hypotheses to the resulting network in order to connect seed genes (obtained from association studies) through shortest paths possessing larger coexpression among their genes. Gene expression data in two conditions (control and disease) are used to obtain two networks, where each node (gene) in these networks is rated according to topological and coexpression aspects. Based on this rating, we developed two ranking scores: one that prioritizes genes with the largest alteration between their ratings in each condition, and another that favors genes with the greatest sum of these scores. The application of this method to three studies involving schizophrenia expression data successfully recovered differentially co-expressed gene in two conditions, while avoiding the ascertainment bias. Furthermore, when applied to the three studies, the method achieved a substantial improvement in replication of results, while other conventional methods did not reach a satisfactory replicability.
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Lima, Leandro de Araujo. "Uma abordagem integrativa usando dados de interação proteína-proteína e estudos genéticos para priorizar genes e funções biológicas em transtorno de déficit de atenção e hiperatividade." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-24082015-160400/.
Анотація:
O Transtorno de Déficit de Atenção e Hiperatividade (TDAH) é a doença do neurodesenvolvimento mais comum na infância, afetando cerca de 5,8% de crianças e adolescentes no mundo. Muitos estudos vêm tentando investigar a suscetibilidade genética em TDAH, mas sem muito sucesso. Este estudo teve como objetivo analisar variantes raras e comuns contribuindo para a arquitetura genética do TDAH. Foram gerados os primeiros dados de exoma de TDAH de 30 trios brasileiros em que o filho foi diagnosticado com TDAH esporádico. Foram analisados tanto variações de único nucleotídeo (ou SNVs, single-nucleotide variants) quanto variações de número de cópias (ou CNVs, copy-number variants), tanto nesses trios quanto em outros conjuntos de dados, incluindo uma amostra brasileira de 503 crianças/adolescentes controles, bem como resultados previamente publicados em quatro estudos com variação de número de cópias e uma meta-análise de estudos de associação ao longo do genoma. Tanto os trios quanto os controles fazem parte da Coorte de Escolares de Alto Risco para o desenvolvimento de Psicopatologia e Resiliência na Infância do Instituto Nacional de Psiquiatria do Desenvolvimento (INPD). Os resultados de trios brasileiros mostraram três padrões marcantes: casos com variações herdadas e somente SNVs de novo ou CNVs de novo, e casos somente com variações herdadas. Embora o tamanho amostral seja pequeno, pudemos ver que diferentes comorbidades são mais frequentes em casos somente com variações herdadas. Após explorarmos a composição de variações nos probandos brasileiros, foram selecionados genes recorrentes entre amostras do nosso estudo ou em bancos de dados públicos. Além disso, usando somente genes expressos no cérebro (amostras pós-mortem dos projetos Brain Atlas e Genotype-Tissue Expression), construímos uma rede de interação proteína-proteína \"in silico\" com interações físicas confirmadas por pelo menos duas fontes. Análises topológicas e funcionais dos genes da rede mostraram genes relacionados a sinapse, adesão celular, vias glutamatérgicas e serotonérgicas, o que confirma achados de trabalhos independentes na literatura indicando ainda novos genes e variantes genéticas nessas vias.Attention-Deficit/Hyperactivity Disorder (ADHD) is the most common neuro-developmental disorder in children, affecting 5.8% of children and adolescents in the world. Many studies have attempted to investigate the genetic susceptibility of ADHD without much success. The present study aimed to analyze rare and common variants contributing to the genetic architecture of ADHD. We generated exome data from 30 Brazilian trios where the children were diagnosed with sporadic ADHD. We analyzed both single-nucleotide variants (SNVs) and copy-number variants (CNVs) in these trios and across multiple datasets, including a Brazilian sample of 503 children/adolescent controls from the High Risk Cohort Study for the Development of Childhood Psychiatric Disorders, and also previously published results of four CNV studies of ADHD involving children/adolescent Caucasian samples. The results from the Brazilian trios showed 3 major patterns: cases with inherited variations and de novo SNVs or de novo CNVs and cases with only inherited variations. Although the sample size is small, we could see that various comorbidities are more frequent in cases with only inherited variants. After exploring the rare variant composition in our 30 cases we selected genes with variations (SNVs or located in CNV regions) in our trio analysis that are recurrent in the families analyzed or in public data sets. Moreover, using only genes expressed in brain (post-mortem samples from Brain Atlas and The Genotype-Tissue Expression project), we constructed an in silico protein-protein interaction (PPI) network, with physical interactions confirmed by at least two sources. Topological and functional analyses of genes in this network uncovered genes related to synapse, cell adhesion, glutamatergic and serotoninergic pathways, both confirming findings of previous studies and capturing new genes and genetic variants in these pathways.
Книги з теми "Protein – protein interactions (PPI)":
1
Fu, Haian. Protein-Protein Interactions. New Jersey: Humana Press, 2004. http://dx.doi.org/10.1385/1592597629.
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Poluri, Krishna Mohan, Khushboo Gulati, and Sharanya Sarkar. Protein-Protein Interactions. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1594-8.
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Meyerkord, Cheryl L., and Haian Fu, eds. Protein-Protein Interactions. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2425-7.
4
Wendt, Michael D., ed. Protein-Protein Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28965-1.
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Kangueane, Pandjassarame. Protein-protein interactions. Hauppauge, N.Y: Nova Science Publisher's, 2010.
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Wendt, Michael D. Protein-Protein Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
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Mukhtar, Shahid, ed. Protein-Protein Interactions. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3327-4.
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Poluri, Krishna Mohan, Khushboo Gulati, Deepak Kumar Tripathi, and Nupur Nagar. Protein-Protein Interactions. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2423-3.
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Ruth, Nussinov, and Schreiber Gideon, eds. Computational protein-protein interactions. Boca Raton: CRC Press/Taylor & Francis, 2009.
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Schuck, Peter, ed. Protein Interactions. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-35966-3.
Частини книг з теми "Protein – protein interactions (PPI)":
1
Fernholz, Mikayla M., Leonie M. Windeln, Monika Papayova, and Ali Tavassoli. "Chapter 6. Genetically Encoded SICLOPPS Libraries for the Identification of PPI Inhibitors." In Inhibitors of Protein–Protein Interactions, 218–31. Cambridge: Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781839160677-00218.
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Yang, Xuan, and Andrey A. Ivanov. "CHAPTER 4. Computational Structural Modeling to Discover PPI Modulators." In Protein–Protein Interaction Regulators, 87–108. Cambridge: Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788016544-00087.
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Doyle, Sean P., Xiulei Mo, Kun Qian, Danielle N. Cicka, Qiankun Niu, and Haian Fu. "CHAPTER 3. High Throughput Screening Methods for PPI Inhibitor Discovery." In Protein–Protein Interaction Regulators, 49–86. Cambridge: Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788016544-00049.
4
SHI, LEI, XIUJUAN LEI, and AIDONG ZHANG. "Protein Functional Module Analysis With Protein-Protein Interaction (PPI) Networks." In Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics, 393–411. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118567869.ch20.
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Tuncbag, Nurcan, Ozlem Keskin, Ruth Nussinov, and Attila Gursoy. "Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information." In Protein Bioinformatics, 255–70. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_12.
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Kapadia, Paritosh, Saudamini Khare, Piali Priyadarshini, and Bhaskarjyoti Das. "Predicting Protein-Protein Interaction in Multi-layer Blood Cell PPI Networks." In Communications in Computer and Information Science, 240–51. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0111-1_22.
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Gadde, Sai Gopala Swamy, Kudipudi Pravallika, and Kudipudi Srinivas. "Evolutionary, Protein–Protein Interaction (PPI), and Domain–Domain Analyses in Huntington’s Disease." In Lecture Notes in Electrical Engineering, 11–23. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6690-5_2.
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Ahuja, Khushi, Aditi Joshi, Navjyoti Chakraborty, Ram Singh Purty, and Sayan Chatterjee. "Comparative Analysis of Computational Methods used in Protein-Protein Interaction (PPI) Studies." In Computational and Analytic Methods in Biological Sciences, 63–100. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003393238-4.
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Wang, Wei, and Jinwen Ma. "Density Based Merging Search of Functional Modules in Protein-Protein Interaction (PPI) Networks." In Lecture Notes in Computer Science, 634–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14922-1_79.
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Ben M’barek, Marwa, Amel Borgi, Sana Ben Hmida, and Marta Rukoz. "GA-PPI-Net: A Genetic Algorithm for Community Detection in Protein-Protein Interaction Networks." In Communications in Computer and Information Science, 133–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52991-8_7.
Тези доповідей конференцій з теми "Protein – protein interactions (PPI)":
1
Lv, Guofeng, Zhiqiang Hu, Yanguang Bi, and Shaoting Zhang. "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/506.
Анотація:
The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms. Existing methods suffer from significant performance degradation when tested in unseen dataset. In this paper, we investigate the problem and find that it is mainly attributed to the poor performance for inter-novel-protein interaction prediction. However, current evaluations overlook the inter-novel-protein interactions, and thus fail to give an instructive assessment. As a result, we propose to address the problem from both the evaluation and the methodology. Firstly, we design a new evaluation framework that fully respects the inter-novel-protein interactions and gives consistent assessment across datasets. Secondly, we argue that correlations between proteins must provide useful information for analysis of novel proteins, and based on this, we propose a graph neural network based method (GNN-PPI) for better inter-novel-protein interaction prediction. Experimental results on real-world datasets of different scales demonstrate that GNN-PPI significantly outperforms state-of-the-art PPI prediction methods, especially for the inter-novel-protein interaction prediction.
2
Ghosh, Supratim, Burcu Guldiken, Maxime Saffon, and Michael Nickeson. "Improved emulsification behaviour of pea protein-polysaccharide complexes for beverage application." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/oniy9265.
Анотація:
Proteins are widely used as emulsifiers in food formulations. However, emulsifying properties of proteins are weak at pH values close to their isoelectric point resulting in destabilization. Protein-polysaccharide interactions have been proposed to improve the emulsification behaviour of proteins in such conditions. In this work, two different polysaccharides (pectin and gum Arabic) with a range of surface charges were chosen to investigate their interactions with pea proteins. The initial aim was to investigate the effect of heat treatment on the complexation of pea protein isolate (PPI) and the polysaccharide with the ultimate purpose of using them as effective emulsifiers at various pH values for beverage application. The emulsions were prepared, and the emulsification ability was determined with the selected protein-polysaccharide complexes at both basic (pH 8.0) and isoelectric pH (pH 4.5) conditions. Turbidity graphs of gum Arabic-PPI and or pectin-PPI complexes at 1:1 mixing ratio revealed an increase in the pH range of the soluble complexes upon heat treatment of the mixture to 75ºC. The soluble complexes of the protein and polysaccharide were able to stabilize oil-in-water beverage emulsions at the isoelectric pH of the protein. The stabilization effect of soluble pectin-PPI complexes was better than gum Arabic-PPI complexes at pH 4.5. At pH 8, although droplet sizes were similar, pectin-PPI complexes caused depletion flocculation leading to a higher accelerated creaming velocity of the emulsion than the gum Arabic-PPI complexes. The emulsions stabilized by pectin-PPI complexes at pH 4.5 had the highest emulsion stability in terms of lower instability index, lower accelerated creaming velocity and the lowest droplet diameter than all other emulsions. The findings of this study will provide beneficial information on the effect of processing conditions on biopolymer interactions and the emulsification ability of protein-polysaccharide complexes for the application in beverage emulsions.
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Kralj, Sebastjan, Milan Hodošček, Marko Jukić, and Urban Bren. "A comprehensive in silico protocol for fast automated mutagenesis and binding affinity scoring of protein-ligand complexes." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.674k.
Анотація:
Protein-protein interactions (PPI) are critical for cellular functions, host-pathogen dynamics and are crucial with drug design efforts. The interaction of proteins is dependent on the amino acid sequence of a protein as it determines its binding affinity to various molecules, including drugs, DNA, RNA, and proteins. Polymorphisms, natural DNA variations, affect PPIs by altering protein structure and stability. Computational chemistry is vital for the prediction of ligand-protein interactions through techniques such as docking and molecular dynamics and can elucidate the changes in energy associated with such mutations. We present a user-friendly protocol that uses the INTE command of CHARMM to predict the effects of mutations on PPIs. This command-line tool automates mutation analysis and interaction energy estimation, is applicable to different ligand types (protein, DNA, RNA, ion, small molecule) and provides various other features. The energy values yield absolute and normalized heat maps that allow rapid identification of stabilizing and destabilizing mutations. Our protocol forms the basis for automated programs that facilitate studies of binding-altering mutations in host-pathogen, protein-protein, and drug-target interactions.
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Zhao, Ziyuan, Peisheng Qian, Xulei Yang, Zeng Zeng, Cuntai Guan, Wai Leong Tam, and Xiaoli Li. "SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein–Protein Interaction Prediction." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/554.
Анотація:
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods suffer from significant performance degradation under complex real-world scenarios due to various factors, e.g., label scarcity and domain shift. In this paper, we propose a self-ensembling multi-graph neural network (SemiGNN-PPI) that can effectively predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we not only model the protein correlations but explore the label dependencies by constructing and processing multiple graphs from the perspectives of both features and labels in the graph learning process. We further marry GNN with Mean Teacher to effectively leverage unlabeled graph-structured PPI data for self-ensemble graph learning. We also design multiple graph consistency constraints to align the student and teacher graphs in the feature embedding space, enabling the student model to better learn from the teacher model by incorporating more relationships. Extensive experiments on PPI datasets of different scales with different evaluation settings demonstrate that SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly in challenging scenarios such as training with limited annotations and testing on unseen data.
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Umbrin, Hina, and Saba Latif. "A survey on Protein Protein Interactions (PPI) methods, databases, challenges and future directions." In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, 2018. http://dx.doi.org/10.1109/icomet.2018.8346326.
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Oviya, I. R., Shanmukha Sravya N, and Kalpana Raja. "R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks." In 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2024. http://dx.doi.org/10.1109/icaect60202.2024.10469595.
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Ma, Xiaoke, and Lin Gao. "Detecting protein complexes in PPI networks: The roles of interactions." In 2011 IEEE International Conference on Systems Biology (ISB). IEEE, 2011. http://dx.doi.org/10.1109/isb.2011.6033120.
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Soude, Anne, Martine Barth, Stephanie Bocart, Frederic Thoreau, Philippe Masson, Isabelle Braccini, Christian Montalbetti, Pierre Broqua, and Claudia Fromond. "Abstract 894: Discovery of YAP-TEAD protein-protein interaction (PPI) inhibitors for cancer therapy." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-894.
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Soude, Anne, Martine Barth, Stephanie Bocart, Frederic Thoreau, Elina Mandry, Sylvie Contal, Philippe Masson, et al. "Abstract A129: Generation of YAP-TEAD Protein-Protein Interaction (PPI) inhibitors for the treatment of cancer." In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; November 5-9, 2015; Boston, MA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1535-7163.targ-15-a129.
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Soudé, Anne, Martine Barth, Jean-Michel Luccarini, Séverine Delaporte, Florence Chirade, Christelle Valaire, Aude Boulay, et al. "Abstract B14: Discovery of YAP-TEAD protein-protein interaction inhibitors (PPI) for treating malignant pleural mesothelioma (MPM)." In Abstracts: AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; May 8-11, 2019; San Diego, CA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1557-3125.hippo19-b14.
Звіти організацій з теми "Protein – protein interactions (PPI)":
1
Noy, A., T. Sulchek, and R. Friddle. Direct Probing of Protein-Protein Interactions. Office of Scientific and Technical Information (OSTI), March 2005. http://dx.doi.org/10.2172/15015174.
2
Dickman, Martin B., and Oded Yarden. Modulation of the Redox Climate and Phosphatase Signaling in a Necrotroph: an Axis for Inter- and Intra-cellular Communication that Regulates Development and Pathogenicity. United States Department of Agriculture, August 2011. http://dx.doi.org/10.32747/2011.7697112.bard.
Анотація:
The long-term goals of our research are to understand the regulation of sclerotial development and pathogenicity in S. sclerotiorum. The focus in this project is on the elucidation of the signaling events and environmental cues that contribute to broad pathogenic success of S. sclerotiorum. In this proposal, we have taken advantage of the recent conceptual (ROS/PPs signaling) and technical (genome sequence availability and gene inactivation possibilities) developments to address the following questions, as appear in our research goals stated below, specifically concerning the involvement of REDOX signaling and protein dephosphorylation in the regulation of hyphal/sclerotial development and pathogenicity of S. sclerotiorum. Our stated specific objectives were to progress our understanding of the following questions: (i) Which ROS species affect S. sclerotiorum development and pathogenicity? (ii) In what manner do PPs affect S. sclerotiorum development and pathogenicity? (iii) Are PPs affected by ROS production and does PP activity affect ROS production and SMK1? (iv) How does Sclerotinia modulate the redox environment in both host and pathogen? While addressing these questions, our main findings include the identification and characterization the NADPH oxidase (NOX) family in S. sclerotiorum. Silencing of Ssnox1 indicated a central role for this enzyme in both virulence and pathogenic (sclerotial) development, while inactivation of Ssnox2 resulted in limited sclerotial development but remained fully pathogenic. Interestingly, we found a consistent correlation with Ssnox1(involved with pathogenicity) and oxalate levels. This same observation was also noted with Sssod1. Thus, fungal enzymes involved in oxidative stress tolerance,when inactivated, also exhibit reduced OA levels. We have also shown that protein phosphatases (specifically PP2A and PTP1) are involved in morphogenesis and pathogenesis of S. sclerotiorum, demonstrating the regulatory role of these key proteins in the mentioned processes. While probing the redox environment and host-pathogen interactions we determined that oxalic acid is an elicitor of plant programmed cell death during S. sclerotiorum disease development and that oxalic acid suppresses host defense via manipulation of the host redox environment. During the course of this project we also contributed to the progress of understanding S. sclerotiorum function and the manipulation of this fungus by establishing an efficient gene replacement and direct hyphal transformation protocols in S. sclerotiorum. Lastly, both PIs were involved in thegenomic analysis of this necrotrophic fungal pathogen (along with Botrytis cinerea). Our results have been published in 11 papers (including joint papers and refereed reviews) and have set the basis for a continuum towards a better understanding and eventual control of this important pathogen (with implications to other fungal-host systems as well).
3
Nelson, Nathan, and Charles F. Yocum. Structure, Function and Utilization of Plant Photosynthetic Reaction Centers. United States Department of Agriculture, September 2012. http://dx.doi.org/10.32747/2012.7699846.bard.
Анотація:
Light capturing and energy conversion by PSI is one of the most fundamental processes in nature. In the heart of these adaptations stand PSI, PSII and their light harvesting antenna complexes. The main goal of this grant proposal was to obtain by X-ray crystallography information on the structure of plant photosystem I (PSI) and photosystem II (PSII) supercomplexes. We achieved several milestones along this line but as yet, like several strong laboratories around the world, we have no crystal structure of plant PSII. We have redesigned the purification and crystallization procedures and recently solved the crystal structure of the PSI supercomplex at 3.3 Å resolution. Even though this advance in resolution appears to be relatively small, we obtained a significantly improved model of the supercomplex. The work was published in J. Biol. Chem. (Amunts et al., 2010). The improved electron density map yielded identification and tracing of the PsaK subunit. The location of an additional 10 ß-carotenes, as well as 5 chlorophylls and several loop regions that were previously uninterruptable have been modeled. This represents the most complete plant PSI structure obtained thus far, revealing the locations of and interactions among 17 protein subunits and 193 non-covalently bound photochemical cofactors. We have continued extensive experimental efforts to improve the structure of plant PSI and to obtain PSII preparation amenable to crystallization. Most of our efforts were devoted to obtain well-defined subcomplexes of plant PSII preparations that are amenable to crystallization. We studied the apparent paradox of the high sensitivity of oxygen evolution of isolated thylakoids while BBY particles exhibit remarkable resilience to the same treatment. The integrity of the photosystem II (PSII) extrinsic protein complement as well as calcium effects arise from the Ca2+ atom associated with the site of photosynthetic water oxidation were investigated. This work provides deeper insights into the interaction of PsbO with PSII. Sight-directed mutagenesis indicated the location of critical sites involved in the stability of the water oxidation reaction. When combined with previous results, the data lead to a more detailed model for PsbO binding in eukaryotic PSII.
4
Avni, Adi, and Gitta L. Coaker. Proteomic investigation of a tomato receptor like protein recognizing fungal pathogens. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600030.bard.
Анотація:
Maximizing food production with minimal negative effects on the environment remains a long-term challenge for sustainable food production. Microbial pathogens cause devastating diseases, minimizing crop losses by controlling plant diseases can contribute significantly to this goal. All plants possess an innate immune system that is activated after recognition of microbial-derived molecules. The fungal protein Eix induces defense responses in tomato and tobacco. Plants recognize Eix through a leucine-rich-repeat receptor- like-protein (LRR-RLP) termed LeEix. Despite the knowledge obtained from studies on tomato, relatively little is known about signaling initiated by RLP-type immune receptors. The focus of this grant proposal is to generate a foundational understanding of how the tomato xylanase receptor LeEix2 signals to confer defense responses. LeEix2 recognition results in pattern triggered immunity (PTI). The grant has two main aims: (1) Isolate the LeEix2 protein complex in an active and resting state; (2) Examine the biological function of the identified proteins in relation to LeEix2 signaling upon perception of the xylanase elicitor Eix. We used two separate approaches to isolate receptor interacting proteins. Transgenic tomato plants expressing LeEix2 fused to the GFP tag were used to identify complex components at a resting and activated state. LeEix2 complexes were purified by mass spectrometry and associated proteins identified by mass spectrometry. We identified novel proteins that interact with LeEix receptor by proteomics analysis. We identified two dynamin related proteins (DRPs), a coiled coil – nucleotide binding site leucine rich repeat (SlNRC4a) protein. In the second approach we used the split ubiquitin yeast two hybrid (Y2H) screen system to identified receptor-like protein kinase At5g24010-like (SlRLK-like) (Solyc01g094920.2.1) as an interactor of LeEIX2. We examined the role of SlNRC4a in plant immunity. Co-immunoprecipitation demonstrates that SlNRC4a is able to associate with different PRRs. Physiological assays with specific elicitors revealed that SlNRC4a generally alters PRR-mediated responses. SlNRC4a overexpression enhances defense responses while silencing SlNRC4 reduces plant immunity. We propose that SlNRC4a acts as a non-canonical positive regulator of immunity mediated by diverse PRRs. Thus, SlNRC4a could link both intracellular and extracellular immune perception. SlDRP2A localizes at the plasma membrane. Overexpression of SlDRP2A increases the sub-population of LeEIX2 inVHAa1 endosomes, and enhances LeEIX2- and FLS2-mediated defense. The effect of SlDRP2A on induction of plant immunity highlights the importance of endomembrane components and endocytosis in signal propagation during plant immune . The interaction of LeEIX2 with SlRLK-like was verified using co- immunoprecipitation and a bimolecular fluorescence complementation assay. The defence responses induced by EIX were markedly reduced when SlRLK-like was over-expressed, and mutation of slrlk-likeusing CRISPR/Cas9 increased EIX- induced ethylene production and SlACSgene expression in tomato. Co-expression of SlRLK-like with different RLPs and RLKs led to their degradation, apparently through an endoplasmic reticulum-associated degradation process. We provided new knowledge and expertise relevant to expression of specific be exploited to enhance immunity in crops enabling the development of novel environmentally friendly disease control strategies.
5
Blackwell, T. K. C-Myc Protein-Protein and Protein-DNA Interactions: Targets for Therapeutic Intervention. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada371161.
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Blackwell, T. K. C-Myc Protein-Protein and Protein-DNA Interactions: Targets for Therapeutic Intervention. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada344737.
7
Blackwell, T. K. C-MYC Protein-Protein and Protein-DNA Interactions: Targets for Therapeutic Intervention. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada381686.
8
Umland, Timothy C. Cross-Species Virus-Host Protein-Protein Interactions Inhibiting Innate Immunity. Fort Belvoir, VA: Defense Technical Information Center, July 2016. http://dx.doi.org/10.21236/ad1012633.
9
Martin, Shawn Bryan, Kenneth L. Sale, Jean-Loup Michel Faulon, and Diana C. Roe. Developing algorithms for predicting protein-protein interactions of homology modeled proteins. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/883467.
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Christopher, David A., and Avihai Danon. Plant Adaptation to Light Stress: Genetic Regulatory Mechanisms. United States Department of Agriculture, May 2004. http://dx.doi.org/10.32747/2004.7586534.bard.
Анотація:
Original Objectives: 1. Purify and biochemically characterize RB60 orthologs in higher plant chloroplasts; 2. Clone the gene(s) encoding plant RB60 orthologs and determine their structure and expression; 3. Manipulate the expression of RB60; 4. Assay the effects of altered RB60 expression on thylakoid biogenesis and photosynthetic function in plants exposed to different light conditions. In addition, we also examined the gene structure and expression of RB60 orthologs in the non-vascular plant, Physcomitrella patens and cloned the poly(A)-binding protein orthologue (43 kDa RB47-like protein). This protein is believed to a partner that interacts with RB60 to bind to the psbA5' UTR. Thus, to obtain a comprehensive view of RB60 function requires analysis of its biochemical partners such as RB43. Background & Achievements: High levels of sunlight reduce photosynthesis in plants by damaging the photo system II reaction center (PSII) subunits, such as D1 (encoded by the chloroplast tpsbAgene). When the rate of D1 synthesis is less than the rate of photo damage, photo inhibition occurs and plant growth is decreased. Plants use light-activated translation and enhanced psbAmRNA stability to maintain D1 synthesis and replace the photo damaged 01. Despite the importance to photosynthetic capacity, these mechanisms are poorly understood in plants. One intriguing model derived from the algal chloroplast system, Chlamydomonas, implicates the role of three proteins (RB60, RB47, RB38) that bind to the psbAmRNA 5' untranslated leader (5' UTR) in the light to activate translation or enhance mRNA stability. RB60 is the key enzyme, protein D1sulfide isomerase (Pill), that regulates the psbA-RN :Binding proteins (RB's) by way of light-mediated redox potentials generated by the photosystems. However, proteins with these functions have not been described from higher plants. We provided compelling evidence for the existence of RB60, RB47 and RB38 orthologs in the vascular plant, Arabidopsis. Using gel mobility shift, Rnase protection and UV-crosslinking assays, we have shown that a dithiol redox mechanism which resembles a Pill (RB60) activity regulates the interaction of 43- and 30-kDa proteins with a thermolabile stem-loop in the 5' UTR of the psbAmRNA from Arabidopsis. We discovered, in Arabidopsis, the PD1 gene family consists of II members that differ in polypeptide length from 361 to 566 amino acids, presence of signal peptides, KDEL motifs, and the number and positions of thioredoxin domains. PD1's catalyze the reversible formation an disomerization of disulfide bonds necessary for the proper folding, assembly, activity, and secretion of numerous enzymes and structural proteins. PD1's have also evolved novel cellular redox functions, as single enzymes and as subunits of protein complexes in organelles. We provide evidence that at least one Pill is localized to the chloroplast. We have used PDI-specific polyclonal and monoclonal antisera to characterize the PD1 (55 kDa) in the chloroplast that is unevenly distributed between the stroma and pellet (containing membranes, DNA, polysomes, starch), being three-fold more abundant in the pellet phase. PD1-55 levels increase with light intensity and it assembles into a high molecular weight complex of ~230 kDa as determined on native blue gels. In vitro translation of all 11 different Pill's followed by microsomal membrane processing reactions were used to differentiate among PD1's localized in the endoplasmic reticulum or other organelles. These results will provide.1e insights into redox regulatory mechanisms involved in adaptation of the photosynthetic apparatus to light stress. Elucidating the genetic mechanisms and factors regulating chloroplast photosynthetic genes is important for developing strategies to improve photosynthetic efficiency, crop productivity and adaptation to high light environments.