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Journal articles on the topic 'Protein interaction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sun, Zheng, Shihao Li, Fuhua Li, and Jianhai Xiang. "Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/416543.

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WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1) and the constructed transcriptome data ofF. chinensiswere used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp.

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DeBlasio, Stacy L., Juan D. Chavez, Mariko M. Alexander, et al. "Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology." Journal of Virology 90, no. 4 (2015): 1973–87. http://dx.doi.org/10.1128/jvi.01706-15.

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ABSTRACTDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus[PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topologica

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Kulharia, Mahesh. "Geometrical and electro-static determinants of protein-protein interactions." Bioinformation 17, no. 10 (2021): 851–60. http://dx.doi.org/10.6026/97320630017851.

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Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redunda

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Yang, Huiying, Yuehua Ke, Jian Wang, et al. "Insight into Bacterial Virulence Mechanisms against Host Immune Response via the Yersinia pestis-Human Protein-Protein Interaction Network." Infection and Immunity 79, no. 11 (2011): 4413–24. http://dx.doi.org/10.1128/iai.05622-11.

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ABSTRACTAYersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66Y. pestisbait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted byY. pestiswere significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed t

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Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

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The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational

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Lang, Benjamin, Jae-Seong Yang, Mireia Garriga-Canut, et al. "Matrix-screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins." Nucleic Acids Research 49, no. 12 (2021): 6702–21. http://dx.doi.org/10.1093/nar/gkab490.

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Abstract RNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs are often poor predictors of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory possibilities. To probe the potential for complex formation among RBPs, we assembled a library of 978 mammalian RBPs and used rec-Y2H matrix screening to detect direct interactions between RB

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Bitbol, Anne-Florence, Robert S. Dwyer, Lucy J. Colwell, and Ned S. Wingreen. "Inferring interaction partners from protein sequences." Proceedings of the National Academy of Sciences 113, no. 43 (2016): 12180–85. http://dx.doi.org/10.1073/pnas.1606762113.

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Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to pre

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Hu, Yang, Ying Zhang, Jun Ren, Yadong Wang, Zhenzhen Wang, and Jun Zhang. "Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network." BioMed Research International 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5313050.

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The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of hum

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Gursoy, Attila, Ozlem Keskin, and Ruth Nussinov. "Topological properties of protein interaction networks from a structural perspective." Biochemical Society Transactions 36, no. 6 (2008): 1398–403. http://dx.doi.org/10.1042/bst0361398.

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Protein–protein interactions are usually shown as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. The graph abstraction of protein interactions is crucial for understanding the global behaviour of the network. In this mini review, we summarize basic graph topological properties, such as node degree and betweenness, and their relation to essentiality and modularity of protein interactions. The classification of hub proteins into date and party hubs with distinct properties has significant implica

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Wang, Tianwen, Ningning Yang, Chen Liang, et al. "Detecting Protein-Protein Interaction Based on Protein Fragment Complementation Assay." Current Protein & Peptide Science 21, no. 6 (2020): 598–610. http://dx.doi.org/10.2174/1389203721666200213102829.

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Proteins are the most critical executive molecules by responding to the instructions stored in the genetic materials in any form of life. More frequently, proteins do their jobs by acting as a roleplayer that interacts with other protein(s), which is more evident when the function of a protein is examined in the real context of a cell. Identifying the interactions between (or amongst) proteins is very crucial for the biochemistry investigation of an individual protein and for the attempts aiming to draw a holo-picture for the interacting members at the scale of proteomics (or protein-protein i

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Srivastav, Ratnesh K., Susan Schwede, Malte Klaus, Jessica Schwermann, Matthias Gaestel, and Rainer Niedenthal. "Monitoring protein–protein interactions in mammalian cells by trans-SUMOylation." Biochemical Journal 438, no. 3 (2011): 495–503. http://dx.doi.org/10.1042/bj20110035.

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Protein–protein interactions are essential for almost all cellular processes, hence understanding these processes mainly depends on the identification and characterization of the relevant protein–protein interactions. In the present paper, we introduce the concept of TRS (trans-SUMOylation), a new method developed to identify and verify protein–protein interactions in mammalian cells in vivo. TRS utilizes Ubc9-fusion proteins that trans-SUMOylate co-expressed interacting proteins. Using TRS, we analysed interactions of 65 protein pairs co-expressed in HEK (human embryonic kidney)-293 cells. We

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Das, Arundhati, Tanvi Sinha, Sharmishtha Shyamal, and Amaresh Chandra Panda. "Emerging Role of Circular RNA–Protein Interactions." Non-Coding RNA 7, no. 3 (2021): 48. http://dx.doi.org/10.3390/ncrna7030048.

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Circular RNAs (circRNAs) are emerging as novel regulators of gene expression in various biological processes. CircRNAs regulate gene expression by interacting with cellular regulators such as microRNAs and RNA binding proteins (RBPs) to regulate downstream gene expression. The accumulation of high-throughput RNA–protein interaction data revealed the interaction of RBPs with the coding and noncoding RNAs, including recently discovered circRNAs. RBPs are a large family of proteins known to play a critical role in gene expression by modulating RNA splicing, nuclear export, mRNA stability, localiz

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Zhu, Zhaozhong, Yunshi Fan, Yang Liu, Taijiao Jiang, Yang Cao, and Yousong Peng. "Prediction of antiviral drugs against African swine fever viruses based on protein–protein interaction analysis." PeerJ 8 (April 1, 2020): e8855. http://dx.doi.org/10.7717/peerj.8855.

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The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is currently no effective antiviral drug or vaccine against the virus. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein–protein interactions between ASFV and swine were curated by integration of protein–protein interactions from multiple sources. Thirty-eight swine proteins were observed to interact with ASFVs and were defined as ASFV-interacting swine proteins. The ASFV-interacting swine proteins were found to play a central role in the

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Vyncke, Laurens, Delphine Masschaele, Jan Tavernier, and Frank Peelman. "Straightforward Protein-Protein Interaction Interface Mapping via Random Mutagenesis and Mammalian Protein Protein Interaction Trap (MAPPIT)." International Journal of Molecular Sciences 20, no. 9 (2019): 2058. http://dx.doi.org/10.3390/ijms20092058.

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The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associate

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Miang-Lon Ng, Patricia, and Thomas Lufkin. "Embryonic stem cells: protein interaction networks." BioMolecular Concepts 2, no. 1-2 (2011): 13–25. http://dx.doi.org/10.1515/bmc.2011.008.

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AbstractEmbryonic stem cells have the ability to differentiate into nearly all cell types. However, the molecular mechanism of its pluripotency is still unclear. Oct3/4, Sox2 and Nanog are important factors of pluripotency. Oct3/4 (hereafter referred to as Oct4), in particular, has been an irreplaceable factor in the induction of pluripotency in adult cells. Proteins interacting with Oct4 and Nanog have been identified via affinity purification and mass spectrometry. These data, together with iterative purifications of interacting proteins allowed a protein interaction network to be constructe

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Zhao, Shijie, Fahao Li, Wen Li, et al. "Mass Spectrometry−Based Proteomic Analysis of Potential Host Proteins Interacting with N in PRRSV−Infected PAMs." International Journal of Molecular Sciences 25, no. 13 (2024): 7219. http://dx.doi.org/10.3390/ijms25137219.

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One of the most significant diseases in the swine business, porcine reproductive and respiratory syndrome virus (PRRSV) causes respiratory problems in piglets and reproductive failure in sows. The PRRSV nucleocapsid (N) protein is essential for the virus’ assembly, replication, and immune evasion. Stages in the viral replication cycle can be impacted by interactions between the PRRSV nucleocapsid protein and the host protein components. Therefore, it is of great significance to explore the interaction between the PRRSV nucleocapsid protein and the host. Nevertheless, no information has been pu

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17

Cao, Yi, Teri Yoo, Shulin Zhuang, and Hongbin Li. "Protein–Protein Interaction Regulates Proteins’ Mechanical Stability." Journal of Molecular Biology 378, no. 5 (2008): 1132–41. http://dx.doi.org/10.1016/j.jmb.2008.03.046.

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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 (2022): 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.

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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 Fo

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Cho, Sa-Yeon, Sung-Goo Park, Do-Hee Lee, and Byoung-Chul Park. "Protein-protein Interaction Networks: from Interactions to Networks." BMB Reports 37, no. 1 (2004): 45–52. http://dx.doi.org/10.5483/bmbrep.2004.37.1.045.

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Nguyen, Tuan N., and James A. Goodrich. "Protein-protein interaction assays: eliminating false positive interactions." Nature Methods 3, no. 2 (2006): 135–39. http://dx.doi.org/10.1038/nmeth0206-135.

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Younger, David, Stephanie Berger, David Baker, and Eric Klavins. "High-throughput characterization of protein–protein interactions by reprogramming yeast mating." Proceedings of the National Academy of Sciences 114, no. 46 (2017): 12166–71. http://dx.doi.org/10.1073/pnas.1705867114.

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High-throughput methods for screening protein–protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination ofSaccharomyces cerevisiaecan be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship

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LI, MIN, JIAN-XIN WANG, HUAN WANG, and YI PAN. "IDENTIFICATION OF ESSENTIAL PROTEINS FROM WEIGHTED PROTEIN–PROTEIN INTERACTION NETWORKS." Journal of Bioinformatics and Computational Biology 11, no. 03 (2013): 1341002. http://dx.doi.org/10.1142/s0219720013410023.

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Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein–protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. Unfortunately, the protein–protein interactions produced by high-throughput experiments generally have high false positives. Moreover, most of centrality measures based on network topology are sensit

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Hara, Kodai, Masayuki Uchida, Risa Tagata, et al. "Structure of proliferating cell nuclear antigen (PCNA) bound to an APIM peptide reveals the universality of PCNA interaction." Acta Crystallographica Section F Structural Biology Communications 74, no. 4 (2018): 214–21. http://dx.doi.org/10.1107/s2053230x18003242.

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Proliferating cell nuclear antigen (PCNA) provides a molecular platform for numerous protein–protein interactions in DNA metabolism. A large number of proteins associated with PCNA have a well characterized sequence termed the PCNA-interacting protein box motif (PIPM). Another PCNA-interacting sequence termed the AlkB homologue 2 PCNA-interacting motif (APIM), comprising the five consensus residues (K/R)-(F/Y/W)-(L/I/V/A)-(L/I/V/A)-(K/R), has also been identified in various proteins. In contrast to that with PIPM, the PCNA–APIM interaction is less well understood. Here, the crystal structure o

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Usman, Muhammad Syafiuddin, Wisnu Ananta Kusuma, Farit Mochamad Afendi, and Rudi Heryanto. "Identification of Significant Proteins Associated with Diabetes Mellitus Using Network Analysis of Protein-Protein Interactions." Computer Engineering and Applications Journal 8, no. 1 (2019): 41–52. http://dx.doi.org/10.18495/comengapp.v8i1.283.

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Computation approach to identify significance of proteins related with disease was proposed as one of the solutions from the problem of experimental method application which is generally high cost and time consuming. The case of study was conducted on Diabetes Melitus (DM) type 2 diseases. Identification of significant proteins was conducted by constructing protein-protein interactions network and then analyzing the network topology. Dataset was obtained from Online Mendelian Inheritance in Man (OMIM) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) which provided prote

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Cook, Helen, Nadezhda Doncheva, Damian Szklarczyk, Christian von Mering, and Lars Jensen. "Viruses.STRING: A Virus-Host Protein-Protein Interaction Database." Viruses 10, no. 10 (2018): 519. http://dx.doi.org/10.3390/v10100519.

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As viruses continue to pose risks to global health, having a better understanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus–virus and virus–host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.

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Guarnaccia, Alissa, Jennie Lill, and Anwesha Dey. "Abstract 3047: Interrogating TEAD protein interactions in cancer." Cancer Research 84, no. 6_Supplement (2024): 3047. http://dx.doi.org/10.1158/1538-7445.am2024-3047.

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Abstract The Hippo signaling pathway is a major regulator of cellular growth and is frequently deregulated in cancer. The main effectors of the Hippo pathway are the TEAD transcription factors which function by interacting with other co-effector proteins to modulate transcriptional outputs. Notably YAP and TAZ interact with TEAD to mediate oncogenic transcriptional programs, and blocking interaction of TEAD with YAP and TAZ is a sought-after mechanism for small molecule TEAD inhibitors. However, the interaction network of TEAD extends well beyond YAP and TAZ to a variety of additional cofactor

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Botelho, João, Paulo Mascarenhas, José João Mendes, and Vanessa Machado. "Network Protein Interaction in Parkinson’s Disease and Periodontitis Interplay: A Preliminary Bioinformatic Analysis." Genes 11, no. 11 (2020): 1385. http://dx.doi.org/10.3390/genes11111385.

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Recent studies supported a clinical association between Parkinson’s disease (PD) and periodontitis. Hence, investigating possible interactions between proteins associated to these two conditions is of interest. In this study, we conducted a protein–protein network interaction analysis with recognized genes encoding proteins with variants strongly associated with PD and periodontitis. Genes of interest were collected via the Genome-Wide Association Studies (GWAS) database. Then, we conducted a protein interaction analysis, using the Search Tool for the Retrieval of Interacting Genes/Proteins (S

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Arkhipov, Dmitry V., Sergey N. Lomin, Yulia A. Myakushina, Ekaterina M. Savelieva, Dmitry I. Osolodkin, and Georgy A. Romanov. "Modeling of Protein–Protein Interactions in Cytokinin Signal Transduction." International Journal of Molecular Sciences 20, no. 9 (2019): 2096. http://dx.doi.org/10.3390/ijms20092096.

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The signaling of cytokinins (CKs), classical plant hormones, is based on the interaction of proteins that constitute the multistep phosphorelay system (MSP): catalytic receptors—sensor histidine kinases (HKs), phosphotransmitters (HPts), and transcription factors—response regulators (RRs). Any CK receptor was shown to interact in vivo with any of the studied HPts and vice versa. In addition, both of these proteins tend to form a homodimer or a heterodimeric complex with protein-paralog. Our study was aimed at explaining by molecular modeling the observed features of in planta protein–protein i

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Vitour, Damien, Pierre Lindenbaum, Patrice Vende, Michelle M. Becker, and Didier Poncet. "RoXaN, a Novel Cellular Protein Containing TPR, LD, and Zinc Finger Motifs, Forms a Ternary Complex with Eukaryotic Initiation Factor 4G and Rotavirus NSP3." Journal of Virology 78, no. 8 (2004): 3851–62. http://dx.doi.org/10.1128/jvi.78.8.3851-3862.2004.

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ABSTRACT Rotavirus mRNAs are capped but not polyadenylated, and viral proteins are translated by the cellular translation machinery. This is accomplished through the action of the viral nonstructural protein NSP3, which specifically binds the 3′ consensus sequence of viral mRNAs and interacts with the eukaryotic translation initiation factor eIF4G I. To further our understanding of the role of NSP3 in rotavirus replication, we looked for other cellular proteins capable of interacting with this viral protein. Using the yeast two-hybrid assay, we identified a novel cellular protein-binding partn

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Schmitz, Alexa M., Monica F. Morrison, Akochi O. Agunwamba, Max L. Nibert, and Cammie F. Lesser. "Protein interaction platforms: visualization of interacting proteins in yeast." Nature Methods 6, no. 7 (2009): 500–502. http://dx.doi.org/10.1038/nmeth.1337.

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Gurung, Raju, Darlami Om, Rabin Pun, Soonsil Hyun, and Dongyun Shin. "Recent Progress in Modulation of WD40-Repeat Domain 5 Protein (WDR5): Inhibitors and Degraders." Cancers 15, no. 15 (2023): 3910. http://dx.doi.org/10.3390/cancers15153910.

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WD40-repeat (WDR) domain proteins play a crucial role in mediating protein–protein interactions that sustain oncogenesis in human cancers. One prominent example is the interaction between the transcription factor MYC and its chromatin co-factor, WD40-repeat domain protein 5 (WDR5), which is essential for oncogenic processes. The MYC family of proteins is frequently overexpressed in various cancers and has been validated as a promising target for anticancer therapies. The recruitment of MYC to chromatin is facilitated by WDR5, highlighting the significance of their interaction. Consequently, in

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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.

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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 accu

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Jung, Se-Hui, Kangseung Lee, Deok-Hoon Kong, Woo Jin Kim, Young-Myeong Kim, and Kwon-Soo Ha. "Integrative Proteomic Profiling of Protein Activity and Interactions Using Protein Arrays." Molecular & Cellular Proteomics 11, no. 11 (2012): 1167–76. http://dx.doi.org/10.1074/mcp.m112.016964.

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Proteomic studies based on abundance, activity, or interactions have been used to investigate protein functions in normal and pathological processes, but their combinatory approach has not been attempted. We present an integrative proteomic profiling method to measure protein activity and interaction using fluorescence-based protein arrays. We used an on-chip assay to simultaneously monitor the transamidating activity and binding affinity of transglutaminase 2 (TG2) for 16 TG2-related proteins. The results of this assay were compared with confidential scores provided by the STRING database to

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Krishnadev, Oruganty, Shveta Bisht, and Narayanaswamy Srinivasan. "Prediction of Protein-Protein Interactions Between Human Host and Two Mycobacterial Organisms." International Journal of Knowledge Discovery in Bioinformatics 1, no. 1 (2010): 1–13. http://dx.doi.org/10.4018/jkdb.2010100201.

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The genomes of many human pathogens have been sequenced but the protein-protein interactions across a pathogen and human are still poorly understood. The authors apply a simple homology-based method to predict protein-protein interactions between human host and two mycobacterial organisms viz., M.tuberculosis and M.leprae. They focused on secreted proteins of pathogens and cellular membrane proteins to restrict to uncovering biologically significant and feasible interactions. Predicted interactions include five mycobacterial proteins of yet unknown function, thus suggesting a role for these pr

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Stöcker, Bianca, Johannes Köster, Eli Zamir, and Sven Rahmann. "Modeling and Simulating Constrained Protein Interaction Networks." Genomics and Computational Biology 4, no. 1 (2017): 100049. http://dx.doi.org/10.18547/gcb.2018.vol4.iss1.e100049.

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Cellular functions of biochemical reactions are enabled by protein interactions. In addition to the protein interactions themselves, dependencies between these interactions such as allosteric activation or mutual exclusion contribute to the complexity and functionality of these systems. We introduce a model of constrained protein interaction networks that uses propositional logic to combine protein networks with interaction dependencies. Further, we present an efficient model, enabling a fast simulation and analysis of many proteins in large networks. This allows to simulate perturbation effec

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ESPEJO, Alexsandra, Jocelyn CÔTÉ, Andrzej BEDNAREK, Stephane RICHARD, and Mark T. BEDFORD. "A protein-domain microarray identifies novel protein–protein interactions." Biochemical Journal 367, no. 3 (2002): 697–702. http://dx.doi.org/10.1042/bj20020860.

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Protein domains mediate protein—protein interactions through binding to short peptide motifs in their corresponding ligands. These peptide recognition modules are critical for the assembly of multiprotein complexes. We have arrayed glutathione S-transferase (GST) fusion proteins, with a focus on protein interaction domains, on to nitrocellulose-coated glass slides to generate a protein-domain chip. Arrayed protein-interacting modules included WW (a domain with two conserved tryptophans), SH3 (Src homology 3), SH2, 14.3.3, FHA (forkhead-associated), PDZ (a domain originally identified in PSD-95

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Meyer, Katrina, and Matthias Selbach. "Peptide-based Interaction Proteomics." Molecular & Cellular Proteomics 19, no. 7 (2020): 1070–75. http://dx.doi.org/10.1074/mcp.r120.002034.

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Protein-protein interactions are often mediated by short linear motifs (SLiMs) that are located in intrinsically disordered regions (IDRs) of proteins. Interactions mediated by SLiMs are notoriously difficult to study, and many functionally relevant interactions likely remain to be uncovered. Recently, pull-downs with synthetic peptides in combination with quantitative mass spectrometry emerged as a powerful screening approach to study protein-protein interactions mediated by SLiMs. Specifically, arrays of synthetic peptides immobilized on cellulose membranes provide a scalable means to identi

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Ward, Katrisa M., Brandon D. Pickett, Mark T. W. Ebbert, John S. K. Kauwe, and Justin B. Miller. "Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins." Genes 13, no. 8 (2022): 1346. http://dx.doi.org/10.3390/genes13081346.

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Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and

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Wong, Eric T. C., Victor So, Mike Guron, et al. "Protein–Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations." Biomolecules 10, no. 8 (2020): 1097. http://dx.doi.org/10.3390/biom10081097.

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Because proteins are fundamental to most biological processes, many genetic diseases can be traced back to single nucleotide variants (SNVs) that cause changes in protein sequences. However, not all SNVs that result in amino acid substitutions cause disease as each residue is under different structural and functional constraints. Influential studies have shown that protein–protein interaction interfaces are enriched in disease-associated SNVs and depleted in SNVs that are common in the general population. These studies focus primarily on folded (globular) protein domains and overlook the preva

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HAYASHI, YOSHIHARU, MIME KOBAYASHI, KATSUYOSHI SAKAGUCHI, et al. "PROTEIN CLASSIFICATION USING COMPARATIVE MOLECULAR INTERACTION PROFILE ANALYSIS SYSTEM." Journal of Bioinformatics and Computational Biology 02, no. 03 (2004): 497–510. http://dx.doi.org/10.1142/s0219720004000703.

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We recently introduced a new molecular description factor, interaction profile Factor (IPF) that is useful for evaluating molecular interactions. IPF is a data set of interaction energies calculated by the Comparative Molecular Interaction Profile Analysis system (CoMIPA). CoMIPA utilizes AutoDock 3.0 docking program, and the system has shown to be a powerful tool in clustering the interacting properties between small molecules and proteins. In this report, we describe the application of CoMIPA for protein clustering. A sample set of 15 proteins that share less than 20% homology and have no co

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Geng, Haijiang, Tao Lu, Xiao Lin, Yu Liu, and Fangrong Yan. "Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier." Biochemistry Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/978193.

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Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181

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Gómez, Antonio, Sergio Hernández, Isaac Amela, Jaume Piñol, Juan Cedano, and Enrique Querol. "Do protein–protein interaction databases identify moonlighting proteins?" Molecular BioSystems 7, no. 8 (2011): 2379. http://dx.doi.org/10.1039/c1mb05180f.

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Lin, Hening, and Virginia W. Cornish. "In Vivo Protein-Protein Interaction Assays: Beyond Proteins." Angewandte Chemie International Edition 40, no. 5 (2001): 871–75. http://dx.doi.org/10.1002/1521-3773(20010302)40:5<871::aid-anie871>3.0.co;2-s.

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Wang, Derui, and Jingyu Hou. "Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions." Journal of Bioinformatics and Computational Biology 13, no. 05 (2015): 1550026. http://dx.doi.org/10.1142/s0219720015500262.

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Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this

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Sánchez Claros, Carmen, and Anna Tramontano. "Detecting Mutually Exclusive Interactions in Protein-Protein Interaction Maps." PLoS ONE 7, no. 6 (2012): e38765. http://dx.doi.org/10.1371/journal.pone.0038765.

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Robertson, David L., and Simon C. Lovell. "Evolution in protein interaction networks: co-evolution, rewiring and the role of duplication." Biochemical Society Transactions 37, no. 4 (2009): 768–71. http://dx.doi.org/10.1042/bst0370768.

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Molecular function is the result of proteins working together, mediated by highly specific interactions. Maintenance and change of protein interactions can thus be considered one of the main links between molecular function and mutation. As a consequence, protein interaction datasets can be used to study functional evolution directly. In terms of constraining change, the co-evolution of interacting molecules is a very subtle process. This has implications for the signal being used to predict protein–protein interactions. In terms of functional change, the ‘rewiring’ of interaction networks, ge

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Elumalai, Elakkiya, and Suresh Kumar Muthuvel. "Network Analysis of Dengue NS1 Interacting Core Human Proteins Driving Dengue Pathogenesis." Current Chemical Biology 15, no. 4 (2021): 287–300. http://dx.doi.org/10.2174/2212796816666211216115753.

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Aim: We aimed to identify critical human proteins involved in cathepsin L regulation Background: It has been shown that Dengue Virus (DENV) NS1 activates cathepsin L (CTSL). The CTSL activates heparanase, which cleaves heparan sulfate proteoglycans and causes dengue pathogenesis. NS1 directly interacts with PTBP1 and Gab proteins. Gab protein activates the Ras signaling pathway. Still, no known direct interaction partners are linking GAB1 to cathepsin L. Objective: Our objective includes three main points.1-Network analysis of NS1 interacting human proteins 2- Identification of protein-drug an

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Lee, Bong-Jin. "S2c2-1 Structure and Protein-Protein Interaction of Helicobacter Pylori Proteins(S2-c2: "Structural biology reveals macromolecular interaction",Symposia,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S127. http://dx.doi.org/10.2142/biophys.46.s127_4.

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Sardiu, Mihaela E., and Michael P. Washburn. "Building Protein-Protein Interaction Networks with Proteomics and Informatics Tools." Journal of Biological Chemistry 286, no. 27 (2011): 23645–51. http://dx.doi.org/10.1074/jbc.r110.174052.

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The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, w

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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 (2008): 435–66. http://dx.doi.org/10.1142/s0219720008003497.

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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

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