Academic literature on the topic 'Protein interactions (PPI)'
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Journal articles on the topic "Protein interactions (PPI)":
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.
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.
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.
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.
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.
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.
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.
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.
Klein, Mark. "Targeting Protein-Protein Interactions to Inhibit Cyclin-Dependent Kinases." Pharmaceuticals 16, no. 4 (March 31, 2023): 519. http://dx.doi.org/10.3390/ph16040519.
Zhang, Jinxiong, Cheng Zhong, Hai Xiang Lin, and Mian Wang. "Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks." BioMed Research International 2019 (August 21, 2019): 1–17. http://dx.doi.org/10.1155/2019/3726721.
Dissertations / Theses on the topic "Protein interactions (PPI)":
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.
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.
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.
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.
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.
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/.
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.
Alleman, Cécile. "Accès synthétique au châssis [5-8-5] de la fusicoccine-A pour la synthèse d’analogues simplifiés en vue d'étudier les interactions protéine-protéine." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENS090.
In biological media, protein-protein interactions (PPI) are of huge importance, as they allow the regulation of many cellular events. PPI classically involve two partners: an adapter protein and its effector protein(s) regulated either in a positive or a negative manner. Inhibition of PPI has thus been considered as a solid therapeutic approach. On the other hand, stabilization of PPI remains scarcely investigated, but may lead to new promising approaches. This project focuses on the 14-3-3 family adapter protein which interacts with more than 200 protein partners. Among them, p53 protein is subjected to a lot of studies as this tumor suppressor protein regulates multiple biological processes (DNA repair, apoptosis). However, those major functions appear to be silenced in most cancer cases, thus allowing tumor cells proliferation. Some studies have shown that stabilization of the 14-3-3/p53 pair with the help of a molecular glue permitted to restore tumor suppressor activity of p53. Among the examined molecular glues, the fusicoccin-A (FC-A) natural product is shown to lodge in the valley formed by 14-3-3 and increases stabilization of the 14-3-3/p53 interaction. In this context, to enlarge the p53/14-3-3 molecular glue library, this project focuses on the access to simplified FC-A analogs through the synthesis of tricyclic scaffold. [6-8-5] analogs from an aromatic substrate are envisaged, as well as [5-8-5] analogs from a cyclopentane derivative, closer to the target structure. Various strategies have been explored in order to access these analogs
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.
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.
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.
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.
Selim, Khaled [Verfasser]. "Structural and Functional Characterization of PII and PII-like Proteins and their Network of Interactions / Khaled Selim." Tübingen : Universitätsbibliothek Tübingen, 2021. http://d-nb.info/1233678507/34.
Books on the topic "Protein interactions (PPI)":
Winter, Sherry Lynn. Genetic and functional characterization of the interaction of BRCA1 with the serine/threonine phosphatase, PP1, and the circadian clock proteins, Per1 and Per2. 2006.
Book chapters on the topic "Protein interactions (PPI)":
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.
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.
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.
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.
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.
Holzinger, Andreas, Anna Saranti, Anne-Christin Hauschild, Jacqueline Beinecke, Dominik Heider, Richard Roettger, Heimo Mueller, Jan Baumbach, and Bastian Pfeifer. "Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning." In Lecture Notes in Computer Science, 45–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40837-3_4.
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.
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.
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.
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.
Conference papers on the topic "Protein interactions (PPI)":
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.
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.
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.
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.
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.
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.
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.
"PREDICTION OF PROTEIN INTERACTIONS ON HIV-1–HUMAN PPI DATA USING A NOVEL CLOSURE-BASED INTEGRATED APPROACH." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003769001640173.
Dimitrakopoulos, Georgios Ν., Konstantinos Lazaros, Aristidis G. Vrahatis, Marios Krokidis, Konstantina Skolariki, Panagiotis Vlamos, and Themis Exarchos. "A Machine Learning approach combining omics data for Alzheimer’s Disease analysis." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.342d.
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.
Reports on the topic "Protein interactions (PPI)":
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.
Sessa, Guido, and Gregory Martin. role of FLS3 and BSK830 in pattern-triggered immunity in tomato. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604270.bard.
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.
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.
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.
Morrison, Mark, Joshuah Miron, Edward A. Bayer, and Raphael Lamed. Molecular Analysis of Cellulosome Organization in Ruminococcus Albus and Fibrobacter Intestinalis for Optimization of Fiber Digestibility in Ruminants. United States Department of Agriculture, March 2004. http://dx.doi.org/10.32747/2004.7586475.bard.
Wolf, Shmuel, and William J. Lucas. Involvement of the TMV-MP in the Control of Carbon Metabolism and Partitioning in Transgenic Plants. United States Department of Agriculture, October 1999. http://dx.doi.org/10.32747/1999.7570560.bard.
Yosef, Carlos M. Production of High Transverse Momentum $\pi^0$ Mesons in Interactions of 530 GeV/c Proton and $\pi^-$ Beams on Beryllium and Copper Targets. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/1426709.
Raghothama, Kashchandra G., Avner Silber, and Avraham Levy. Biotechnology approaches to enhance phosphorus acquisition of tomato plants. United States Department of Agriculture, January 2006. http://dx.doi.org/10.32747/2006.7586546.bard.
Choudhary, Brajesh Chandra. A Study of High Transverse Momentum Direct Photon Production in Interactions of 500 GeV/c $\pi^{-}$ and Proton Beams on a Beryllium Target. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/1372873.