Academic literature on the topic 'Protein – protein interactions (PPI)'
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Journal articles on the topic "Protein – protein interactions (PPI)":
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dissertations / Theses on the topic "Protein – 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.
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.
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.
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, 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.
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.
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.
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.
The 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.
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/.
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.
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.
Books on the topic "Protein – protein interactions (PPI)":
Fu, Haian. Protein-Protein Interactions. New Jersey: Humana Press, 2004. http://dx.doi.org/10.1385/1592597629.
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.
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.
Wendt, Michael D., ed. Protein-Protein Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28965-1.
Kangueane, Pandjassarame. Protein-protein interactions. Hauppauge, N.Y: Nova Science Publisher's, 2010.
Wendt, Michael D. Protein-Protein Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Mukhtar, Shahid, ed. Protein-Protein Interactions. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3327-4.
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.
Ruth, Nussinov, and Schreiber Gideon, eds. Computational protein-protein interactions. Boca Raton: CRC Press/Taylor & Francis, 2009.
Schuck, Peter, ed. Protein Interactions. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-35966-3.
Book chapters on the topic "Protein – 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.
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.
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.
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.
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.
Conference papers on the topic "Protein – 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.
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.
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.
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.
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.
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.
Reports on the topic "Protein – protein interactions (PPI)":
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.
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.
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