Log in

Relevant bibliographies by topics / Protein discovery

Academic literature on the topic 'Protein discovery'

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Protein discovery.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Protein discovery"

1

Cheng, Miaomiao, Lizhen Liu, Hanshi Wang, Chao Du, and Wei Song. "Essential Proteins Discovery from Weighted Protein–Protein Interaction Networks." Journal of Bionanoscience 8, no. 4 (August 1, 2014): 293–97. http://dx.doi.org/10.1166/jbns.2014.1239.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Oláh, Judit, Tibor Szénási, Attila Lehotzky, Victor Norris, and Judit Ovádi. "Challenges in Discovering Drugs That Target the Protein–Protein Interactions of Disordered Proteins." International Journal of Molecular Sciences 23, no. 3 (January 28, 2022): 1550. http://dx.doi.org/10.3390/ijms23031550.

Full text

Abstract:

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

APA, Harvard, Vancouver, ISO, and other styles

3

Li, Meijing, Tsendsuren Munkhdalai, Xiuming Yu, and Keun Ho Ryu. "A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/942435.

Full text

Abstract:

Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

APA, Harvard, Vancouver, ISO, and other styles

4

Fischer, P. "Protein-Protein Interactions in Drug Discovery." Drug Design Reviews - Online 2, no. 3 (May 1, 2005): 179–207. http://dx.doi.org/10.2174/1567269053828837.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Llabrés, Mercè, and Gabriel Valiente. "Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2." PLOS ONE 15, no. 12 (December 7, 2020): e0236304. http://dx.doi.org/10.1371/journal.pone.0236304.

Full text

Abstract:

Motivation Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. Results We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

APA, Harvard, Vancouver, ISO, and other styles

6

Huston, James S. "Antibody discovery and the arrow of time." Protein Engineering, Design and Selection 31, no. 7-8 (July 1, 2018): 231–32. http://dx.doi.org/10.1093/protein/gzy026.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Zhu, LingZhi, Junling Zhang, Lingya He, Jun Wang, Zhenwu Peng, and Zixin Jian. "Essential Proteins Discovery Methods based on the Protein-Protein Interaction Networks." American Journal of Biochemistry and Biotechnology 13, no. 4 (April 1, 2017): 242–51. http://dx.doi.org/10.3844/ajbbsp.2017.242.251.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

B, Joshi, Boraste A, Khairnar Y, Vamsi KK, Jhadav A, Patil P, Trivedi S, et al. "Protein Based Drug Discovery." International Journal of Drug Discovery 1, no. 2 (December 30, 2009): 40–51. http://dx.doi.org/10.9735/0975-4423.1.2.40-51.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Perry, Sarah. "Protein discovery goes global." Nature Methods 12, S1 (September 10, 2015): 19. http://dx.doi.org/10.1038/nmeth.3534.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Stein, Richard A. "Protein-Specific Discovery Strategies." Genetic Engineering & Biotechnology News 34, no. 6 (March 15, 2014): 1, 12, 13, 15. http://dx.doi.org/10.1089/gen.34.06.01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Protein discovery"

1

Tjernberg, Agneta. "Protein mass spectrometry in the drug discovery process /." Stockholm, 2005. http://diss.kib.ki.se/2005/91-7140-251-9/.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Samuel, Jarvie John. "Elicitation of Protein-Protein Interactions from Biomedical Literature Using Association Rule Discovery." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30508/.

Full text

Abstract:

Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction.

APA, Harvard, Vancouver, ISO, and other styles

3

Álvarez, García Daniel. "Protein solvation preferences: applications to drug discovery." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/285451.

Full text

Abstract:

Computer-aided drug design is a key player in current drug discovery projects. Structure-based computational approaches use the target structural information to suggest potentially active and safe drugs. However, the process is far from trivial and novel methodologies are continuously sought to address two main factors usually simplified and overlooked: Target flexibility and the effect and structure of water molecules at the binding site. As demonstrated by different NMR and crystallography experiments, small organic solvents (e.g. ethanol, isopropanol, acetonitrile) are able to identify binding sites and provide clues for rational drug design. MDmix is a simulation-based method that exploits this natural behavior in silico. By using small organic molecules and water mixtures, each one with a distinct chemical nature, key interaction spots are identified on the protein surface allowing the identification and characterization of binding sites for hit discovery and lead optimization. The work presented in this thesis is divided in two main publications: In the first one, the effect of target flexibility was investigated to establish some guidelines on how to treat this important factor during the simulations. We found that flexibility is essential for correctly identifying induced binding sites but might lead to uninterpretable results when large conformational changes occur. Soft restraints applied during the simulation are suggested as a way to obtain reproducible results and still characterize high affinity interaction sites (hot spots) with mild errors on the energy estimates. In the second publication, the use of solvent mixtures for the identification of experimentally known pharmacophores was evaluated in two test systems for which many inhibitors are known (e.g. heat shock protein 90 and HIV protease 1). The explicit treatment of water molecules provides interaction maps which identify the most favorable interactions in the binding site with unprecedented accuracy when compared to classical molecular interaction potentials. Moreover, we demonstrate how the interaction maps obtained for the water molecules accompanying the small organic solvents are useful to identify non-displaceable waters. Both the solvent interaction maps and the water interaction maps are extremely useful information for the identification of novel active molecules and for the optimization of potency for already active ones. Finally, a software package is presented that aims at facilitating the use of the methodology and at helping in adopting it to everyday drug design projects. A final chapter treats ongoing and future research where method improvements and practical uses in real examples are discussed. MDmix being a simulation-based method, the target flexibility and the explicit treatment of the solvent provide significant advantages over traditional approaches for binding site finding and characterization. This novel approach, which is applicable to previously unmet targets and binding sites, offers a new alternative in the challenging process of drug design.
El diseño de fármacos asistido por ordenador es actualmente un actor fundamental en el proceso de descubrimiento de nuevos fármacos. Las aproximaciones basadas en estructura usan la información estructural de la Diana terapéutica para proponer moléculas activas y seguras. Sin embargo, el proceso dista de ser sencillo y nuevas metodologías están continuamente siendo investigadas para solventar las limitaciones actuales, siendo la flexibilidad de la diana y el tratamiento y la estructura del agua en la cavidad, dos factores usualmente obviados o simplificados. Como ha sido demostrado por varios experimentos de NMR y cristalografía, moléculas pequeñas de solventes orgánicos (p.e. etanol, acetamida o acetonitrilo), son capaces de identificar sitios de unión y proporcionan pistas para el diseño racional de nuevas moléculas bioactivas. MDmix es un método basado en simulación molecular que explota dicho fenómeno in silico. Usando mezclas de moléculas orgánicas pequeñas y agua, cada una con propiedades químicas diferentes, se identifican mapas energéticos de interacción sobre la superficie de la diana. Esta información nos permite identificar sitios de unión para ligandos y caracterizar dicha interacción para guiar el proceso de identificación de hits y la optimización de cabezas de serie. El trabajo presentado en esta tesis se puede dividir en dos publicaciones principales: En la primera, el efecto de la flexibilidad de la diana es estudiado para establecer unas guías de actuación a la hora de simular el sistema. Encontramos que la flexibilidad es fundamental a la hora de identificar cavidades inducidas o con alto grado de flexibilidad pero, a la vez, la interpretación de los resultados es mucho más compleja cuando hay cambios conformacionales. Por otra banda, aplicando restricciones suaves a la movilidad de los átomos, se gana reproducibilidad en los resultados y los errores en la estimación energética son mínimos. En la segunda publicación, se estudió el uso de diferentes mezclas de solventes para la identificación de farmacóforos experimentales en dos sistemas test (heat shock protein 90 y HIV proteasa 1). El tratamiento explícito del agua proporciona mapas energéticos capaces de identificar correctamente los puntos de interacción más favorables con una precisión sin precedentes cuando se compara con otros métodos. Además, demostramos como los mapas energéticos obtenidos para las moléculas de agua son capaces de discernir entre aguas desplazables y no desplazables por un potencial ligando. La información extraída de dichos mapas puede ser de alta utilidad para guiar la identificación de nuevas moléculas activas y para la optimización de la potencia de ligandos ya identificados. Finalmente, se presenta un programa de código abierto escrito en python cuyo objetivo es facilitar el uso de la metodología así como su adopción en cualquier proyecto de diseño de fármacos. En el capítulo final se discuten posibles mejoras y aplicaciones prácticas del método en proyectos actualmente en investigación y direcciones futuras a seguir. MDmix, siendo un método basado en simulación molecular, permite incorporar la flexibilidad de la diana y tratar explícitamente el efecto del solvente. Por ello, ofrece ventajas significativas sobre aproximaciones tradicionales en la identificación de sitios de unión y su caracterización. Siendo aplicable sobre cualquier diana, aún sin conocimiento previo, ofrece una nueva alternativa en el siempre desafiante proceso del diseño de fármacos.

APA, Harvard, Vancouver, ISO, and other styles

4

Steeg, Evan W. "Automated motif discovery in protein structure prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq27733.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Huan, Jun Wang Wei. "Graph based pattern discovery in protein structures." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,583.

Full text

Abstract:

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.
Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science." Discipline: Computer Science; Department/School: Computer Science.

APA, Harvard, Vancouver, ISO, and other styles

6

Levin, Yishai. "Discovery of protein disease biomarkers for schizophrenia." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608745.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

au, ngiles@anhb uwa edu, and Natalie Giles. "Exploitation of the Protein Tubulin For Controlling African Trypanosomiasis." Murdoch University, 2005. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20060315.191003.

Full text

Abstract:

This thesis presents the results of an investigation into the structural protein, tubulin, as a potential target for anti-trypanosomatid drug discovery and vaccine development. Recombinant alpha- and beta- tubulin proteins from Trypanosoma brucei rhodesiense were expressed as soluble fusion proteins in an E. coli expression system. The recombinant alpha- and beta- tubulins were used to determine the nature of binding of novel trifluralin analogues EPL-AJ 1003, 1007, 1008, 1016 and 1017. Native tubulin from rats was used to determine the extent of binding to mammalian tubulin. The results of this study clearly demonstrate two important aspects of the binding of trifluralins to tubulin. Firstly, they have specific affinity for trypanosomal tubulin compared with mammalian regardless of the chemical composition of the trifluralin analogue tested. Secondly, they have a demonstrably stronger affinity for alpha-tubulin compared with beta-tubulin. In addition, compounds 1007, 1008, 1016 and 1017 have strong binding affinities for alpha-tubulin, with limited binding affinity for mammalian tubulin, which indicates that these compounds selectively bind to trypanosomal tubulin. The morphology of bloodstream forms of T. b. rhodesiense exposed to trifluralin analogues was studied using electron microscopy and immunofluorescence to determine the ultrastructural changes these compounds induce as a result of binding to tubulin. All compounds tested induced severe irreparable damage in T. b. rhodesiense, including perturbation of subpellicular microtubules, extensive cytoplasmic swellings, axoneme and paraflagellar rod malformation, disconfiguration around the flagellar pocket and membrane disintegration. These results suggest that the mechanism of action of these trifluralin analogues is through the disruption of polymerization of tubulin into microtubules as a result of binding to alpha-tubulin. The potential for recombinant trypanosomal tubulins to be used as vaccine candidates was assessed by monitoring parasitaemia and length of survival of mice immunised with the proteins and challenged with a lethal infection of T. b. rhodesiense. Although all the mice vaccinated with recombinant tubulin developed a patent parasitaemia and did not survive, they were partially protected because their patency period and length of survival were significantly greater than the control groups. Furthermore, plasma collected from mice immunised with recombinant trypanosomal tubulin contained antibodies that recognized tubulin in a soluble extraction from T. b. rhodesiense. The results of this thesis confirm the potential for the structural protein, tubulin, to be used as a target for anti-trypanosomatid drug discovery and vaccine development.

APA, Harvard, Vancouver, ISO, and other styles

8

Harrison, Benjamin J. "Discovery and characterisation of novel protein interactions with death associated protein kinase." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/29795.

Full text

Abstract:

Combinatorial peptide libraries displayed on M13 filamentous bacteriophage were used to identify peptide consensus binding sites for the kinase domain of DAPK. Peptides that bound to the DAPK core kinase domain were then isolated and sequenced leading to the discovery of binding peptides with striking homology to the SK1-4 family of transcription factors, the Promyelocytic Leukemia protein (PML) and the microtubule associating protein MAP1B. Cell growth and viability assays demonstrated that MAP1B co-operates with DAPK to reduce cell proliferation. This co-operative cell growth inhibition was independent of the p53 pathway and apoptotic (Type 1) cell death, but induced autophagic (Type II) cell death. MAP1B cooperation with DAPK was marked by a striking increase in the number of cells with membrane blebbing morphology, an effect previously shown to involve DAPK interaction with the actin cytoskeleton leading to actin-myosin contraction. This was in contrast to the known role of MAP1B that is primarily thought of as a tubulin associating protein that modifies microtubule dynamics. Therefore the role of the cytoskeleton in DAPK co-operation with MAP1B was studied in detail using immunoflurorescent cytoskeleton staining and microtubule purification assays. During DAPK transfection induced membrane blebbing, a pool of DAPK and MAP1B co-localise and co-purify with tubulin where as a separate pool is co-located to cortical actin. This DAPK and MAP1B cooperation-induced membrane blebbing involves a novel interaction with both microtubules and microfilaments. These studies highlight the utility of peptide combinatorial libraries to identify novel binding interfaces and highlight a positive role for MAP1B in DAPK dependent cytoskeletal rearrangement and the autophagic cell death program.

APA, Harvard, Vancouver, ISO, and other styles

9

Burslem, George McEwan. "An integrated approach to the discovery of inhibitors of protein-protein interactions." Thesis, University of Leeds, 2015. http://etheses.whiterose.ac.uk/9348/.

Full text

Abstract:

Protein-protein interactions present challenging targets for therapeutic intervention with enormous potential for modulating biological pathways, particularly in the field of oncology. Two α-helix mediated protein-protein interactions of interest, hypoxia inducible factor-1α (HIF-1α)/p300 and eukaryotic initiation factor 4E (eIF4E) /eIF4G are introduced and their known inhibitors discussed in Chapter 1. Initially, biophysical assays for both interactions were developed and the binding requirements between peptides derived from helix donor components (HIF-1α and eIF4G) to their protein counterparts (p300 and eIF4E respectively) were investigated. This information was used to develop competition assays capable of identifying inhibitors and provided important insight for the rational design of inhibitors. Subsequently, a computational approach, described in Chapter 3, to inhibitor discovery was applied to both targets, using both docking and pharmacophore modelling. Several series of compounds were purchased or prepared and screened as inhibitors. The development of a synthetic route to a novel scaffold is described providing a weak small molecule inhibitor. In parallel, a proteomimetic approach to inhibitor design was employed, using sequence based rational design, drawing on the knowledge gained in Chapter 2. By mimicking a key helical region of HIF-1α, the interaction with p300 can be disrupted, as discussed in Chapter 4. Additionally, new methods for the preparation of oligobenzamide helix mimetics were investigated allowing the preparation of challenging targets, late stage functionalization and the preparation of oligobenzamide/peptide hybrids. Overall, this thesis provides an introduction to two therapeutically relevant interactions, provides biophysical assays for the identification of inhibitors and discloses the first biophysically characterised inhibitors of the HIF-1α/p300 interaction.

APA, Harvard, Vancouver, ISO, and other styles

10

Stanta, Johannes Lukas. "Discovery of protein and glycan biomarkers in schizophrenia." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608882.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Protein discovery"

1

E, Babine R., and Abdel-Meguid S. S, eds. Protein crystallography in drug discovery. Weinheim: Wiley-VCH, 2004.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Zhang, Jian, and Ruth Nussinov, eds. Protein Allostery in Drug Discovery. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8719-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Giralt, Ernest, Mark Peczuh, and Xavier Salvatella. Protein surface recognition: Approaches for drug discovery. Chichester, West Sussex: John Wiley & Sons, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

G protein-coupled receptors in drug discovery. New York: Humana Press, 2009.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Methods for the discovery and characterization of G protein-coupled receptors. New York: Humana Press, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Steeg, Evan W. Automated motif discovery in protein structure prediction. Toronto: University of Toronto, Dept. of Computer Science, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Filizola, Marta, ed. G Protein-Coupled Receptors in Drug Discovery. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2914-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Leifert, Wayne R., ed. G Protein-Coupled Receptors in Drug Discovery. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-317-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Protein and peptide mass spectrometry in drug discovery. Hoboken, N.J: Wiley, 2012.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Gross, Michael L., Guodong Chen, and Birendra N. Pramanik, eds. Protein and Peptide Mass Spectrometry in Drug Discovery. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118116555.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Protein discovery"

1

Kangueane, Pandjassarame. "Protein-Protein Interaction." In Bioinformation Discovery, 95–106. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95327-4_4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Delahunty, Claire M., and John R. Yates. "Protein-Protein Interactions." In Proteomics for Biological Discovery, 125–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119081661.ch5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Kangueane, Pandjassarame. "Protein Subunits Interaction." In Bioinformation Discovery, 79–86. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0519-2_4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Drabovich, Andrei P., Eduardo Martínez-Morillo, and Eleftherios P. Diamandis. "Protein Biomarker Discovery." In Proteomics for Biological Discovery, 63–88. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119081661.ch3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ritchie, David W. "Chapter 3. Modeling Protein–Protein Interactions by Rigid-body Docking." In Drug Discovery, 56–86. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849733403-00056.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

López, David J., Rafael Álvarez, and Pablo V. Escribá. "Chapter 7. Lipid–Protein Interactions in G Protein Signal Transduction." In Drug Discovery, 153–78. Cambridge: Royal Society of Chemistry, 2011. http://dx.doi.org/10.1039/9781849733441-00153.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Reinhard-Rupp, J., and G. Wess. "Drug Discovery Opportunities." In Small Molecule — Protein Interactions, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05314-0_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Baptista, Cassio Da Silva, and David J. Munroe. "Protein Microarrays." In Proteomics for Biological Discovery, 187–204. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470007745.ch10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Festa, Fernanda, and Joshua LaBaer. "Protein Microarrays." In Proteomics for Biological Discovery, 29–61. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119081661.ch2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Bettinetti, Laura, Matteo Magnani, and Alessandro Padova. "Drug Discovery by Targeting Protein–Protein Interactions." In Disruption of Protein-Protein Interfaces, 1–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37999-4_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Protein discovery"

1

Meng, Tao, Mei-Ling Shyu, and Hua Zhang. "Automatic Discovery of Bioluminescent Proteins from Large Protein Databases." In 2013 IEEE Seventh International Conference on Semantic Computing (ICSC). IEEE, 2013. http://dx.doi.org/10.1109/icsc.2013.67.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Zhang, Hong, Yun Xu, and Yuzhong Zhao. "Discovery of Motif Pairs from Protein-Protein Interaction Networks." In 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing. IEEE, 2009. http://dx.doi.org/10.1109/ijcbs.2009.21.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Zhu, W., X. Lin, X. Hu, and B. A. Sokhansanj. "Visualization of protein-protein interaction network for knowledge discovery." In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547307.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Li, Peipei, Lyong Heo, Meijing Li, Keun Ho Ryu, and Gouchol Pok. "Protein function prediction using frequent patterns in protein-protein interaction networks." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019850.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Liu, Juan, Bin Liu, and Deyi Li. "Discovering Protein Complexes from Protein-Protein Interaction Data by Local Cluster Detecting Algorithm." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.257.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Stanberry, Larissa, Yuan Liu, Bhanu Rekepalli, Paul Giblock, Roger Higdon, and William Broomall. "High performance computing workflow for protein functional annotation." In XSEDE '13: Extreme Science and Engineering Discovery Environment: Gateway to Discovery. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2484762.2484809.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Waterson, Alex G., James D. Patrone, J. Phillip Kennedy, Nicholas F. Pelz, Andreas O. Frank, Bhavatarini Vandgamudi, Michael D. Feldkamp, et al. "Abstract 2473: Fragment-based discovery of inhibitors of replication protein A protein-protein interactions." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-2473.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Yang, Xulei, Qing Da, Peisheng Qian, Bharadwaj Veeravalli, Tam Wai Leong, Lingyun Dai, Par Nordlund, Nayana Prabhu, Ziyuan Zhao, and Zeng Zeng. "CETSA Feature Based Clustering for Protein Outlier Discovery by Protein-to-Protein Interaction Prediction." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871558.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Zhang, Fan, and Jianjun Hu. "Bayesian Classifier for Anchored Protein Sorting Discovery." In 2009 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2009. http://dx.doi.org/10.1109/bibm.2009.43.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Cai, Lu, Shen Qin, and Zhiyong Pei. "Novel hub protein classification and interaction rules in protein-protein interaction network in Saccharomyces cerevisia." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019657.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Protein discovery"

1

Yang, Dejun. Structure-Based Discovery of Novel Inhibitors of Protein Kinase. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada424718.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Zhou, C., and A. Zemla. Computational biology for target discovery and characterization: a feasibility study in protein-protein interaction detection. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/948981.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Seeholzer, Steven. Discovery of Protein Markers in Breast Cancer by Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, May 2001. http://dx.doi.org/10.21236/ada395453.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Seeholzer, Steven H. Discovery of Protein Markers in Breast Cancer by Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada406377.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Seeholzer, Stephen H. Discovery of Protein Markers in Breast Cancer by Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada417070.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Gershoni, Jonathan M., David E. Swayne, Tal Pupko, Shimon Perk, Alexander Panshin, Avishai Lublin, and Natalia Golander. Discovery and reconstitution of cross-reactive vaccine targets for H5 and H9 avian influenza. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7699854.bard.

Full text

Abstract:

Research objectives: Identification of highly conserved B-cell epitopes common to either H5 or H9 subtypes of AI Reconstruction of conserved epitopes from (1) as recombinantimmunogens, and testing their suitability to be used as universal vaccine components by measuring their binding to Influenza vaccinated sera of birds Vaccination of chickens with reconstituted epitopes and evaluation of successful vaccination, clinical protection and viral replication Development of a platform to investigate the dynamics of immune response towards infection or an epitope based vaccine Estimate our ability to focus the immune response towards an epitope-based vaccine using the tool we have developed in (D) Summary: This study is a multi-disciplinary study of four-way collaboration; The SERPL, USDA, Kimron-Israel, and two groups at TAU with the purpose of evaluating the production and implementation of epitope based vaccines against avian influenza (AI). Systematic analysis of the influenza viral spike led to the production of a highly conserved epitope situated at the hinge of the HA antigen designated “cluster 300” (c300). This epitope consists of a total of 31 residues and was initially expressed as a fusion protein of the Protein 8 major protein of the bacteriophagefd. Two versions of the c300 were produced to correspond to the H5 and H9 antigens respectively as well as scrambled versions that were identical with regard to amino acid composition yet with varied linear sequence (these served as negative controls). The recombinantimmunogens were produced first as phage fusions and then subsequently as fusions with maltose binding protein (MBP) or glutathioneS-transferase (GST). The latter were used to immunize and boost chickens at SERPL and Kimron. Furthermore, vaccinated and control chickens were challenged with concordant influenza strains at Kimron and SEPRL. Polyclonal sera were obtained for further analyses at TAU and computational bioinformatics analyses in collaboration with Prof. Pupko. Moreover, the degree of protection afforded by the vaccination was determined. Unfortunately, no protection could be demonstrated. In parallel to the main theme of the study, the TAU team (Gershoni and Pupko) designed and developed a novel methodology for the systematic analysis of the antibody composition of polyclonal sera (Deep Panning) which is essential for the analyses of the humoral response towards vaccination and challenge. Deep Panning is currently being used to monitor the polyclonal sera derived from the vaccination studies conducted at the SEPRL and Kimron.

APA, Harvard, Vancouver, ISO, and other styles

7

Smith, Margaret, Nurit Katzir, Susan McCouch, and Yaakov Tadmor. Discovery and Transfer of Genes from Wild Zea Germplasm to Improve Grain Oil and Protein Composition of Temperate Maize. United States Department of Agriculture, 1998. http://dx.doi.org/10.32747/1998.7580683.bard.

Full text

Abstract:

Project Objectives 1. Develop and amplify two interspecific populations (annual and perennial teosintes x elite maize inbred) as the basis for genetic analysis of grain quality. 2. Identify quantitative trait loci (QTLs) from teosinte that improve oil, protein, and essential amino acid composition of maize grain. 3. Develop near isogenic lines (NILs) to quantify QTL contributions to grain quality and as a resource for future breeding and gene cloning efforts. 4. Analyze the contribution of these QTLs to hybrid performance in both the US and Israel. 5. Measure the yield potential of improved grain quality hybrids. (NOTE: Yield potential could not be evaluated due to environmentally-caused failure of the breeding nursery where seed was produced for this evaluation.) Background: Maize is a significant agricultural commodity worldwide. As an open pollinated crop, variation within the species is large and, in most cases, sufficient to supply the demand for modem varieties and for new environments. In recent years there is a growing demand for maize varieties with special quality attributes. While domesticated sources of genetic variation for high oil and protein content are limited, useful alleles for these traits may remain in maize's wild relative, teosinte. We utilized advanced backcross (AB) analysis to search for QTLs contributing to oil and protein content from two teosinte accessions: Zea mays ssp. mexicana Race Chalco, an annual teosinte (referred to as Chalco), and Z diploperennis Race San Miguel, a perennial teosinte (referred to as Diplo). Major Conclusions and Achievements Two NILs targeting a Diplo introgression in bin 1.04 showed a significant increase in oil content in homozygous sib-pollinated seed when compared to sibbed seed of their counterpart non-introgressed controls. These BC4S2 NILs, referred to as D-RD29 and D-RD30, carry the Diplo allele in bin 1.04 and the introgression extends partially into bins 1.03 and 1.05. These NILs remain heterozygous in bins 4.01 and 8.02, but otherwise are homozygous for the recurrent parent (RD6502) alleles. NILs were developed also for the Chalco introgression in bin 1.04 but these do not show any improvement in oil content, suggesting that the Chalco alleles differ from the Diplo alleles in this region. Testcross Fl seed and sibbed grain from these Fl plants did not show any effect on oil content from this introgression, suggesting that it would need to be present in both parents of a maize hybrid to have an effect on oil content. Implications, both Scientific and Agricultural The Diplo region identified increases oil content by 12.5% (from 4.8% to 5.4% oil in the seed). Although this absolute difference is not large in agronomic terms, this locus could provide additive increases to oil content in combination with other maize-derived loci for high oil. To our knowledge, this is the first confirmed report of a QTL from teosinte for improved grain oil content in maize. It suggests that further research on grain quality alleles from maize wild relatives would be of both scientific and agricultural interest.

APA, Harvard, Vancouver, ISO, and other styles

8

Smith, Margaret, Nurit Katzir, Susan McCouch, and Yaakov Tadmor. Discovery and Transfer of Genes from Wild Zea Germplasm to Improve Grain Oil and Protein Composition of Temperate Maize. United States Department of Agriculture, October 2002. http://dx.doi.org/10.32747/2002.7695846.bard.

Full text

Abstract:

Project Objectives 1. Develop and amplify two interspecific populations (annual and perennial teosintes x elite maize inbred) as the basis for genetic analysis of grain quality. 2. Identify quantitative trait loci (QTLs) from teosinte that improve oil, protein, and essential amino acid composition of maize grain. 3. Develop near isogenic lines (NILs) to quantify QTL contributions to grain quality and as a resource for future breeding and gene cloning efforts. 4. Analyze the contribution of these QTLs to hybrid performance in both the US and Israel. 5. Measure the yield potential of improved grain quality hybrids. (NOTE: Yield potential could not be evaluated due to environmentally-caused failure of the breeding nursery where seed was produced for this evaluation.) Background: Maize is a significant agricultural commodity worldwide. As an open pollinated crop, variation within the species is large and, in most cases, sufficient to supply the demand for modem varieties and for new environments. In recent years there is a growing demand for maize varieties with special quality attributes. While domesticated sources of genetic variation for high oil and protein content are limited, useful alleles for these traits may remain in maize's wild relative, teosinte. We utilized advanced backcross (AB) analysis to search for QTLs contributing to oil and protein content from two teosinte accessions: Zea mays ssp. mexicana Race Chalco, an annual teosinte (referred to as Chalco), and Z diploperennis Race San Miguel, a perennial teosinte (referred to as Diplo). Major Conclusions and Achievements Two NILs targeting a Diplo introgression in bin 1.04 showed a significant increase in oil content in homozygous sib-pollinated seed when compared to sibbed seed of their counterpart non-introgressed controls. These BC4S2 NILs, referred to as D-RD29 and D-RD30, carry the Diplo allele in bin 1.04 and the introgression extends partially into bins 1.03 and 1.05. These NILs remain heterozygous in bins 4.01 and 8.02, but otherwise are homozygous for the recurrent parent (RD6502) alleles. NILs were developed also for the Chalco introgression in bin 1.04 but these do not show any improvement in oil content, suggesting that the Chalco alleles differ from the Diplo alleles in this region. Testcross Fl seed and sibbed grain from these Fl plants did not show any effect on oil content from this introgression, suggesting that it would need to be present in both parents of a maize hybrid to have an effect on oil content. Implications, both Scientific and Agricultural The Diplo region identified increases oil content by 12.5% (from 4.8% to 5.4% oil in the seed). Although this absolute difference is not large in agronomic terms, this locus could provide additive increases to oil content in combination with other maize-derived loci for high oil. To our knowledge, this is the first confirmed report of a QTL from teosinte for improved grain oil content in maize. It suggests that further research on grain quality alleles from maize wild relatives would be of both scientific and agricultural interest.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

The function of the 30-kilodalton movement protein (MP) of tobacco mosaic virus (TMV) is to facilitate cell-to-cell movement of viral progeny in infected plants. Our earlier findings have indicated that this protein has a direct effect on plasmodesmal function. In addition, these studies demonstrated that constitutive expression of the TMV MP gene (under the control of the CaMV 35S promoter) in transgenic tobacco plants significantly affects carbon metabolism in source leaves and alters the biomass distribution between the various plant organs. The long-term goal of the proposed research was to better understand the factors controlling carbon translocation in plants. The specific objectives were: A) To introduce into tobacco and potato plants a virally-encoded (TMV-MP) gene that affects plasmodesmal functioning and photosynthate partitioning under tissue-specific promoters. B) To introduce into tobacco and potato plants the TMV-MP gene under the control of promoters which are tightly repressed by the Tn10-encoded Tet repressor, to enable the expression of the protein by external application of tetracycline. C) To explore the mechanism by which the TMV-MP interacts with the endogenous control o~ carbon allocation. Data obtained in our previous project together with the results of this current study established that the TMV-MP has pleiotropic effects when expressed in transgenic tobacco plants. In addition to its ability to increase the plasmodesmal size exclusion limit, it alters carbohydrate metabolism in source leaves and dry matter partitioning between the various plant organs, Expression of the TMV-MP in various tissues of transgenic potato plants indicated that sugars and starch levels in source leaves are reduced below those of control plants when the TMV-MP is expressed in green tissue only. However, when the TMV-MP was expressed predominantly in PP and CC, sugar and starch levels were raised above those of control plants. Perhaps the most significant result obtained from experiments performed on transgenic potato plants was the discovery that the influence of the TMV-MP on carbohydrate allocation within source leaves was under developmental control and was exerted only during tuber development. The complexity of the mode by which the TMV-MP exerts its effect on the process of carbohydrate allocation was further demonstrated when transgenic tobacco plants were subjected to environmental stresses such as drought stress and nutrients deficiencies, Collectively, these studies indicated that the influence of the TMV-MP on carbon allocation L the result of protein-protein interaction within the source tissue. Based on these results, together with the findings that plasmodesmata potentiate the cell-to-cell trafficking of viral and endogenous proteins and nucleoproteins complexes, we developed the theme that at the whole plant level, the phloem serves as an information superhighway. Such a long-distance communication system may utilize a new class of signaling molecules (proteins and/or RNA) to co-ordinate photosynthesis and carbon/nitrogen metabolism in source leaves with the complex growth requirements of the plant under the prevailing environmental conditions. The discovery that expression of viral MP in plants can induce precise changes in carbon metabolism and photoassimilate allocation, now provide a conceptual foundation for future studies aimed at elucidating the communication network responsible for integrating photosynthetic productivity with resource allocation at the whole-plant level. Such information will surely provide an understanding of how plants coordinate the essential physiological functions performed by distantly-separated organs. Identification of the proteins involved in mediating and controlling cell-to-cell transport, especially at the companion cell-sieve element boundary, will provide an important first step towards achieving this goal.

APA, Harvard, Vancouver, ISO, and other styles

10

Brosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.

Full text

Abstract:

Feed inputs represent the largest variable cost of producing meat and milk from ruminant animals. Thus, strategies that improve the efficiency of feed utilization are needed to improve the global competitiveness of Israeli and U.S. cattle industries, and mitigate their environmental impact through reductions in nutrient excretions and greenhouse gas emissions. Implementation of innovative technologies that will enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies to meet future demands for animal-protein foods in an environmentally sustainable manner. While considerable genetic variation in feed efficiency exist within cattle populations, the expense of measuring individual-animal feed intake has precluded implementation of selection programs that target this trait. Residual feed intake (RFI) is a trait that quantifies between-animal variation in feed intake beyond that expected to meet energy requirements for maintenance and production, with efficient animals being those that eat less than expected for a given size and level of production. There remains a critical need to understand the biological drivers for genetic variation in RFI to facilitate development of effective selection programs in the future. Therefore, the aim of this project was to determine the biological basis for phenotypic variation in RFI of growing and lactating cattle, and discover metabolic biomarkers of RFI for early and more cost-effective selection of cattle for feed efficiency. Objectives were to: (1) Characterize the phenotypic relationships between RFI and production traits (growth or lactation), (2) Quantify inter-animal variation in residual HP, (3) Determine if divergent RFIphenotypes differ in HP, residual HP, recovered energy and digestibility, and (4) Determine if divergent RFI phenotypes differ in physical activity, feeding behavior traits, serum hormones and metabolites and hepatic mitochondrial traits. The major research findings from this project to date include: In lactating dairy cattle, substantial phenotypic variation in RFI was demonstrated as cows classified as having low RMEI consumed 17% less MEI than high-RMEI cows despite having similar body size and lactation productivity. Further, between-animal variation in RMEI was found to moderately associated with differences in RHP demonstrating that maintenance energy requirements contribute to observed differences in RFI. Quantifying energetic efficiency of dairy cows using RHP revealed that substantial changes occur as week of lactation advances—thus it will be critical to measure RMEI at a standardized stage of lactation. Finally, to determine RMEI in lactating dairy cows, individual DMI and production data should be collected for a minimum of 6 wk. We demonstrated that a favorably association exists between RFI in growing heifers and efficiency of forage utilization in pregnant cows. Therefore, results indicate that female progeny from parents selected for low RFI during postweaning development will also be efficient as mature females, which has positive implications for both dairy and beef cattle industries. Results from the beef cattle studies further extend our knowledge regarding the biological drivers of phenotypic variation in RFI of growing animals, and demonstrate that significant differences in feeding behavioral patterns, digestibility and heart rate exist between animals with divergent RFI. Feeding behavior traits may be an effective biomarker trait for RFI in beef and dairy cattle. There are differences in mitochondrial acceptor control and respiratory control ratios between calves with divergent RFI suggesting that variation in mitochondrial metabolism may be visible at the genome level. Multiple genes associated with mitochondrial energy processes are altered by RFI phenotype and some of these genes are associated with mitochondrial energy expenditure and major cellular pathways involved in regulation of immune responses and energy metabolism.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!