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Showing 25 to 36 of 41 entries
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CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers.

Structure (London, England : 1993)

Nepomnyachiy S, Ben-Tal N, Kolodny R.
PMID: 25865247
Structure. 2015 May 05;23(5):941-948. doi: 10.1016/j.str.2015.02.013. Epub 2015 Apr 09.

It can be informative to view biological data, e.g., protein-protein interactions within a large complex, in a network representation coupled with three-dimensional structural visualizations of individual molecular entities. CyToStruct, introduced here, provides a transparent interface between the Cytoscape platform...

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Nepomnyachiy S, Ben-Tal N, Kolodny R. CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers. Structure. 2015;23(5):941-948doi: 10.1016/j.str.2015.02.013.
Nepomnyachiy, S., Ben-Tal, N., & Kolodny, R. (2015). CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers. Structure (London, England : 1993), 23(5), 941-948. https://doi.org/10.1016/j.str.2015.02.013
Nepomnyachiy, Sergey, et al. "CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers." Structure (London, England : 1993) vol. 23,5 (2015): 941-948. doi: https://doi.org/10.1016/j.str.2015.02.013
Nepomnyachiy S, Ben-Tal N, Kolodny R. CyToStruct: Augmenting the Network Visualization of Cytoscape with the Power of Molecular Viewers. Structure. 2015 May 05;23(5):941-948. doi: 10.1016/j.str.2015.02.013. Epub 2015 Apr 09. PMID: 25865247.
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Artificial supramolecular protein assemblies as functional high-order protein scaffolds.

Organic & biomolecular chemistry

Kim YN, Jung Y.
PMID: 26964520
Org Biomol Chem. 2016 Jun 28;14(24):5352-6. doi: 10.1039/c6ob00116e. Epub 2016 Mar 11.

Supramolecular assemblies of protein building blocks potentially offer unique biomaterials with unmatched functionalities as well as atomic level structural accuracy. An increasing number of assembling strategies have been reported for the fabrication of diverse artificial protein assemblies, ranging from...

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Kim YN, Jung Y. Artificial supramolecular protein assemblies as functional high-order protein scaffolds. Org Biomol Chem. 2016;14(24):5352-6doi: 10.1039/c6ob00116e.
Kim, Y. N., & Jung, Y. (2016). Artificial supramolecular protein assemblies as functional high-order protein scaffolds. Organic & biomolecular chemistry, 14(24), 5352-6. https://doi.org/10.1039/c6ob00116e
Kim, Yu-Na, and Jung, Yongwon. "Artificial supramolecular protein assemblies as functional high-order protein scaffolds." Organic & biomolecular chemistry vol. 14,24 (2016): 5352-6. doi: https://doi.org/10.1039/c6ob00116e
Kim YN, Jung Y. Artificial supramolecular protein assemblies as functional high-order protein scaffolds. Org Biomol Chem. 2016 Jun 28;14(24):5352-6. doi: 10.1039/c6ob00116e. Epub 2016 Mar 11. PMID: 26964520.
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Designing Conservation Relations in Layered Synthetic Biomolecular Networks.

IEEE transactions on biomedical circuits and systems

Prescott TP, Papachristodoulou A.
PMID: 26357406
IEEE Trans Biomed Circuits Syst. 2015 Aug;9(4):572-80. doi: 10.1109/TBCAS.2015.2460376. Epub 2015 Sep 09.

In Synthetic Biology, biomolecular networks are designed and constructed to perform specified tasks. Design strategies for these networks tend to center on tuning the parameters of mathematical models to achieve a specified behavior, and implementing these parameters experimentally. This...

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Prescott TP, Papachristodoulou A. Designing Conservation Relations in Layered Synthetic Biomolecular Networks. IEEE Trans Biomed Circuits Syst. 2015;9(4):572-80doi: 10.1109/TBCAS.2015.2460376.
Prescott, T. P., & Papachristodoulou, A. (2015). Designing Conservation Relations in Layered Synthetic Biomolecular Networks. IEEE transactions on biomedical circuits and systems, 9(4), 572-80. https://doi.org/10.1109/TBCAS.2015.2460376
Prescott, Thomas P, and Papachristodoulou, Antonis. "Designing Conservation Relations in Layered Synthetic Biomolecular Networks." IEEE transactions on biomedical circuits and systems vol. 9,4 (2015): 572-80. doi: https://doi.org/10.1109/TBCAS.2015.2460376
Prescott TP, Papachristodoulou A. Designing Conservation Relations in Layered Synthetic Biomolecular Networks. IEEE Trans Biomed Circuits Syst. 2015 Aug;9(4):572-80. doi: 10.1109/TBCAS.2015.2460376. Epub 2015 Sep 09. PMID: 26357406.
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Fuento: functional enrichment for bioinformatics.

Bioinformatics (Oxford, England)

Weichselbaum D, Zagrovic B, Polyansky AA.
PMID: 28379299
Bioinformatics. 2017 Aug 15;33(16):2604-2606. doi: 10.1093/bioinformatics/btx179.

SUMMARY: The currently available functional enrichment software focuses mostly on gene expression analysis, whereby server- and graphical-user-interface-based tools with specific scope dominate the field. Here we present an efficient, user-friendly, multifunctional command-line-based functional enrichment tool (fu-en-to), tailored for the...

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Weichselbaum D, Zagrovic B, Polyansky AA. Fuento: functional enrichment for bioinformatics. Bioinformatics. 2017;33(16):2604-2606doi: 10.1093/bioinformatics/btx179.
Weichselbaum, D., Zagrovic, B., & Polyansky, A. A. (2017). Fuento: functional enrichment for bioinformatics. Bioinformatics (Oxford, England), 33(16), 2604-2606. https://doi.org/10.1093/bioinformatics/btx179
Weichselbaum, David, et al. "Fuento: functional enrichment for bioinformatics." Bioinformatics (Oxford, England) vol. 33,16 (2017): 2604-2606. doi: https://doi.org/10.1093/bioinformatics/btx179
Weichselbaum D, Zagrovic B, Polyansky AA. Fuento: functional enrichment for bioinformatics. Bioinformatics. 2017 Aug 15;33(16):2604-2606. doi: 10.1093/bioinformatics/btx179. PMID: 28379299; PMCID: PMC5870553.
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Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions.

Physical review. E

Hubicka K, Janczura J.
PMID: 32168604
Phys Rev E. 2020 Feb;101(2):022107. doi: 10.1103/PhysRevE.101.022107.

In this paper, we have proposed a statistical procedure for detecting transitions of the mean-square-displacement exponent value within a single trajectory. With this procedure, we have identified three regimes of proteins dynamics on a cell membrane, namely, subdiffusion, free...

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Hubicka K, Janczura J. Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions. Phys Rev E. 2020;101(2):022107doi: 10.1103/PhysRevE.101.022107.
Hubicka, K., & Janczura, J. (2020). Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions. Physical review. E, 101(2), 022107. https://doi.org/10.1103/PhysRevE.101.022107
Hubicka, Katarzyna, and Janczura, Joanna. "Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions." Physical review. E vol. 101,2 (2020): 022107. doi: https://doi.org/10.1103/PhysRevE.101.022107
Hubicka K, Janczura J. Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions. Phys Rev E. 2020 Feb;101(2):022107. doi: 10.1103/PhysRevE.101.022107. PMID: 32168604.
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Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features.

Current protocols in protein science

Ding Z, Kihara D.
PMID: 29927082
Curr Protoc Protein Sci. 2018 Aug;93(1):e62. doi: 10.1002/cpps.62. Epub 2018 Jun 21.

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms....

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Ding Z, Kihara D. Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. Curr Protoc Protein Sci. 2018;93(1):e62doi: 10.1002/cpps.62.
Ding, Z., & Kihara, D. (2018). Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. Current protocols in protein science, 93(1), e62. https://doi.org/10.1002/cpps.62
Ding, Ziyun, and Kihara, Daisuke. "Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features." Current protocols in protein science vol. 93,1 (2018): e62. doi: https://doi.org/10.1002/cpps.62
Ding Z, Kihara D. Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. Curr Protoc Protein Sci. 2018 Aug;93(1):e62. doi: 10.1002/cpps.62. Epub 2018 Jun 21. PMID: 29927082; PMCID: PMC6097941.
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FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

BMC bioinformatics

Bachman JA, Gyori BM, Sorger PK.
PMID: 29954318
BMC Bioinformatics. 2018 Jun 28;19(1):248. doi: 10.1186/s12859-018-2211-5.

BACKGROUND: For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct...

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Bachman JA, Gyori BM, Sorger PK. FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining. BMC Bioinformatics. 2018;19(1):248doi: 10.1186/s12859-018-2211-5.
Bachman, J. A., Gyori, B. M., & Sorger, P. K. (2018). FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining. BMC bioinformatics, 19(1), 248. https://doi.org/10.1186/s12859-018-2211-5
Bachman, John A, et al. "FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining." BMC bioinformatics vol. 19,1 (2018): 248. doi: https://doi.org/10.1186/s12859-018-2211-5
Bachman JA, Gyori BM, Sorger PK. FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining. BMC Bioinformatics. 2018 Jun 28;19(1):248. doi: 10.1186/s12859-018-2211-5. PMID: 29954318; PMCID: PMC6022344.
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The Future of Computational Chemogenomics.

Methods in molecular biology (Clifton, N.J.)

Jacoby E, Brown JB.
PMID: 30334216
Methods Mol Biol. 2018;1825:425-450. doi: 10.1007/978-1-4939-8639-2_15.

Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and...

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Jacoby E, Brown JB. The Future of Computational Chemogenomics. Methods Mol Biol. 2018;1825:425-450doi: 10.1007/978-1-4939-8639-2_15.
Jacoby, E., & Brown, J. B. (2018). The Future of Computational Chemogenomics. Methods in molecular biology (Clifton, N.J.), 1825425-450. https://doi.org/10.1007/978-1-4939-8639-2_15
Jacoby, Edgar, and Brown, J B. "The Future of Computational Chemogenomics." Methods in molecular biology (Clifton, N.J.) vol. 1825 (2018): 425-450. doi: https://doi.org/10.1007/978-1-4939-8639-2_15
Jacoby E, Brown JB. The Future of Computational Chemogenomics. Methods Mol Biol. 2018;1825:425-450. doi: 10.1007/978-1-4939-8639-2_15. PMID: 30334216.
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Research in Rare Disease: From Genomics to Proteomics.

Assay and drug development technologies

Lacoste J.
PMID: 29345978
Assay Drug Dev Technol. 2018 Jan;16(1):12-14. doi: 10.1089/adt.2017.828.

Jessica Lacoste from the Donnelly Centre at the University of Toronto was awarded best poster at the annual Society of Biomolecular Imaging and Informatics meeting held in San Diego, September 2017. Her work focuses on characterizing the protein localization...

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Lacoste J. Research in Rare Disease: From Genomics to Proteomics. Assay Drug Dev Technol. 2018;16(1):12-14doi: 10.1089/adt.2017.828.
Lacoste, J. (2018). Research in Rare Disease: From Genomics to Proteomics. Assay and drug development technologies, 16(1), 12-14. https://doi.org/10.1089/adt.2017.828
Lacoste, Jessica. "Research in Rare Disease: From Genomics to Proteomics." Assay and drug development technologies vol. 16,1 (2018): 12-14. doi: https://doi.org/10.1089/adt.2017.828
Lacoste J. Research in Rare Disease: From Genomics to Proteomics. Assay Drug Dev Technol. 2018 Jan;16(1):12-14. doi: 10.1089/adt.2017.828. PMID: 29345978.
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Tools 2021: New methods, new approaches, and a salute to recent developments in protein science.

Protein science : a publication of the Protein Society

Matthews BW.
PMID: 33264460
Protein Sci. 2021 Jan;30(1):5-7. doi: 10.1002/pro.4003. Epub 2020 Dec 02.

No abstract available.

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Matthews BW. Tools 2021: New methods, new approaches, and a salute to recent developments in protein science. Protein Sci. 2020;30(1):5-7doi: 10.1002/pro.4003.
Matthews, B. W. (2021). Tools 2021: New methods, new approaches, and a salute to recent developments in protein science. Protein science : a publication of the Protein Society, 30(1), 5-7. https://doi.org/10.1002/pro.4003
Matthews, Brian W. "Tools 2021: New methods, new approaches, and a salute to recent developments in protein science." Protein science : a publication of the Protein Society vol. 30,1 (2021): 5-7. doi: https://doi.org/10.1002/pro.4003
Matthews BW. Tools 2021: New methods, new approaches, and a salute to recent developments in protein science. Protein Sci. 2021 Jan;30(1):5-7. doi: 10.1002/pro.4003. Epub 2020 Dec 02. PMID: 33264460; PMCID: PMC7737770.
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A three-phase method for identifying functionally related protein groups in weighted PPI networks.

Computational biology and chemistry

Grbić M, Matić D, Kartelj A, Vračević S, Filipović V.
PMID: 32339914
Comput Biol Chem. 2020 Jun;86:107246. doi: 10.1016/j.compbiolchem.2020.107246. Epub 2020 Mar 05.

Identifying significant protein groups is of great importance for further understanding protein functions. This paper introduces a novel three-phase heuristic method for identifying such groups in weighted PPI networks. In the first phase a variable neighborhood search (VNS) algorithm...

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Grbić M, Matić D, Kartelj A, et al. A three-phase method for identifying functionally related protein groups in weighted PPI networks. Comput Biol Chem. 2020;86:107246doi: 10.1016/j.compbiolchem.2020.107246.
Grbić, M., Matić, D., Kartelj, A., Vračević, S., & Filipović, V. (2020). A three-phase method for identifying functionally related protein groups in weighted PPI networks. Computational biology and chemistry, 86107246. https://doi.org/10.1016/j.compbiolchem.2020.107246
Grbić, Milana, et al. "A three-phase method for identifying functionally related protein groups in weighted PPI networks." Computational biology and chemistry vol. 86 (2020): 107246. doi: https://doi.org/10.1016/j.compbiolchem.2020.107246
Grbić M, Matić D, Kartelj A, Vračević S, Filipović V. A three-phase method for identifying functionally related protein groups in weighted PPI networks. Comput Biol Chem. 2020 Jun;86:107246. doi: 10.1016/j.compbiolchem.2020.107246. Epub 2020 Mar 05. PMID: 32339914.
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Targeted protein degradation: A promise for undruggable proteins.

Cell chemical biology

Samarasinghe KTG, Crews CM.
PMID: 34004187
Cell Chem Biol. 2021 Jul 15;28(7):934-951. doi: 10.1016/j.chembiol.2021.04.011. Epub 2021 May 17.

Protein homeostasis, or "proteostasis," is indispensable for a balanced, healthy environment within the cell. However, when natural proteostasis mechanisms are overwhelmed from excessive loads of dysregulated proteins, their accumulation can lead to disease initiation and progression. Recently, the induced...

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Samarasinghe KTG, Crews CM. Targeted protein degradation: A promise for undruggable proteins. Cell Chem Biol. 2021;28(7):934-951doi: 10.1016/j.chembiol.2021.04.011.
Samarasinghe, K. T. G., & Crews, C. M. (2021). Targeted protein degradation: A promise for undruggable proteins. Cell chemical biology, 28(7), 934-951. https://doi.org/10.1016/j.chembiol.2021.04.011
Samarasinghe, Kusal T G, and Crews, Craig M. "Targeted protein degradation: A promise for undruggable proteins." Cell chemical biology vol. 28,7 (2021): 934-951. doi: https://doi.org/10.1016/j.chembiol.2021.04.011
Samarasinghe KTG, Crews CM. Targeted protein degradation: A promise for undruggable proteins. Cell Chem Biol. 2021 Jul 15;28(7):934-951. doi: 10.1016/j.chembiol.2021.04.011. Epub 2021 May 17. PMID: 34004187; PMCID: PMC8286327.
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Showing 25 to 36 of 41 entries