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Showing 1 to 12 of 22 entries
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POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

Bioinformatics (Oxford, England)

Wang J, Yang B, Revote J, Leier A, Marquez-Lago TT, Webb G, Song J, Chou KC, Lithgow T.
PMID: 28903538
Bioinformatics. 2017 Sep 01;33(17):2756-2758. doi: 10.1093/bioinformatics/btx302.

SUMMARY: Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to improve the performance of various predictors of...

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Wang J, Yang B, Revote J, et al. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics. 2017;33(17):2756-2758doi: 10.1093/bioinformatics/btx302.
Wang, J., Yang, B., Revote, J., Leier, A., Marquez-Lago, T. T., Webb, G., Song, J., Chou, K. C., & Lithgow, T. (2017). POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics (Oxford, England), 33(17), 2756-2758. https://doi.org/10.1093/bioinformatics/btx302
Wang, Jiawei, et al. "POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles." Bioinformatics (Oxford, England) vol. 33,17 (2017): 2756-2758. doi: https://doi.org/10.1093/bioinformatics/btx302
Wang J, Yang B, Revote J, Leier A, Marquez-Lago TT, Webb G, Song J, Chou KC, Lithgow T. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics. 2017 Sep 01;33(17):2756-2758. doi: 10.1093/bioinformatics/btx302. PMID: 28903538.
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Prediction of intrinsic disorder in proteins using MFDp2.

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

Mizianty MJ, Uversky V, Kurgan L.
PMID: 24573480
Methods Mol Biol. 2014;1137:147-62. doi: 10.1007/978-1-4939-0366-5_11.

Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant across all kingdoms of life, particularly in eukaryotes, and are implicated in numerous cellular processes. Experimental annotation...

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Mizianty MJ, Uversky V, Kurgan L. Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol. 2014;1137:147-62doi: 10.1007/978-1-4939-0366-5_11.
Mizianty, M. J., Uversky, V., & Kurgan, L. (2014). Prediction of intrinsic disorder in proteins using MFDp2. Methods in molecular biology (Clifton, N.J.), 1137147-62. https://doi.org/10.1007/978-1-4939-0366-5_11
Mizianty, Marcin J, et al. "Prediction of intrinsic disorder in proteins using MFDp2." Methods in molecular biology (Clifton, N.J.) vol. 1137 (2014): 147-62. doi: https://doi.org/10.1007/978-1-4939-0366-5_11
Mizianty MJ, Uversky V, Kurgan L. Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol. 2014;1137:147-62. doi: 10.1007/978-1-4939-0366-5_11. PMID: 24573480.
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OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

Bioinformatics (Oxford, England)

Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A.
PMID: 29360926
Bioinformatics. 2018 Jun 01;34(11):1850-1858. doi: 10.1093/bioinformatics/bty032.

MOTIVATION: Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from...

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Sharma R, Raicar G, Tsunoda T, et al. OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics. 2018;34(11):1850-1858doi: 10.1093/bioinformatics/bty032.
Sharma, R., Raicar, G., Tsunoda, T., Patil, A., & Sharma, A. (2018). OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics (Oxford, England), 34(11), 1850-1858. https://doi.org/10.1093/bioinformatics/bty032
Sharma, Ronesh, et al. "OPAL: prediction of MoRF regions in intrinsically disordered protein sequences." Bioinformatics (Oxford, England) vol. 34,11 (2018): 1850-1858. doi: https://doi.org/10.1093/bioinformatics/bty032
Sharma R, Raicar G, Tsunoda T, Patil A, Sharma A. OPAL: prediction of MoRF regions in intrinsically disordered protein sequences. Bioinformatics. 2018 Jun 01;34(11):1850-1858. doi: 10.1093/bioinformatics/bty032. PMID: 29360926.
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Thousands of protein linear motif classes may still be undiscovered.

PloS one

Bulavka D, Aptekmann AA, Méndez NA, Krick T, Sánchez IE.
PMID: 33939703
PLoS One. 2021 May 03;16(5):e0248841. doi: 10.1371/journal.pone.0248841. eCollection 2021.

Linear motifs are short protein subsequences that mediate protein interactions. Hundreds of motif classes including thousands of motif instances are known. Our theory estimates how many motif classes remain undiscovered. As commonly done, we describe motif classes as regular...

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Bulavka D, Aptekmann AA, Méndez NA, et al. Thousands of protein linear motif classes may still be undiscovered. PLoS One. 2021;16(5):e0248841doi: 10.1371/journal.pone.0248841.
Bulavka, D., Aptekmann, A. A., Méndez, N. A., Krick, T., & Sánchez, I. E. (2021). Thousands of protein linear motif classes may still be undiscovered. PloS one, 16(5), e0248841. https://doi.org/10.1371/journal.pone.0248841
Bulavka, Denys, et al. "Thousands of protein linear motif classes may still be undiscovered." PloS one vol. 16,5 (2021): e0248841. doi: https://doi.org/10.1371/journal.pone.0248841
Bulavka D, Aptekmann AA, Méndez NA, Krick T, Sánchez IE. Thousands of protein linear motif classes may still be undiscovered. PLoS One. 2021 May 03;16(5):e0248841. doi: 10.1371/journal.pone.0248841. eCollection 2021. PMID: 33939703; PMCID: PMC8092775.
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PyMOL and Inkscape Bridge the Data and the Data Visualization.

Structure (London, England : 1993)

Yuan S, Chan HCS, Filipek S, Vogel H.
PMID: 27926832
Structure. 2016 Dec 06;24(12):2041-2042. doi: 10.1016/j.str.2016.11.012.

No abstract available.

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Yuan S, Chan HCS, Filipek S, et al. PyMOL and Inkscape Bridge the Data and the Data Visualization. Structure. 2016;24(12):2041-2042doi: 10.1016/j.str.2016.11.012.
Yuan, S., Chan, H. C. S., Filipek, S., & Vogel, H. (2016). PyMOL and Inkscape Bridge the Data and the Data Visualization. Structure (London, England : 1993), 24(12), 2041-2042. https://doi.org/10.1016/j.str.2016.11.012
Yuan, Shuguang, et al. "PyMOL and Inkscape Bridge the Data and the Data Visualization." Structure (London, England : 1993) vol. 24,12 (2016): 2041-2042. doi: https://doi.org/10.1016/j.str.2016.11.012
Yuan S, Chan HCS, Filipek S, Vogel H. PyMOL and Inkscape Bridge the Data and the Data Visualization. Structure. 2016 Dec 06;24(12):2041-2042. doi: 10.1016/j.str.2016.11.012. PMID: 27926832.
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DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Briefings in bioinformatics

Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ.
PMID: 31885041
Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152.

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the...

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Chu Y, Kaushik AC, Wang X, et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2021;22(1):451-462doi: 10.1093/bib/bbz152.
Chu, Y., Kaushik, A. C., Wang, X., Wang, W., Zhang, Y., Shan, X., Salahub, D. R., Xiong, Y., & Wei, D. Q. (2021). DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Briefings in bioinformatics, 22(1), 451-462. https://doi.org/10.1093/bib/bbz152
Chu, Yanyi, et al. "DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features." Briefings in bioinformatics vol. 22,1 (2021): 451-462. doi: https://doi.org/10.1093/bib/bbz152
Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152. PMID: 31885041.
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pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

Bioinformatics (Oxford, England)

Cheng X, Xiao X, Chou KC.
PMID: 29106451
Bioinformatics. 2018 May 01;34(9):1448-1456. doi: 10.1093/bioinformatics/btx711.

MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence information alone. Although considerable efforts...

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Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics. 2018;34(9):1448-1456doi: 10.1093/bioinformatics/btx711.
Cheng, X., Xiao, X., & Chou, K. C. (2018). pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics (Oxford, England), 34(9), 1448-1456. https://doi.org/10.1093/bioinformatics/btx711
Cheng, Xiang, et al. "pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information." Bioinformatics (Oxford, England) vol. 34,9 (2018): 1448-1456. doi: https://doi.org/10.1093/bioinformatics/btx711
Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics. 2018 May 01;34(9):1448-1456. doi: 10.1093/bioinformatics/btx711. PMID: 29106451.
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Paving the way to single-molecule protein sequencing.

Nature nanotechnology

Restrepo-Pérez L, Joo C, Dekker C.
PMID: 30190617
Nat Nanotechnol. 2018 Sep;13(9):786-796. doi: 10.1038/s41565-018-0236-6. Epub 2018 Sep 06.

Proteins are major building blocks of life. The protein content of a cell and an organism provides key information for the understanding of biological processes and disease. Despite the importance of protein analysis, only a handful of techniques are...

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Restrepo-Pérez L, Joo C, Dekker C. Paving the way to single-molecule protein sequencing. Nat Nanotechnol. 2018;13(9):786-796doi: 10.1038/s41565-018-0236-6.
Restrepo-Pérez, L., Joo, C., & Dekker, C. (2018). Paving the way to single-molecule protein sequencing. Nature nanotechnology, 13(9), 786-796. https://doi.org/10.1038/s41565-018-0236-6
Restrepo-Pérez, Laura, et al. "Paving the way to single-molecule protein sequencing." Nature nanotechnology vol. 13,9 (2018): 786-796. doi: https://doi.org/10.1038/s41565-018-0236-6
Restrepo-Pérez L, Joo C, Dekker C. Paving the way to single-molecule protein sequencing. Nat Nanotechnol. 2018 Sep;13(9):786-796. doi: 10.1038/s41565-018-0236-6. Epub 2018 Sep 06. PMID: 30190617.
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Considering scores between unrelated proteins in the search database improves profile comparison.

BMC bioinformatics

Sadreyev RI, Wang Y, Grishin NV.
PMID: 19961610
BMC Bioinformatics. 2009 Dec 04;10:399. doi: 10.1186/1471-2105-10-399.

BACKGROUND: Profile-based comparison of multiple sequence alignments is a powerful methodology for the detection remote protein sequence similarity, which is essential for the inference and analysis of protein structure, function, and evolution. Accurate estimation of statistical significance of detected...

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Sadreyev RI, Wang Y, Grishin NV. Considering scores between unrelated proteins in the search database improves profile comparison. BMC Bioinformatics. 2009;10:399doi: 10.1186/1471-2105-10-399.
Sadreyev, R. I., Wang, Y., & Grishin, N. V. (2009). Considering scores between unrelated proteins in the search database improves profile comparison. BMC bioinformatics, 10399. https://doi.org/10.1186/1471-2105-10-399
Sadreyev, Ruslan I, et al. "Considering scores between unrelated proteins in the search database improves profile comparison." BMC bioinformatics vol. 10 (2009): 399. doi: https://doi.org/10.1186/1471-2105-10-399
Sadreyev RI, Wang Y, Grishin NV. Considering scores between unrelated proteins in the search database improves profile comparison. BMC Bioinformatics. 2009 Dec 04;10:399. doi: 10.1186/1471-2105-10-399. PMID: 19961610; PMCID: PMC3087343.
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Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe.

Protein science : a publication of the Protein Society

Necci M, Piovesan D, Tosatto SC.
PMID: 27636733
Protein Sci. 2016 Dec;25(12):2164-2174. doi: 10.1002/pro.3041. Epub 2016 Oct 25.

Intrinsic disorder (ID) in proteins has been extensively described for the last decade; a large-scale classification of ID in proteins is mostly missing. Here, we provide an extensive analysis of ID in the protein universe on the UniProt database...

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Necci M, Piovesan D, Tosatto SC. Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe. Protein Sci. 2016;25(12):2164-2174doi: 10.1002/pro.3041.
Necci, M., Piovesan, D., & Tosatto, S. C. (2016). Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe. Protein science : a publication of the Protein Society, 25(12), 2164-2174. https://doi.org/10.1002/pro.3041
Necci, Marco, et al. "Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe." Protein science : a publication of the Protein Society vol. 25,12 (2016): 2164-2174. doi: https://doi.org/10.1002/pro.3041
Necci M, Piovesan D, Tosatto SC. Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe. Protein Sci. 2016 Dec;25(12):2164-2174. doi: 10.1002/pro.3041. Epub 2016 Oct 25. PMID: 27636733; PMCID: PMC5119570.
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DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields.

International journal of molecular sciences

Wang S, Weng S, Ma J, Tang Q.
PMID: 26230689
Int J Mol Sci. 2015 Jul 29;16(8):17315-30. doi: 10.3390/ijms160817315.

Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of...

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Wang S, Weng S, Ma J, et al. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields. Int J Mol Sci. 2015;16(8):17315-30doi: 10.3390/ijms160817315.
Wang, S., Weng, S., Ma, J., & Tang, Q. (2015). DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields. International journal of molecular sciences, 16(8), 17315-30. https://doi.org/10.3390/ijms160817315
Wang, Sheng, et al. "DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields." International journal of molecular sciences vol. 16,8 (2015): 17315-30. doi: https://doi.org/10.3390/ijms160817315
Wang S, Weng S, Ma J, Tang Q. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields. Int J Mol Sci. 2015 Jul 29;16(8):17315-30. doi: 10.3390/ijms160817315. PMID: 26230689; PMCID: PMC4581195.
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AmyLoad: website dedicated to amyloidogenic protein fragments.

Bioinformatics (Oxford, England)

Wozniak PP, Kotulska M.
PMID: 26088800
Bioinformatics. 2015 Oct 15;31(20):3395-7. doi: 10.1093/bioinformatics/btv375. Epub 2015 Jun 17.

UNLABELLED: Analyses of amyloidogenic sequence fragments are essential in studies of neurodegenerative diseases. However, there is no one internet dataset that collects all the sequences that have been investigated for their amyloidogenicity. Therefore, we have created the AmyLoad website...

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Wozniak PP, Kotulska M. AmyLoad: website dedicated to amyloidogenic protein fragments. Bioinformatics. 2015;31(20):3395-7doi: 10.1093/bioinformatics/btv375.
Wozniak, P. P., & Kotulska, M. (2015). AmyLoad: website dedicated to amyloidogenic protein fragments. Bioinformatics (Oxford, England), 31(20), 3395-7. https://doi.org/10.1093/bioinformatics/btv375
Wozniak, Pawel P, and Kotulska, Malgorzata. "AmyLoad: website dedicated to amyloidogenic protein fragments." Bioinformatics (Oxford, England) vol. 31,20 (2015): 3395-7. doi: https://doi.org/10.1093/bioinformatics/btv375
Wozniak PP, Kotulska M. AmyLoad: website dedicated to amyloidogenic protein fragments. Bioinformatics. 2015 Oct 15;31(20):3395-7. doi: 10.1093/bioinformatics/btv375. Epub 2015 Jun 17. PMID: 26088800.
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Showing 1 to 12 of 22 entries