5 results on '"Peng, Zhenling"'
Search Results
2. Additional file 1 of Grazing exclusion had greater effects than nitrogen addition on soil and plant community in a desert steppe, Northwest of China
- Author
-
Li, Mengru, Wang, Lilong, Li, Junjun, Peng, Zhenling, Wang, Liang, Zhang, Xinfang, and Xu, Shijian
- Abstract
Additional file 1: Table S1. The community eigenvalue in EX and FG
- Published
- 2022
- Full Text
- View/download PDF
3. Critical assessment of protein intrinsic disorder prediction
- Author
-
Necci, Marco, Piovesan, Damiano, Hoque Md, Tamjidul, Walsh, Ian, Iqbal, Sumaiya, Vendruscolo, Michele, Sormanni, Pietro, Wang, Chen, Raimondi, Daniele, Sharma, Ronesh, Zhou, Yaoqi, Litfin, Thomas, Galzitskaya Oxana, Valerianovna, Lobanov Michail, Yu, Vranken, Wim, Wallner, Björn, Mirabello, Claudio, Malhis, Nawar, Dosztányi, Zsuzsanna, Erdős, Gábor, Mészáros, Bálint, Gao, Jianzhao, Wang, Kui, Hu, Gang, Wu, Zhonghua, Sharma, Alok, Hanson, Jack, Paliwal, Kuldip, Callebaut, Isabelle, Bitard-Feildel, Tristan, Orlando, Gabriele, Peng, Zhenling, Xu, Jinbo, Wang, Sheng, Jones David, T., Cozzetto, Domenico, Meng, Fanchi, Yan, Jing, Gsponer, Jörg, Cheng, Jianlin, Wu, Tianqi, Kurgan, Lukasz, Promponas Vasilis, J., Tamana, Stella, Marino-Buslje, Cristina, Martínez-Pérez, Elizabeth, Chasapi, Anastasia, Ouzounis, Christos, Dunker A., Keith, Kajava Andrey, V., Leclercq Jeremy, Y., Aykac-Fas, Burcu, Lambrughi, Matteo, Maiani, Emiliano, Papaleo, Elena, Chemes Lucia, Beatriz, Álvarez, Lucía, González-Foutel Nicolás, S., Iglesias, Valentin, Pujols, Jordi, Ventura, Salvador, Palopoli, Nicolás, Benítez Guillermo, Ignacio, Parisi, Gustavo, Bassot, Claudio, Elofsson, Arne, Govindarajan, Sudha, Lamb, John, Salvatore, Marco, Hatos, András, Monzon Alexander, Miguel, Bevilacqua, Martina, Mičetić, Ivan, Minervini, Giovanni, Paladin, Lisanna, Quaglia, Federica, Leonardi, Emanuela, Davey, Norman, Horvath, Tamas, Kovacs Orsolya, Panna, Murvai, Nikoletta, Pancsa, Rita, Schad, Eva, Szabo, Beata, Tantos, Agnes, Macedo-Ribeiro, Sandra, Manso Jose, Antonio, Pereira Pedro José, Barbosa, Davidović, Radoslav, Veljkovic, Nevena, Hajdu-Soltész, Borbála, Pajkos, Mátyás, Szaniszló, Tamás, Guharoy, Mainak, Lazar, Tamas, Macossay-Castillo, Mauricio, Tompa, Peter, Tosatto Silvio C., E., Caid, Predictors, DisProt, Curators, Università degli Studi di Padova = University of Padua (Unipd), Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC), Muséum national d'Histoire naturelle (MNHN)-Institut de recherche pour le développement [IRD] : UR206-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Necci, Marco [0000-0001-9377-482X], Piovesan, Damiano [0000-0001-8210-2390], Tosatto, Silvio C. E. [0000-0003-4525-7793], Apollo - University of Cambridge Repository, Informatics and Applied Informatics, Chemistry, Basic (bio-) Medical Sciences, Department of Bio-engineering Sciences, Faculty of Sciences and Bioengineering Sciences, Structural Biology Brussels, Tosatto, Silvio CE [0000-0003-4525-7793], ANR-17-CE12-0016,FUNBRCA2,Caractérisation d'un nouveau site de liaison à l'ADN dans la protéine BRCA2(2017), Universita degli Studi di Padova, CAID Predictors, and DisProt Curators
- Subjects
Protein Folding ,Protein Conformation ,Computer science ,631/45/612 ,analysis ,[SDV]Life Sciences [q-bio] ,purl.org/becyt/ford/1.7 [https] ,MESH: Amino Acid Sequence ,Biochemistry ,purl.org/becyt/ford/1 [https] ,Protein structure ,MESH: Protein Conformation ,631/114/2398 ,Databases, Protein ,Biological sciences ,ComputingMilieux_MISCELLANEOUS ,MESH: Intrinsically Disordered Proteins ,0303 health sciences ,030302 biochemistry & molecular biology ,disorder ,Critical assessment ,Protein folding ,Protein Binding ,Biotechnology ,MESH: Computational Biology ,MESH: Databases, Protein ,disorder prediction ,MESH: Protein Folding ,Computational biology ,Intrinsically disordered proteins ,Orders of magnitude (entropy) ,03 medical and health sciences ,MESH: Software ,Computational platforms and environments ,631/114/2411 ,Machine learning ,Molecule ,MESH: Protein Binding ,[INFO]Computer Science [cs] ,Amino Acid Sequence ,Molecular Biology ,030304 developmental biology ,business.industry ,Deep learning ,Computational Biology ,Proteins ,Cell Biology ,631/114/1305 ,Intrinsically Disordered Proteins ,CAID ,631/114/794 ,Protein structure predictions ,Artificial intelligence ,business ,Software - Abstract
Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude., Results are presented from the first Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment, a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins.
- Published
- 2021
4. Large-scale Characterization Of Intrinsic Disorder And High-throughput Prediction Of RNA, DNA and Protein Binding Mediated By Intrinsic Disorder
- Author
-
Peng, Zhenling
- Published
- 2014
- Full Text
- View/download PDF
5. MFDp2
- Author
-
Mizianty, Marcin J., Peng, Zhenling, and Kurgan, Lukasz
- Subjects
disorder content ,Technical Paper ,disorder profiles ,intrinsically disordered proteins ,prediction - Abstract
Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases, and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel content-rich and user-friendly web server for sequence-based prediction of protein disorder that builds upon our residue-level disorder predictor MFDp and chain-level disorder content predictor DisCon. It applies novel post-processing filters and uses sequence alignment to improve predictive quality. Using a new benchmark data set, which has reduced sequence identity to corresponding training data sets, MFDp2 is shown to provide competitive predictive quality when compared with MFDp and a comprehensive set of 13 other state-of-the-art predictors, including publicly available versions of the top predictors from CASP9. Our server obtains the highest Mathews Correlation Coefficient (MCC) and the second best Area Under the receiver operating characteristic Curve (AUC). In addition to the disorder predictions, our server also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility and alignments to chains with annotated disorder. We allow predictions for multiple proteins at the same time and each prediction can be downloaded as text-based (parsable) file. The web server, which includes help pages and tutorial, is freely available at biomine.ece.ualberta.ca/MFDp2/.
- Published
- 2013
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.