216 related articles for article (PubMed ID: 29408627)
1. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.
Song J; Li F; Takemoto K; Haffari G; Akutsu T; Chou KC; Webb GI
J Theor Biol; 2018 Apr; 443():125-137. PubMed ID: 29408627
[TBL] [Abstract][Full Text] [Related]
2. Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties.
Petrova NV; Wu CH
BMC Bioinformatics; 2006 Jun; 7():312. PubMed ID: 16790052
[TBL] [Abstract][Full Text] [Related]
3. L1pred: a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier.
Dou Y; Wang J; Yang J; Zhang C
PLoS One; 2012; 7(4):e35666. PubMed ID: 22558194
[TBL] [Abstract][Full Text] [Related]
4. Structure-based prediction of protein- peptide binding regions using Random Forest.
Taherzadeh G; Zhou Y; Liew AW; Yang Y
Bioinformatics; 2018 Feb; 34(3):477-484. PubMed ID: 29028926
[TBL] [Abstract][Full Text] [Related]
5. Accurate sequence-based prediction of catalytic residues.
Zhang T; Zhang H; Chen K; Shen S; Ruan J; Kurgan L
Bioinformatics; 2008 Oct; 24(20):2329-38. PubMed ID: 18710875
[TBL] [Abstract][Full Text] [Related]
6. An improved prediction of catalytic residues in enzyme structures.
Tang YR; Sheng ZY; Chen YZ; Zhang Z
Protein Eng Des Sel; 2008 May; 21(5):295-302. PubMed ID: 18287176
[TBL] [Abstract][Full Text] [Related]
7. Evaluation of features for catalytic residue prediction in novel folds.
Youn E; Peters B; Radivojac P; Mooney SD
Protein Sci; 2007 Feb; 16(2):216-26. PubMed ID: 17189479
[TBL] [Abstract][Full Text] [Related]
8. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.
Wang S; Sun S; Li Z; Zhang R; Xu J
PLoS Comput Biol; 2017 Jan; 13(1):e1005324. PubMed ID: 28056090
[TBL] [Abstract][Full Text] [Related]
9. CSmetaPred: a consensus method for prediction of catalytic residues.
Choudhary P; Kumar S; Bachhawat AK; Pandit SB
BMC Bioinformatics; 2017 Dec; 18(1):583. PubMed ID: 29273005
[TBL] [Abstract][Full Text] [Related]
10. Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces.
Xiong Y; Zhu X; Dai H; Wei DQ
Methods Mol Biol; 2018; 1754():223-234. PubMed ID: 29536446
[TBL] [Abstract][Full Text] [Related]
11. LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction.
Kauffman C; Karypis G
Bioinformatics; 2009 Dec; 25(23):3099-107. PubMed ID: 19786483
[TBL] [Abstract][Full Text] [Related]
12. Identification of catalytic residues from protein structure using support vector machine with sequence and structural features.
Pugalenthi G; Kumar KK; Suganthan PN; Gangal R
Biochem Biophys Res Commun; 2008 Mar; 367(3):630-4. PubMed ID: 18206645
[TBL] [Abstract][Full Text] [Related]
13. Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.
Adhikari B; Hou J; Cheng J
Proteins; 2018 Mar; 86 Suppl 1(Suppl 1):84-96. PubMed ID: 29047157
[TBL] [Abstract][Full Text] [Related]
14. Genome-wide enzyme annotation with precision control: catalytic families (CatFam) databases.
Yu C; Zavaljevski N; Desai V; Reifman J
Proteins; 2009 Feb; 74(2):449-60. PubMed ID: 18636476
[TBL] [Abstract][Full Text] [Related]
15. An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins.
Zheng C; Wang M; Takemoto K; Akutsu T; Zhang Z; Song J
PLoS One; 2012; 7(11):e49716. PubMed ID: 23166753
[TBL] [Abstract][Full Text] [Related]
16. Detection of protein catalytic residues at high precision using local network properties.
Slama P; Filippis I; Lappe M
BMC Bioinformatics; 2008 Dec; 9():517. PubMed ID: 19055796
[TBL] [Abstract][Full Text] [Related]
17. Active site prediction using evolutionary and structural information.
Sankararaman S; Sha F; Kirsch JF; Jordan MI; Sjölander K
Bioinformatics; 2010 Mar; 26(5):617-24. PubMed ID: 20080507
[TBL] [Abstract][Full Text] [Related]
18. On the relationship between catalytic residues and their protein contact number.
Huang SW; Yu SH; Shih CH; Guan HW; Huang TT; Hwang JK
Curr Protein Pept Sci; 2011 Sep; 12(6):574-9. PubMed ID: 21787303
[TBL] [Abstract][Full Text] [Related]
19. Predicting enzyme functional surfaces and locating key residues automatically from structures.
Tseng YY; Liang J
Ann Biomed Eng; 2007 Jun; 35(6):1037-42. PubMed ID: 17294116
[TBL] [Abstract][Full Text] [Related]
20. Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines.
Tong W; Williams RJ; Wei Y; Murga LF; Ko J; Ondrechen MJ
Protein Sci; 2008 Feb; 17(2):333-41. PubMed ID: 18096640
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
