Prediction of novel archaeal enzymes from sequence-derived features

被引：34
|
作者：
Jensen, LJ [1 ]
Skovgaard, M [1 ]
Brunak, S [1 ]
机构：
[1] Tech Univ Denmark, Ctr Biol Sequence Anal, BioCentrum, DK-2800 Lyngby, Denmark
来源：
PROTEIN SCIENCE | 2002年 / 11卷 / 12期
关键词：
function prediction; enzyme classification; Archaea; glycosylation; secondary structure;
D O I：
10.1110/ps.0225102
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
The completely sequenced archaeal genomes potentially encode, among their many functionally uncharacterized genes, novel enzymes of biotechnological interest. We have developed a prediction method for detection and classification of enzymes from sequence alone (available at http://www.cbs.dtu.dk/services/ArchaeaFun/). The method does not make use of sequence similarity; rather, it relies on predicted protein features like cotranslational and posttranslational modifications, secondary structure, and simple physical/chemical properties.
引用
收藏
页码：2894 / 2898
页数：5
相关论文
共 50 条
  • [41] APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features
    Malik, Adeel
    Kamli, Majid Rasool
    Sabir, Jamal S. M.
    Rather, Irfan A.
    Phan, Le Thi
    Kim, Chang-Bae
    Manavalan, Balachandran
    METHODS, 2024, 229 : 133 - 146
  • [42] Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features
    Zhang, Wen
    Liu, Juan
    Zhao, Meng
    Li, Qingjiao
    INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2012, 6 (05) : 557 - 569
  • [43] Identification of Mammalian Enzymatic Proteins Based on Sequence-Derived Features and Species-Specific Scheme
    Chai, Haiting
    Zhang, Jian
    IEEE ACCESS, 2018, 6 : 8452 - 8458
  • [44] HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information
    Yang, Sen
    Xu, Piao
    ANALYTICAL BIOCHEMISTRY, 2024, 690
  • [45] Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods
    Wen Zhang
    Xiaopeng Zhu
    Yu Fu
    Junko Tsuji
    Zhiping Weng
    BMC Bioinformatics, 18
  • [46] Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods
    Zhang, Wen
    Zhu, Xiaopeng
    Fu, Yu
    Tsuji, Junko
    Weng, Zhiping
    BMC BIOINFORMATICS, 2017, 18
  • [47] Protein fold recognition using sequence-derived predictions
    Fischer, D
    Eisenberg, D
    PROTEIN SCIENCE, 1996, 5 (05) : 947 - 955
  • [48] A novel approach to fold recognition using sequence-derived properties from sets of structurally similar local fragments of proteins
    Hvidsten, Torgeir R.
    Kryshtafovych, Andriy
    Komorowski, Jan
    Fidelis, Krzysztof
    BIOINFORMATICS, 2003, 19 : II81 - II91
  • [49] A machine learning approach for the identification of odorant binding proteins from sequence-derived properties
    Ganesan Pugalenthi
    Ke Tang
    PN Suganthan
    G Archunan
    R Sowdhamini
    BMC Bioinformatics, 8
  • [50] Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features
    Yuan Li
    Mingjun Wang
    Huilin Wang
    Hao Tan
    Ziding Zhang
    Geoffrey I. Webb
    Jiangning Song
    Scientific Reports, 4
12345