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Gunnar von Heijne
Gunnar von Heijne
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Title
Cited by
Cited by
Year
Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes
A Krogh, B Larsson, G Von Heijne, ELL Sonnhammer
Journal of molecular biology 305 (3), 567-580, 2001
142732001
Tissue-based map of the human proteome
M Uhlén, L Fagerberg, BM Hallström, C Lindskog, P Oksvold, ...
Science 347 (6220), 1260419, 2015
141622015
SignalP 4.0: discriminating signal peptides from transmembrane regions
TN Petersen, S Brunak, G Von Heijne, H Nielsen
Nature methods 8 (10), 785-786, 2011
101632011
Improved prediction of signal peptides: SignalP 3.0
JD Bendtsen, H Nielsen, G Von Heijne, S Brunak
Journal of molecular biology 340 (4), 783-795, 2004
79742004
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
H Nielsen, J Engelbrecht, S Brunak, G Von Heijne
Protein engineering 10 (1), 1-6, 1997
67921997
A new method for predicting signal sequence cleavage sites
G Von Heijne
Nucleic acids research 14 (11), 4683-4690, 1986
53701986
Predicting subcellular localization of proteins based on their N-terminal amino acid sequence
O Emanuelsson, H Nielsen, S Brunak, G Von Heijne
Journal of molecular biology 300 (4), 1005-1016, 2000
51932000
Locating proteins in the cell using TargetP, SignalP and related tools
O Emanuelsson, S Brunak, G Von Heijne, H Nielsen
Nature protocols 2 (4), 953-971, 2007
38162007
A hidden Markov model for predicting transmembrane helices in protein sequences.
ELL Sonnhammer, G Von Heijne, A Krogh
Ismb 6, 175-182, 1998
33011998
Signal sequences: the limits of variation
G Von Heijne
Journal of molecular biology 184 (1), 99-105, 1985
27761985
SignalP 5.0 improves signal peptide predictions using deep neural networks
JJ Almagro Armenteros, KD Tsirigos, CK Sønderby, TN Petersen, ...
Nature biotechnology 37 (4), 420-423, 2019
27412019
Patterns of amino acids near signal‐sequence cleavage sites
G Von Heijne
European journal of biochemistry 133 (1), 17-21, 1983
26751983
SignalP 5.0 improves signal peptide predictions using deep neural networks
JJA Armenteros, KD Tsirigos, CK Sønderby, TN Petersen, O Winther, ...
Nature biotechnology 37, 420-423, 2019
26182019
ChloroP, a neural network‐based method for predicting chloroplast transit peptides and their cleavage sites
O Emanuelsson, H Nielsen, GV Heijne
Protein Science 8 (5), 978-984, 1999
21591999
Membrane protein structure prediction: hydrophobicity analysis and the positive-inside rule
G Von Heijne
Journal of molecular biology 225 (2), 487-494, 1992
21291992
Genome‐wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms
E Wallin, GV Heijne
Protein Science 7 (4), 1029-1038, 1998
21081998
The signal peptide
G von Heijne
The Journal of membrane biology 115, 195-201, 1990
14511990
Domain structure of mitochondrial and chloroplast targeting peptides
G von HEIJNE, J Steppuhn, RG Herrmann
European Journal of Biochemistry 180 (3), 535-545, 1989
13941989
SignalP 6.0 predicts all five types of signal peptides using protein language models
F Teufel, JJ Almagro Armenteros, AR Johansen, MH Gíslason, SI Pihl, ...
Nature biotechnology 40 (7), 1023-1025, 2022
13772022
Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method.
M Cserzö, E Wallin, I Simon, G von Heijne, A Elofsson
Protein engineering 10 (6), 673-676, 1997
13711997
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