Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analyzing protein unfolding data (traces) coming from preparations with heterogeneous composition (e.g. where different proteins are present in the sample). An automatic procedure able to distinguish the unfolding patterns of the proteins is needed. Here, we introduce a data analysis pipeline able to recognize in such datasets traces with recurrent patterns (clusters).

Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples / Ilieva, Nina I; Galvanetto, Nicola; Allegra, Michele; Brucale, Marco; Laio, Alessandro. - In: BIOINFORMATICS. - ISSN 1367-4803. - 36:20(2020), pp. 5014-5020. [10.1093/bioinformatics/btaa626]

Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples

Galvanetto, Nicola;Allegra, Michele;Laio, Alessandro
2020

Abstract

Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analyzing protein unfolding data (traces) coming from preparations with heterogeneous composition (e.g. where different proteins are present in the sample). An automatic procedure able to distinguish the unfolding patterns of the proteins is needed. Here, we introduce a data analysis pipeline able to recognize in such datasets traces with recurrent patterns (clusters).
36
20
5014
5020
Ilieva, Nina I; Galvanetto, Nicola; Allegra, Michele; Brucale, Marco; Laio, Alessandro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/116137
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