Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples.
Bioinformatics
; 36(20): 5014-5020, 2020 12 22.
Article
em En
| MEDLINE
| ID: mdl-32653898
ABSTRACT
MOTIVATION Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analysing 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). RESULTS:
We illustrate the performance of our method on two prototypical datasets â¼50 000 traces from a sample containing tandem GB1 and â¼400 000 traces from a native rod membrane. Despite a daunting signal-to-noise ratio in the data, we are able to identify several unfolding clusters. This work demonstrates how an automatic pattern classification can extract relevant information from SMFS traces from heterogeneous samples without prior knowledge of the sample composition. AVAILABILITY AND IMPLEMENTATION https//github.com/ninailieva/SMFS_clustering. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Imagem Individual de Molécula
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Itália