Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data.

ABSTRACT:

Motivation

Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs.

Results

Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PT

SUBMITTER: Khater IM 

PROVIDER: S-EPMC6748737 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature