Minji Chung, Sohee Oh, Jin-Sik Kim, Sungbo Hwang, Ki-Baek Jeong, Seung-Wook Chi
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引用次数: 0
Abstract
Resolving cancer-associated protein variants remains difficult when variants share similar molecular masses and folded structures. Conventional assays often require labels or provide ensemble-averaged readouts, limiting their ability to resolve subtle variant differences. Here, a YaxAB nanopore workflow is presented for label-free single-molecule current fingerprinting of wild-type Bcl-xL and the cancer-associated single-residue variants N136K and R165W. Individual Bcl-xL proteins were electro-osmotically trapped within the funnel-shaped YaxAB lumen, where they generated characteristic ionic-current events. These events formed reproducible, analyte-specific event populations in normalized current blockade and current noise space, resolving wild-type Bcl-xL, N136K, and R165W as distinct single-molecule current fingerprints. Wild-type Bcl-xL and N136K remained distinct despite a mass difference of only 14 Da, and the fingerprint separation was consistent with combined differences in excluded volume, net charge, and dipole moment. Concentration-dependent capture-rate analysis further supported sub-nanomolar to low-nanomolar detection corresponding to protein amounts in the sub-picomole to low-picomole range, while capture-rate normalization brought the estimated concentrations closer to the nominal concentrations. These results present YaxAB nanopore sensing as a label-free single-molecule workflow for the current fingerprinting of cancer-associated single-residue Bcl-xL variants, with low-concentration detection as a supporting analytical capability.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.