Skip to content

Label-Free YaxAB Nanopore Fingerprinting of Cancer-Associated Single-Residue Bcl-xL Variants.

Oct 2026 · Small Methods · pp. e71126 · 0 citations
Medicine

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.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.