AI Insight
Researchers developed a single-molecule nanopore tweezer platform that can classify different binding modes of kinase inhibitors without requiring traditional structural biology methods like X-ray crystallography. The technique successfully distinguished three types of ATP-competitive inhibitors binding to Abl kinase by detecting unique ionic current signatures, achieving 97.5% classification accuracy using machine learning analysis. The method also revealed how clinically important Abl mutations alter inhibitor binding patterns and could identify inhibitor mixtures at nanomolar concentrations.
Why it matters
This technology could accelerate drug discovery and personalized cancer treatment by providing a faster, label-free method to characterize how kinase inhibitors bind to their targets and how mutations cause drug resistance. The approach offers a high-throughput alternative to conventional structural methods while maintaining sub-angstrom resolution, potentially enabling rapid screening of inhibitor libraries and patient-specific resistance profiling.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Accurate determination of kinase inhibitor binding modes could provide essential information for understanding resistance mechanisms and accelerating drug discovery. While conventional structural methods such as X-ray crystallography, cryo-EM and NMR provide high-resolution information but are low-throughput and capture largely static snapshots of dynamic protein-ligand interactions Here, we introduce a single-molecule nanopore tweezer platform that functionally subtypes ATP-competitive Abl kinase inhibitors by resolving distinct ionic current signatures of Abl-inhibitor complexes. This approach distinguishes Type I, Type IIA, and Type IIB inhibitors without structural determination. We further show how clinically relevant Abl variants (T315I and E255V) reshape inhibitor engagement and binding modes. By combining baseline probability features with wavelet-based time-frequency descriptors, ensemble machine-learning models achieved 97.5% classification accuracy across seven kinase inhibitor binding modes at sub-angstrom resolution and enabled deconvolution of mixed-inhibitor samples at nanomolar concentrations. These results establish nanopore tweezers as a label-free, super-resolution platform for profiling kinase conformational states and inhibitor binding modes, complementing structural approaches and supporting precision oncology.
Source: Subtyping active-site inhibitor binding mode to Abl kinase using super-resolution nanopore tweezers.