Sculpting conducting nanopore size and shape through de novo protein design

Paper

Abstract

Transmembrane β-barrels have considerable potential for a broad range of sensing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels, which provide suboptimal starting points. By contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to designing transmembrane β-barrel pores with different diameters and pore geometries. Nuclear magnetic resonance and crystallographic characterization show that the designs are stably folded with structures resembling those of the design models. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 picosiemens (~0.5 nanometer pore diameter) to 430 picosiemens (~1.1 nanometer pore diameter). Our approach opens the door to the custom design of transmembrane nanopores for sensing and sequencing applications.

Analysis

Paper intelligence

Summary

This paper presents a de novo protein design approach to create transmembrane β-barrel pores with tunable diameters and geometries for sensing applications.

Key findings

Successfully designed and characterized novel transmembrane β-barrel pores.
Designed pores exhibit distinct conductances correlating with pore diameter.
Demonstrated a general approach for custom nanopore design.

Methods

De novo protein design of transmembrane β-barrels.
Nuclear magnetic resonance (NMR) characterization.
Crystallographic characterization.
Conductance measurements.

Limitations

The abstract does not explicitly state limitations of the current study.

Applications

Sensing applications.
Nanopore sequencing.

Confidence100%

Discovery

Related papers

2026Crossref2 citations

AI Guided Protein Design for Next‐Generation Autogenic Engineered Living Materials

Hoda M. Hammad; Anna M. Duraj‐Thatte

Apr 1, 2026Advanced Intelligent Systems

2026Crossref2 citations

<i>De novo</i> protein–ligand design including protein flexibility and conformational adaptation

Jakob Agamia; Martin Zacharias

Feb 3, 2026Bioinformatics

2026PubMed

ScrambleBench: a workflow for comparative assessment of structure-based de novo generative models.

Yap V; Xu P; Mak FS; Foo K; Kang C; Anbazhagan P; Xu W

Jan 1, 2026Journal of cheminformatics

2025Crossref3 citations

AI and Quantum Computing for Advanced Materials Design

Hun Hee Cho; Tae Hyung Kim; Seung Gyu Hwang; Hongchul Shin

Oct 21, 2025Journal of AI-Driven Communication Engineering

2025OpenAlex36 citations

Strengthening nucleic acid biosecurity screening against generative protein design tools

Bruce J. Wittmann; Tessa Alexanian; Craig Bartling; Jacob Beal; Adam Clore; James Diggans; Kevin Flyangolts; Bryan T. Gemler; Tom Mitchell; Steven T. Murphy; Nicole E. Wheeler; Eric Horvitz

Oct 2, 2025Science

2025Crossref14 citations

The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe

Guohao Zhang; Chuanyang Liu; Jiajie Lu; Shaowei Zhang; Lingyun Zhu

Sep 15, 2025Biology

Source record

Publication metadata

Authors: Samuel Berhanu; Sagardip Majumder; Thomas Müntener; James Whitehouse; Carolin Berner; Asim K. Bera; Alex Kang; Binyong Liang; Nasir Khan; Banumathi Sankaran; Lukas K. Tamm; David J. Brockwell; Sebastian Hiller; Sheena E. Radford; David Baker; Anastassia A. Vorobieva
Published: Jul 18, 2024
Journal or venue: Science
DOI: 10.1126/science.adn3796
Paper type: journal article
Source: Openalex

DOI OpenAlex