Sculpting conducting nanopore size and shape through de novo protein design

Water, Solute, and Ion Transport in De Novo-Designed Membrane Protein Channels

Biological organisms engineer peptide sequences to fold into membrane pore proteins capable of performing a wide variety of transport functions. Synthetic de novo-designed membrane pores can mimic this approach to achieve a potentially even larger set of functions. Here we explore water, solute, and ion transport in three de novo designed β-barrel membrane channels in the 5-10 Å pore size range. We show that these proteins form passive membrane pores with high water transport efficiencies and size rejection characteristics consistent with the pore size encoded in the protein structure. Ion conductance and ion selectivity measurements also show trends consistent with the pore size, with the two larger pores showing weak cation selectivity. MD simulations of water and ion transport and solute size exclusion are consistent with the experimental trends and provide further insights into structure-function correlations in these membrane pores.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science.

Bottom-up design of calcium channels from defined selectivity filter geometry

Native ion channels play key roles in biological systems, and engineered versions are widely used as chemogenetic tools and in sensing devices1,2. Protein design has been harnessed to generate pore-containing transmembrane proteins, but the capability to design ion selectivity based on the interactions between ions and selectivity filter residues, a crucial feature of native ion channels3, has been constrained by the lack of methods to place the metal-coordinating residues with atomic-level precision. Here we describe a bottom-up RFdiffusion-based approach to construct Ca2+ channels from defined selectivity filter residue geometries, and use this approach to design symmetric oligomeric channels with Ca2+ selectivity filters having different coordination numbers and different geometries at the entrance of a wide pore buttressed by multiple transmembrane helices. The designed channel proteins assemble into homogenous pore-containing particles, and for both tetrameric and hexameric ion-coordinating configurations, patch-clamp experiments show that the designed channels have higher conductances for Ca2+ than for Na+ and other divalent ions (Sr2+ and Mg2+). Cryo-electron microscopy indicates that the design method has high accuracy: the structure of the hexameric Ca2+ channel is nearly identical to the design model. Our bottom-up design approach now enables the testing of hypotheses relating filter geometry to ion selectivity by direct construction, and provides a roadmap for creating selective ion channels for a wide range of applications.

Hetero-Oligomeric Protein Pores for Single-Molecule Sensing

Protein nanopores are emerging as versatile single-molecule sensors with broad applications in DNA and protein sequencing. However, their narrow size restricts the range of detectable analytes, necessitating the development of advanced nanopores to broaden their applications in biotechnology. This review highlights a natural hetero-oligomeric porin, Nocardia farcinica porin AB (NfpAB), based on the Gram-positive mycolata, Nocardia farcinica. The pore comprises two subunits, NfpA and NfpB, that combine to form a stable structure with a unique pore geometry, asymmetrical shape, and charge distribution. Single-channel electrical recordings demonstrate that NfpAB forms stable, high-conductance channels suitable for sensing charged molecules, particularly cationic polypeptides and cyclic sugars. This pore offers advantages such as enhanced control over molecular interactions due to densely crowded charged residues, thus allowing the quantification of voltage-dependent translocation kinetics. Notably, NfpAB contains intrinsic cysteines in the pore lumen, providing an accessible site for thiol-based reactions and attachment of molecular adapters. We propose that such hetero-oligomeric pores will be effective for several applications in nanopore technology for biomolecular detection and sequencing.

Re-engineering of a carotenoid-binding protein based on NMR structure

Recently, a number of message passing neural network (MPNN)-based methods have been introduced that, based on backbone atom coordinates, efficiently recover native amino acid sequences of proteins and predict modifications that result in better expressing, more soluble, and stable variants. However, usually, X-ray structures, or artificial structures generated by algorithms trained on X-ray structures, were employed to define target backbone conformations. Here, we show that commonly used algorithms ProteinMPNN and SolubleMPNN display low sequence recovery on structures determined using NMR. We subsequently propose a computational approach that we successfully apply to re-engineer AstaP, a protein that natively binds a large hydrophobic ligand astaxanthin (C40H52O4), and for which only a structure determined using NMR is currently available. The engineered variants, designated NeuroAstaP, are 51 amino acid shorter than the 22 kDa parent protein, have 38%-42% sequence identity to it, exhibit good yields, are expressed in a soluble, mostly monomeric form, and demonstrate efficient binding of carotenoids in vitro and in cells. Altogether, our work further tests the limits of using machine learning for protein engineering and paves the way for MPNN-based modification of proteins based on NMR-derived structures.

Nanopore sequencing: flourishing in its teenage years

Over the past decade, nanopore sequencing has experienced significant advancements and changes, transitioning from an initially emerging technology to a significant instrument in the field of genomic sequencing. However, as advancements in next-generation sequencing technology persist, nanopore sequencing also improves. This paper reviews the developments, applications, and outlook on nanopore sequencing technology. Currently, nanopore sequencing supports both DNA and RNA sequencing, making it widely applicable in areas such as telomere-to-telomere (T2T) genome assembly, direct RNA sequencing (DRS), and metagenomics. The openness and versatility of nanopore sequencing have established it as a preferred option for an increasing number of research teams, signaling a transformative influence on life science research. As the nanopore sequencing technology advances, it provides a faster, more cost-effective approach with extended read lengths, demonstrating the significant potential for complex genome assembly, pathogen detection, environmental monitoring, and human disease research, offering a fresh perspective in sequencing technologies.

Binding and sensing diverse small molecules using shape-complementary pseudocycles

We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.