AIMC Topic: Nanopores
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Binding and sensing diverse small molecules using shape-complementary pseudocycles.
Science (New York, N.Y.)
Jul 18, 2024
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 sha...
Effective training of nanopore callers for epigenetic marks with limited labelled data.
Open biology
Jun 12, 2024
Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5-methylcytosine (5mC) and N6-methyladenine (6mA). These ML-based nanopore callers have typically been tra...
Synergistic Machine Learning Accelerated Discovery of Nanoporous Inorganic Crystals as Non-Absorbable Oral Drugs.
Advanced materials (Deerfield Beach, Fla.)
Jun 6, 2024
Machine learning (ML) has taken drug discovery to new heights, where effective ML training requires vast quantities of high-quality experimental data as input. Non-absorbable oral drugs (NODs) have unique safety advantage for chronic diseases due to ...
Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.
RNA biology
May 17, 2024
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...
A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing.
Nature communications
Feb 16, 2024
Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequenc...
RUBICON: a framework for designing efficient deep learning-based genomic basecallers.
Genome biology
Feb 16, 2024
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later step...
Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore.
ACS nano
Dec 19, 2023
Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both spatial and temporal scales. Protein PTMs have also emerged as reliable biomarkers for several dis...
Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking.
The journal of physical chemistry letters
Oct 2, 2023
Brain-inspired neuromorphic computing is currently being investigated for effective artificial intelligence (AI) systems. The development of artificial neurons and synapses is imperative to creating efficient computational biomimetic networks. Here w...
Machine Learning Assisted Simultaneous Structural Profiling of Differently Charged Proteins in a Porin A (MspA) Electroosmotic Trap.
Journal of the American Chemical Society
Jan 7, 2022
The nanopore is emerging as a means of single-molecule protein sensing. However, proteins demonstrate different charge properties, which complicates the design of a sensor that can achieve simultaneous sensing of differently charged proteins. In this...
Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network.
ACS nano
Aug 17, 2021
Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because ...
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