Leveraging nature’s biomolecular designs in next-generation protein sequencing reagent development

Tullman, Jennifer; Marino, John P.; Kelman, Zvi

doi:10.1007/s00253-020-10745-2

Cited by 8 publications

(3 citation statements)

References 87 publications

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“…Several natural proteins and RNA molecules recognize specific amino acids either as free amino acids or as a part of a polypeptide chain 12 . These proteins and nucleic acids provide different solutions for N-terminal amino acid recognition.…”

Section: Sequence Coverage In Global Proteomics Studies Ms-based Globalmentioning

confidence: 99%

The emerging landscape of single-molecule protein sequencing technologies

et al. 2021

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Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.

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Section: Sequence Coverage In Global Proteomics Studies Ms-based Globalmentioning

confidence: 99%

The emerging landscape of single-molecule protein sequencing technologies

et al. 2021

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“…Then, by sequentially degrading the peptide using Edman chemistry, or direct imaging using single-molecule fluorescence resonance energy transfer (FRET), the relative position of labeled amino acids can be deduced from the fluorescent signals [12][13][14] . Additionally, fluorophore-labeled amino-terminal recognizers of amino acids have been engineered to bind specific amino acids reversibly 15,16 . The repetitive signals of the same amino acid can greatly improve the accuracy of single-molecule peptide identification 17 .…”

Section: Articlementioning

confidence: 99%

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

Zhang,

Tang,

Wang

et al. 2024

Nat Methods

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Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer’s disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

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“…Advancements in protein identification and quantification are much sought after by researchers in the field of single molecule protein sequencing. SMPS technologies apply concepts from DNA and RNA sequencing to protein analysis-including imaging or nanopore-based assays, high parallelism, and single molecule detection-with the hope of achieving the high throughput and sensitivity offered by such approaches [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Estimating error rates for single molecule protein sequencing experiments

Smith,

VanderVelden,

Blom

et al. 2023

Preprint

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The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently, which should help prevent overfitting. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

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Leveraging nature’s biomolecular designs in next-generation protein sequencing reagent development

Cited by 8 publications

References 87 publications

The emerging landscape of single-molecule protein sequencing technologies

The emerging landscape of single-molecule protein sequencing technologies

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

Estimating error rates for single molecule protein sequencing experiments

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