arXiv subjects

Tatiana Agback

Publications and source records attributed to Tatiana Agback.

6 recordsLinked to original sources

arXiv2026

FOSY: Segmental Backbone Assignment in Intrinsically Disordered Proteins

Backbone resonance assignment is a prerequisite for most biomolecular NMR applications, yet conventional multidimensional strategies frequently fail for intrinsically disordered proteins (IDPs) and regions (IDRs) because of severe spectral overlap, rapid amide proton exchange with water, and missing sequential correlations. In many biological applications, however, complete protein assignment is unnecessary, as only a limited sequence segment surrounding a functional site is required. Here we introduce segmental backbone assignment, an assignment strategy implemented by FOcused SpectroscopY (FOSY), which concentrates experimental effort on relatively short regions while retaining the high-dimensional sequential connectivity needed for unambiguous assignments. We present a self-consistent suite of selective two-dimensional FOSY experiments that enables bidirectional assignment walks along the protein sequence through complementary forward and backward transfer schemes. The methodology employs frequency-selective polarisation transfer to replace high-dimensional experiments with sensitive and readily interpretable 2D spectra while preserving the information content of multidimensional correlation experiments. The approach is demonstrated by completing the assignment of G302-K311 segment, which is missing in the published assignment of the 441-residue human Tau protein. The approach complements conventional multidimensional or residue type-selective assignment strategies by providing an efficient means of traversing assignment interruptions and rapidly characterising functionally important segments in intrinsically disordered proteins.

physics.chem-ph↗

arXiv2025

Towards Ultimate NMR Resolution with Deep Learning

In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations ($P^3$)- a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood of a peak maximum occurring at that location. The mapping between the spectrum and $P^3$ is achieved using MR-Ai, a physics-inspired deep learning neural network architecture, designed to handle multidimensional NMR spectra. Furthermore, we demonstrate that MR-Ai enables coprocessing of multiple spectra, facilitating direct information exchange between datasets. This feature significantly enhances spectral quality, particularly in cases of highly sparse sampling. Performance of MR-Ai and high value of the $P^3$ are demonstrated on the synthetic data and spectra of Tau, MATL1, Calmodulin, and several other proteins.

physics.bio-ph↗

arXiv2022

Resolution enhancement of NMR by decoupling with low-rank Hankel model

Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine. Although J-coupling carries essential structural information it may also limit the spectral resolution. Homonuclear decoupling remains a challenging problem. In this work, we introduce a new approach that uses a specific coupling value as prior knowledge, and Hankel property of exponential NMR signal to achieve the broadband heteronuclear decoupling using the low-rank method. Our results on synthetic and realistic HMQC spectra demonstrate that the proposed method not only effectively enhances resolution by decoupling, but also maintains sensitivity and suppresses spectral artefacts. The approach can be combined with the non-uniform sampling, which means that the resolution can be further improved without any extra acquisition time

q-bio.BM↗

arXiv2022

NMR spectrum reconstruction as a pattern recognition problem

A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of the corresponding point spread function (PSF) pattern produced by each spectral peak resulting in the highest quality and robust reconstruction of the NUS spectra as demonstrated in simulations and exemplified in this work on 2D 1H-15N correlation spectra of three representative globular proteins with different sizes: Ubiquitin (8.6 kDa), Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also demonstrated for successful virtual homo-decoupling in a 2D methyl 1H-13 HMQC spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS schedule, which so far was not fully exploited, can be used for designing new powerful NMR processing techniques that surpass the existing algorithmic methods.

q-bio.BM↗

arXiv2022

A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy

The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled exponentials is highly expected in general signal processing and many applications. Deep learning has shown astonishing potential in this field but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining merits of the sparse model-based optimization method and data-driven deep learning, we propose a deep learning architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultra-fast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications.

cs.LG↗

arXiv2019

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.

physics.med-ph↗