Simultaneous prediction of the molecular response properties, such as polarizability and the NMR shielding constant, at a low computational cost is an unresolved issue. We propose to combine a linear-scaling generalized energy-based fragmentation (GEBF) method and deep learning (DL) with both molecular and atomic information-theoretic approach (ITA) quantities as effective descriptors. In GEBF, the total molecular polarizability can be assembled as a linear combination of the corresponding...
[NMR paper] Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction
Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction
Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative...
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11-23-2023 10:09 AM
[NMR paper] Rapid estimation approach for glycosylated serum protein of human serum based on the combination of deep learning and TD-NMR technology
Rapid estimation approach for glycosylated serum protein of human serum based on the combination of deep learning and TD-NMR technology
Rapid and precise estimation of glycosylated serum protein (GSP) of human serum is of great importance for the treatment and diagnosis of diabetes mellitus. In this study, we propose a novel method for estimation of GSP level based on the combination of deep learning and time domain nuclear magnetic resonance (TD-NMR) transverse relaxation signal of human serum. Specifically, a principal component analysis (PCA)-enhanced one-dimensional convolutional...
[NMR paper] Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Related Articles Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Comput Struct Biotechnol J. 2020;18:603-611
Authors: Fino R, Byrne R, Softley CA, Sattler M, Schneider G, Popowicz GM
Abstract
NMR-based screening, especially fragment-based drug discovery is a...
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04-09-2020 05:35 AM
[NMR analysis blog] Learning NMR with Deep Purple
Learning NMR with Deep Purple
It’s not that I’ve gone crazy (well, I hope not :-) ) or that Deep Purple has moved from Hard Rock to Science (or at least I‘m not aware of this), but after my last post about the acoustic reproduction of NMR FIDS, I thought that it would be fun to compose a simple song and, at the same time, create some stuff which can serve as an educational tool for some very basic NMR.
I won’t actually compose anything original, but rather make an NMR cover of the famous Smoke on the Water riff by Deep Purple. It’s very simple with a central theme consisting in a...
Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates
Linear Mode
