Biology subjects

Shukla, V. K.

Publications and source records attributed to Shukla, V. K..

6 recordsLinked to original sources

bioRxiv2025

Two-dimensional NMR from a Single Pulse: Reconstructing Heteronuclear 2D spectra via off-resonance decoupling and Deep Neural Networks

Nuclear Magnetic Resonance (NMR) spectroscopy is ubiquitous in many areas of science, including chemistry, material science, biophysics and structural biology. Heteronuclear two-dimensional (2D) NMR experiments form the basis of several NMR based investigations, especially those involving biomolecules like proteins. However, in solution, as the molecule of interest becomes larger, transverse relaxation times of the spins of interest become shorter. This makes it difficult to record 2D 1H-13C or 1H-15N correlation maps of large protein molecules using standard Fourier-transform based experiments as they contain transfer delays that are long compared to the short relaxation times. Herein, we explore the possibility of leveraging deep neural networks (DNNs) to obtain 2D correlation maps of proteins while eliminating transfer delays from experiments. In this study, we show that 2D methyl 1H-13C correlation maps can be obtained from experiments containing only a single 1H excitation pulse followed by off-resonance 13C continuous-wave decoupling. A DNN was successfully trained to reconstruct the 2D 1H-13C correlation map from datasets recorded with two 13C decoupling fields using protein samples enriched with (ILV) 13CHD2 methyl groups in a 2H background. The efficacy of this strategy is demonstrated by using the DNN to reconstruct methyl 1H-13C correlation maps of the [~]8 kDa FF domain from human HYPA/FBP11, the [~]18 kDa T4 phage lysozyme, as well as on the [~]360 kDa 77 particle from the Thermoplasma acidophilum proteasome. This study illustrates the potential for improving the sensitivity and resolution of NMR spectra using new experiments tailored for use with DNNs. Significance statementMultidimensional heteronuclear solution state nuclear magnetic resonance (NMR) spectroscopy underpins studies of protein dynamics and interactions. However, the experiments have thus far required magnetization-transfer periods and evolution delays, both of which rapidly erode the signal arising from large biomolecules. We introduce here a single-pulse strategy wherein these delays are replaced by off-resonance continuous-wave decoupling so that the two-dimensional correlation map can be reconstructed from the off-resonance decoupling data using a deep neural network. The approach yields high-quality methyl correlation spectra for proteins spanning [~]8-360 kDa. The co-design of NMR experiments along with deep neural networks establishes a general framework to acquire multidimensional NMR spectra using nontraditional strategies that can be extended to other spin systems and higher-dimensional experiments.

biophysics↗

bioRxiv2024

An integrative characterisation of proline cis and trans conformers in a disordered peptide

Intrinsically disordered proteins (IDPs) often contain proline residues, which undergo cis/trans isomerisation. While molecular dynamics (MD) simulations have the potential to fully characterise the proline cis and trans sub-ensembles, they are limited by the slow timescales of isomerisation and force field inaccuracies. Nuclear magnetic resonance (NMR) spectroscopy can report on ensemble-averaged observables for both the cis and trans proline states, but a full atomistic characterisation of these sub-ensembles is challenging. Given the importance of proline cis/trans isomerisation for influencing the conformational sampling of disordered proteins, we employed a combination of all-atom MD simulations with enhanced sampling (metadynamics), NMR, and small-angle X-ray scattering (SAXS) to characterise the two sub-ensembles of the ORF6 C-terminal region (ORF6CTR) from SARS-CoV-2 corresponding to the proline-57 (P57) cis and trans states. We performed MD simulations in three distinct force fields: AMBER03ws, AMBER99SB-disp, and CHARMM36m, which are all optimised for disordered proteins. Each simulation was run for an accumulated time of 180-220 {micro}s until convergence was reached, as assessed by blocking analysis. A good agreement between the cis-P57 populations predicted from metadynamics simulations in AMBER03ws was observed with populations obtained from experimental NMR data. Moreover, we observed good agreement between the radius of gyration predicted from the metadynamics simulations in AMBER03ws and that measured using SAXS. Our findings suggest that both the cis-P57 and trans-P57 conformations of ORF6CTR are extremely dynamic and that interdisciplinary approaches combining both multi-scale computations and experiments offer avenues to explore highly dynamic states that cannot be reliably characterised by either approach in isolation. SIGNIFICANCEThis study employs MD simulations (with metadynamics), NMR spectroscopy, and SAXS to elucidate the individual cis and trans proline conformations of ORF6CTR from SARS-CoV-2. The good agreement on proline cis/trans populations observed in experiments (NMR) and those calculated from simulations in the AMBER03ws force field (with SAXS reweighting) showcases the efficiency of this interdisciplinary approach, which can be used to characterise highly dynamic disordered protein states, even for very slow processes. Furthermore, our study emphasises the importance of considering both computational and experimental methodologies to gain a more holistic understanding of highly dynamic proteins. The presented integrative approach sets a precedent for future studies aiming to explore complex and dynamic biological systems with slow transitions such as proline isomerisations.

biophysics↗

bioRxiv2024

Characterising Aromatic Side Chains in Proteins through the Synergistic Development of NMR Experiments and Deep Neural Networks

Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the structure, dynamics and interactions of macromolecules. While a plethora of NMR methods are now available to inform on backbone and methyl-bearing side-chains of proteins, a characterisation of aromatic side chains is more challenging and often requires specific labelling or 13C-detection. Here we present a deep neural network (DNN) named FID-Net-2, which transforms NMR spectra recorded on simple uniformly 13C labelled samples to yield high-quality 1H-13C correlation spectra of the aromatic side chains. Key to the success of the DNN is the design of a complementary set of NMR experiments that produce spectra with unique features to aid the DNN produce high-resolution aromatic 1H-13C correlation spectra with accurate intensities. The reconstructed spectra can be used for quantitative purposes as FID-Net-2 predicts uncertainties in the resulting spectra. We have validated the new methodology experimentally on protein samples ranging from 7 to 40 kDa in size. We demonstrate that the method can accurately reconstruct high resolution two-dimensional aromatic 1H-13C correlation maps, high resolution three-dimensional aromatic-methyl NOESY spectra to facilitate aromatic 1H-13C assignments, and that the intensities of peaks from the reconstructed aromatic 1H-13C correlation maps can be used to quantitatively characterise the kinetics of protein folding. More generally, we believe that this strategy of devising new NMR experiments specifically for analysis using customised DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.

biophysics↗

bioRxiv2023

Solution State Methyl NMR Spectroscopy of Large Non-Deuterated Proteins Enabled by Deep Neural Networks

Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is arduous and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled, samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing more information at a fraction of the cost. We validated the new methodology experimentally on three proteins with molecular weights in the range 42-360 kDa and further by analysing deep learning-processed NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks were in good agreement with the available structure. The new method represents a substantial advance in the field of using deep learning to analyse complex magnetic resonance data and could have a major impact on the study of large biomolecules in the years to come.

biophysics↗

bioRxiv2023

Small-molecule binding to an intrinsically disordered protein revealed by experimental NMR 19F transverse spin-relaxation

Intrinsically disordered proteins are highly dynamic biomolecules that rapidly interconvert between many structural conformations. Traditionally, these proteins have been considered un-druggable because of their lack of classical long-lived binding pockets. Recent evidence suggests that intrinsically disordered proteins can bind small, drug-like molecules, however, there are limited approaches to characterize these interactions experimentally. Here we demonstrate that ligand-detected 19F transverse relaxation rates (R2) obtained from Nuclear Magnetic Resonance spectroscopy are highly sensitive to the interaction between a small-molecule and an intrinsically disordered protein, in contrast to chemical shift perturbations which are minimally sensitive for this interaction. With this method, we show that the small molecule, 5-fluoroindole, interacts with the disordered domains of non-structural protein 5A from hepatitis C virus with a Kd of 260 {+/-} 110 M. We also demonstrate that 5-fluoroindole remains highly dynamic in the bound form. Our findings suggest that ligand-detected 19F transverse relaxation measurements could represent a highly effective screening strategy to identify molecules capable of interacting with these traditionally elusive, dynamic biomolecules.

biophysics↗

bioRxiv2021

Implications of critical nodes-dependent unidirectional cross-talk between Plasmodium and Human SUMO

The endoparasitic pathogen, Plasmodium falciparum (Pf), modulates protein-protein interactions to employ post-translational modifications like SUMOylation in order to establish successful infections. The interaction between E1 and E2 (Ubc9) enzymes governs species specificity in the Plasmodium SUMOylation pathway. Here, we demonstrate that a unidirectional cross-species interaction exists between Pf-SUMO and Human-E2, whereas Hs-SUMO1 failed to interact with Pf-E2. Biochemical and biophysical analysis revealed that surface-accessible Aspartates of Pf-SUMO determine the efficacy and specificity of SUMO-Ubc9 interactions. Furthermore, we demonstrate that critical residues of the Pf-Ubc9 N-terminal are responsible for the lack of interaction between Hs-SUMO1 and Pf-Ubc9. Mutating these residues to corresponding Hs-Ubc9 residues restore electrostatic, {pi}-{pi}, and hydrophobic interactions and allows efficient cross-species interactions. We suggest that the critical changes acquired on the surfaces of Plasmodium SUMO and Ubc9 proteins as nodes can help Plasmodium exploit the host SUMOylation machinery. Thus, Pf-SUMO interactions can be targeted for developing antimalarials.

biochemistry↗