Biology subjects

Vallurupalli, P.

Publications and source records attributed to Vallurupalli, P..

4 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

Increasing the accuracy of exchange parameters reporting on slow dynamics by performing CEST experiments with high B1 fields

Over the last decade chemical exchange saturation transfer (CEST) NMR methods have emerged as powerful tools to characterize biomolecular conformational dynamics occurring between a visible major state and invisible minor states. The ability of the CEST experiment to detect these minor states, and provide precise exchange parameters, hinges on using appropriate B1 field strengths during the saturation period. Typically, a pair of B1 fields with{omega} 1 (= 2{pi}B1) values around the exchange rate kex are chosen. Here we show that the transverse relaxation rate of the minor state resonance (R2,B) also plays a crucial role in determining the B1 fields that lead to the most informative datasets. Using [Formula], to guide the choice of B1, instead of kex, leads to data wherefrom substantially more accurate exchange parameters can be derived. The need for higher B1 fields, guided by K, is demonstrated by studying the conformational exchange in two mutants of the 71 residue FF domain with kex [~]11 s-1 and [~]72 s-1, respectively. In both cases analysis of CEST datasets recorded using B1 field values guided by kex lead to imprecise exchange parameters, whereas using B1 values guided by K resulted in precise site-specific exchange parameters. The conclusions presented here will be valuable while using CEST to study slow processes at sites with large intrinsic relaxation rates, including carbonyl sites in small to medium sized proteins, amide 15N sites in large proteins and when the minor state dips are broadened due to exchange among the minor states.

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↗

bioRxiv2018

Atomic Resolution Mechanism of Ligand Binding to a Solvent Inaccessible Cavity in T4 Lysozyme

Ligand binding sites in proteins are often localized to deeply buried cavities, inaccessible to bulk solvent. Yet, in many cases binding of cognate ligands occurs rapidly. An intriguing system is presented by the L99A cavity mutant of T4 Lysozyme (L99A T4L) that rapidly binds benzene (~106 M-1 s-1). Although the protein has long served as a model system for protein thermodynamics and crystal structures of both free and benzene-bound L99A T4L are available, the kinetic pathways by which benzene reaches its solvent-inaccessible binding cavity remain elusive. The current work, using extensive molecular dynamics simulation, achieves this by capturing the complete process of spontaneous recognition of benzene by L99A T4L at atomistic resolution. A series of multi-microsecond unbiased molecular dynamics simulation trajectories unequivocally reveal how benzene, starting in bulk solvent, diffuses to the protein and spontaneously reaches the solvent inaccessible cavity of L99A T4L. The simulated and high-resolution X-ray derived bound structures are in excellent agreement. A robust four-state Markov model, developed using cumulative 60 {micro}s trajectories, identifies and quantifies multiple ligand binding pathways with low activation barriers. Interestingly, none of these identified binding pathways required large conformational changes for ligand access to the buried cavity. Rather, these involve transient but crucial opening of a channel to the cavity via subtle displacements in the positions of key helices (helix4/helix6, helix7/helix9) leading to rapid binding. Free energy simulations further elucidate that these channel-opening events would have been unfavorable in otherwise ligand-inactive wild type T4L. Taken together, by integrating experiments, these simulations provide unprecedented mechanistic insights into complete ligand recognition process in a buried cavity. By illustrating the power of subtle helix movements in opening up multiple pathways for ligand access, this work offers an alternate view of ligand recognition mechanism in a solvent-inaccessible cavity, contrary to common perception of single dominant pathway for ligand binding.

biophysics↗