Biology subjects

Karunanithy, G.

Publications and source records attributed to Karunanithy, G..

7 recordsLinked to original sources

bioRxiv2026

SpinForecast: chain-free probabilistic backbone assignment of intrinsically disordered proteins from NMR chemical shifts

Assigning peaks in NMR spectra to specific residues is an essential but time-consuming step in the study of intrinsically disordered proteins (IDPs). Conventional approaches rely on building chains of sequential connectivities between peaks, which are particularly prone to failure in disordered systems due to spectral overlap, missing peaks, and proline-rich sequences. Here we present SpinForecast, a tool that performs chain-free probabilistic backbone assignment of IDPs from chemical shifts alone, without requiring peaks to be linked into sequential chains. SpinForecast predicts residue-specific chemical shift distributions from a disorder-filtered subset of the Biomolecular Magnetic Resonance Data Bank (BMRB), incorporating nearest-neighbour residue effects and corrections for temperature and pH. Experimental chemical shifts are then assigned to residues by Bayes theorem, using residue-specific chemical shift distributions as likelihoods. We validate SpinForecast on three disordered proteins, IAPP (37 residues), NUPR1 (82 residues), and JPT2 (218 residues), achieving 100% confidence assignments for 74%, 57%, and 44% of in-distribution peaks, respectively, all with at least 99% accuracy. Where single assignments cannot be determined for these systems, the correct assignment was contained within the returned set of candidate assignments in greater than 97% of cases. SpinForecast is freely available at https://tools.bindresearch.org/bindbox/SpinForecast.

biophysics↗

bioRxiv2025

Mass photometry reveals stoichiometry and binding dynamics of bispecific tetravalent anti-VEGF-PD-1 antibody ivonescimab

BackgroundThe bispecific antibody ivonescimab targets programmed cell death protein 1 (PD-1) and vascular endothelial growth factor (VEGF). Recent clinical trials have shown it has greater efficacy against PD-L1 positive non-small cell lung cancer than pembrolizumab (Keytruda), a frequently prescribed anti-PD-1 monoclonal antibody. Ivonescimab binds to two VEGF and two PD-1 molecules, with complex formation through higher-order structure formation (or daisy chain binding). However, the binding stoichiometries and interaction dynamics of ivonescimab with VEGF and PD-1 have not been characterized in depth. MethodsWe used mass photometry (MP) and kinetic modelling to analyze these interactions, quantifying the complexes formed and their affinities. Dissociation constants (KD) for ivonescimabs binding to VEGF and PD-1 were calculated from equilibrium counts and real-time measurements, respectively. ResultsVEGF drove oligomerization of ivonescimab, which bound VEGF predominantly in a 2:2 stoichiometry, with KD=0.08 nM. Higher-order oligomeric complexes, present only at low abundance, displayed markedly weaker affinities (3.17 nM; 1.29 nM). Ternary complexes of ivonescimab with its two targets consistently presented two PD-1 antigens for each ivonescimab molecule, with a 1.66 nM KD for the binding of the first PD-1 and the slightly stronger 0.89 nM for the second PD-1 molecule. ConclusionsMP confirmed VEGF-induced ivonescimab oligomerization and revealed that dimers, not higher-order structures, were the most stable stoichiometry. MP enables detailed analysis of antibody-antigen interactions, even for bispecific antibodies that interact with antigens with complex stoichiometries. Statement of significanceWe report the first application of mass photometry to characterize the binding of the clinically promising bispecific antibody ivonescimab to its targets, VEGF and PD-1. The most stable assembly is a dimeric complex, not higher-order, as expected - advancing understanding of ivonescimab and demonstrating mass photometrys value for complex biologics analysis.

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↗

bioRxiv2024

Seedless: On-the-fly pulse calculation for NMR experiments

All NMR experiments require sequences of RF pulses to manipulate nuclear spins. Signal is lost due to non-uniform excitation of nuclear spins resonating at different energies (chemical shifts) and inhomogeneity in the RF actually generated by hardware over the sample volume. Here, we present Seedless, a tool to calculate NMR pulses that compensate for these effects to enhance control of magnetisation and boost signal. The calculations take only a few seconds using an optimised GRadient Ascent Pulse Engineering (GRAPE) implementation, allowing pulses to be generated on-the-fly, optimised for individual samples and spectrometers. Each calculated pulse requires bands of chemical shift to be identified, over which one of 4 transforms will be performed, selected from a set that covers all commonly used applications: a universal rotation (e.g. 90{degrees} about the x axis), including as a special case an identity operation (return spins in the same state as they started), state-to-state (e.g. Z->Y), an XYcite (Z->XY plane), or a novel type, a suppression, that leaves spins minimally perturbed at all times during the pulse. Using imaging experiments we demonstrate our pulses effectively increase the size of the coil volume and effectively increase signal-to-noise in all experiments. We illustrate the approach by devising ultra-broadband pulses (300 ppm excitation pulse for 19F 1D spectra), a 15N HSQC with 58% increased S/N (950 MHz spectrometer + cryoprobe), triple resonance biomolecular NMR experiments such as HNCACO with 55% increased S/N (600 MHz spectrometer + RT probe), and a highly efficient pulse sequence for water suppression. The 8 optimised pulse sequences presented required 54 bespoke pulses all calculated on-the-fly. Seedless provides a means to enhance sensitivity in all pulse sequences in a manner that can be tailored to all samples/hardware being used.

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↗

bioRxiv2021

Anatomy of unfolding: The site-specific fold stability of Yfh1 measured by 2D NMR

Most techniques allow detection of protein unfolding either by following the behaviour of single reporters or as an averaged all-or-none process. We recently added 2D NMR spectroscopy to the well-established techniques able to obtain information on the process of unfolding using resonances of residues in the hydrophobic core of a protein. Here, we questioned whether an analysis of the individual stability curves from each resonance could provide additional site-specific information. We used the Yfh1 protein that has the unique feature to undergo both cold and heat denaturation at temperatures above water freezing at low ionic strength. We show that stability curves inconsistent with the average NMR curve from hydrophobic core residues mainly comprise exposed outliers that do nevertheless provide precious information. By monitoring both cold and heat denaturation of individual residues we gain knowledge on the process of cold denaturation and convincingly demonstrate that the two unfolding processes are intrinsically different.

biophysics↗

bioRxiv2018

Atomic Level Characterisation of Millisecond-Time Scale Protein Motions through a Combined Molecular Simulations and NMR Approach

Proteins need to interconvert between many conformations in order to function, many of which are formed transiently, and sparsely populated. Particularly when the lifetimes of these states approach the millisecond timescale, identifying the relevant structures and the mechanism by which they inter-convert remains a tremendous challenge. Here we introduce a novel combination of accelerated MD (aMD) simulations and Markov State modelling (MSM) to explore these excited conformational states. Applying this to the highly dynamic protein CypA, a protein involved in immune response and associated with HIV infection, we identify five principally populated conformational states and the atomistic mechanism by which they interconvert. A rational design strategy predicted that the mutant D66A should stabilise the minor conformations and substantially alter the dynamics whereas the similar mutant H70A should leave the landscape broadly unchanged. These predictions are confirmed using CPMG and R1{rho} solution state NMR measurements. By accurately and reliably exploring functionally relevant, but sparsely populated conformations with milli-second lifetimes in silico, our aMD/MSM method has tremendous promise for the design of dynamic protein free energy landscapes for both protein engineering and drug discovery.

biophysics↗