Informing NMR experiments with molecular dynamics simulations to characterize the dominant activated state of the KcsA ion channel

LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts

This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms (N, Cα, Cβ, C', HN, Hα). LEGOLAS has been designed to be fast without loss of accuracy, as our model is able to predict backbone chemical shifts with root-mean-square errors of 2.53 ppm for N, 0.91 ppm for Cα, 1.14 ppm for Cβ, 1.02 ppm for C', 0.49 ppm for amide protons, and 0.27 ppm for Hα. The program predicts chemical shifts an order of magnitude faster than the widely utilized SHIFTX2 model. This breakthrough allows us to predict NMR chemical shifts for a very large number of input structures, such as frames from a molecular dynamics (MD) trajectory. In our simulation of the protein BBL from Escherichia coli, we observe that averaging the chemical shift predictions for a set of frames of an MD trajectory substantially improves the agreement with experiment with respect to using a single frame of the dynamics. We also show that LEGOLAS can be successfully applied to the problem of recognizing the native states of a protein among a set of decoys.

Resolution in cryogenic solid state NMR: Challenges and solutions

NMR at cryogenic temperatures has the potential to provide rich site-specific details regarding biopolymer structure, function, and mechanistic intermediates. Broad spectral lines compared with room temperature NMR can sometimes present practical challenges. A number of hypotheses regarding the origins of line broadening are explored. One frequently considered explanation is the presence of inhomogeneous conformational distributions. Possibly these arise when the facile characteristic motions that occur near room temperature become dramatically slower or "frozen out" at temperatures below the solvent phase change. Recent studies of low temperature spectra harness the distributions in properties in these low temperature spectra to uncover information regarding the conformational ensembles that drive biological function.

Predicted and Experimental NMR Chemical Shifts at Variable Temperatures: The Effect of Protein Conformational Dynamics

NMR chemical shifts provide a sensitive probe of protein structure and dynamics but remain challenging to predict and interpret. We examine the effect of protein conformational distributions on 15N chemical shifts for dihydrofolate reductase (DHFR), comparing QM/MM predicted shifts with experimental shifts in solution as well as frozen distributions. Representative snapshots from MD trajectories exhibit variation in predicted 15N chemical shifts of up to 25 ppm. The average over the fluctuations is in significantly better agreement with room temperature solution experimental values than the prediction for any single optimal conformations. Meanwhile, solid-state NMR (SSNMR) measurements of frozen solutions at 105 K exhibit broad lines whose widths agree well with the widths of distributions of predicted shifts for samples from the trajectory. The backbone torsion angle ψi-1 varies over 60° on the picosecond time scale, compensated by φi. These fluctuations can explain much of the shift variation.

Conformational ensemble of the NSP1 CTD in SARS-CoV-2: Perspectives from the free energy landscape

The nonstructural protein-1 (NSP1) of the severe acute respiratory syndrome-associated coronavirus 2 plays a crucial role in the translational shutdown and immune evasion inside host cells. Despite its known intrinsic disorder, the C-terminal domain (CTD) of NSP1 has been reported to form a double α-helical structure and block the 40S-ribosomal channel for mRNA translation. Experimental studies indicate that NSP1 CTD functions independently from the globular N-terminal region separated with a long linker domain, underscoring the necessity of exploring the standalone conformational ensemble. In this contribution, we utilize exascale computing resources to yield unbiased molecular dynamics simulation of NSP1 CTD in all-atom resolution starting from multiple initial seed structures. A data-driven approach elicits collective variables (CVs) that are significantly superior to conventional descriptors in capturing the conformational heterogeneity. The free energy landscape as a function of the CV space is estimated using the modified expectation maximized molecular dynamics. Originally developed by us for small peptides, here, we establish the efficacy of expectation maximized molecular dynamics in conjunction with data-driven CV space for a more complex and relevant biomolecular system. The results reveal the existence of two disordered metastable populations in the free energy landscape that are separated from the conformation resembling ribosomal subunit bound state by high kinetic barriers. Chemical shift correlation and secondary structure analysis capture significant differences among key structures of the ensemble. Altogether, these insights can underpin drug development studies and mutational experiments that help induce population shifts to alter translational blocking and understand its molecular basis in further detail.

Predicted and Experimental NMR Chemical Shifts at Variable Temperatures: The Effect of Protein Conformational Dynamics

NMR chemical shifts provide a sensitive probe of protein structure and dynamics. Prediction of shifts, and therefore interpretation of shifts, particularly for the frequently measured amidic 15 N sites, remains a tall challenge. We demonstrate that protein 15 N chemical shift prediction from QM/MM predictions can be improved if conformational variation is included via MD sampling, focusing on the antibiotic target, E. coli Dihydrofolate reductase (DHFR). Variations of up to 25 ppm in predicted 15 N chemical shifts are observed over the trajectory. For solution shifts the average of fluctuations on the low picosecond timescale results in a superior prediction to a single optimal conformation. For low temperature solid state measurements, the histogram of predicted shifts for locally minimized snapshots with specific solvent arrangements sampled from the trajectory explains the heterogeneous linewidths; in other words, the conformations and associated solvent are 'frozen out' at low temperatures and result in inhomogeneously broadened NMR peaks. We identified conformational degrees of freedom that contribute to chemical shift variation. Backbone torsion angles show high amplitude fluctuations during the trajectory on the low picosecond timescale. For a number of residues, including I60, ψ varies by up to 60º within a conformational basin during the MD simulations, despite the fact that I60 (and other sites studied) are in a secondary structure element and remain well folded during the trajectory. Fluctuations in ψ appear to be compensated by other degrees of freedom in the protein, including φ of the succeeding residue, resulting in "rocking" of the amide plane with changes in hydrogen bonding interactions. Good agreement for both room temperature and low temperature NMR spectra provides strong support for the specific approach to conformational averaging of computed chemical shifts.

Full opening of helix bundle crossing does not lead to NaK channel activation

A critical part of ion channel function is the ability to open and close in response to stimuli and thus conduct ions in a regulated fashion. While x-ray diffraction studies of ion channels suggested a general steric gating mechanism located at the helix bundle crossing (HBC), recent functional studies on several channels indicate that the helix bundle crossing is wide-open even in functionally nonconductive channels. Two NaK channel variants were crystallized in very different open and closed conformations, which served as important models of the HBC gating hypothesis. However, neither of these NaK variants is conductive in liposomes unless phenylalanine 92 is mutated to alanine (F92A). Here, we use NMR to probe distances at near-atomic resolution of the two NaK variants in lipid bicelles. We demonstrate that in contrast to the crystal structures, both NaK variants are in a fully open conformation, akin to Ca2+-bound MthK channel structure where the HBC is widely open. While we were not able to determine what a conductive NaK structure is like, our further inquiry into the gating mechanism suggests that the selectivity filter and pore helix are coupled to the M2 helix below and undergo changes in the structure when F92 is mutated. Overall, our data show that NaK exhibits coupling between the selectivity filter and HBC, similar to K+ channels, and has a more complex gating mechanism than previously thought, where the full opening of HBC does not lead to channel activation.

High-Resolution Magic Angle Spinning NMR of KcsA in Liposomes: The Highly Mobile C-Terminus

The structure of the transmembrane domain of the pH-activated bacterial potassium channel KcsA has been extensively characterized, yet little information is available on the structure of its cytosolic, functionally critical N- and C-termini. This study presents high-resolution magic angle spinning (HR-MAS) and fractional deuteration as tools to study these poorly resolved regions for proteoliposome-embedded KcsA. Using ¹H-detected HR-MAS NMR, we show that the C-terminus transitions from a rigid structure to a more dynamic structure as the solution is rendered acidic. We make previously unreported assignments of residues in the C-terminus of lipid-embedded channels. These data agree with functional models of the C-terminus-stabilizing KcsA tetramers at a neutral pH with decreased stabilization effects at acidic pH. We present evidence that a C-terminal truncation mutation has a destabilizing effect on the KcsA selectivity filter. Finally, we show evidence of hydrolysis of lipids in proteoliposome samples during typical experimental timeframes.

TUPÃ: Electric field analyses for molecular simulations

We introduce TUPÃ, a Python-based algorithm to calculate and analyze electric fields in molecular simulations. To demonstrate the features in TUPÃ, we present three test cases in which the orientation and magnitude of the electric field exerted by biomolecules help explain biological phenomena or observed kinetics. As part of TUPÃ, we also provide a PyMOL plugin to help researchers visualize how electric fields are organized within the simulation system. The code is freely available and can be obtained at https://mdpoleto.github.io/tupa/.