Full Atom Simulations of Spin Label Conformations

FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries

Förster resonance energy transfer (FRET) is a widely-used and versatile technique for the structural characterization of biomolecules. Here, we introduce FRETpredict, an easy-to-use Python software to predict FRET efficiencies from ensembles of protein conformations. FRETpredict uses a rotamer library approach to describe the FRET probes covalently bound to the protein. The software efficiently and flexibly operates on large conformational ensembles such as those generated by molecular dynamics simulations to facilitate the validation or refinement of molecular models and the interpretation of experimental data. We provide access to rotamer libraries for many commonly used dyes and linkers and describe a general methodology to generate new rotamer libraries for FRET probes. We demonstrate the performance and accuracy of the software for different types of systems: a rigid peptide (polyproline 11), an intrinsically disordered protein (ACTR), and three folded proteins (HiSiaP, SBD2, and MalE). FRETpredict is open source (GPLv3) and is available at github.com/KULL-Centre/FRETpredict and as a Python PyPI package at pypi.org/project/FRETpredict .

FRETpredict: A Python package for FRET efficiency predictions using rotamer libraries

Here, we introduce FRETpredict, a Python software program to predict FRET efficiencies from ensembles of protein conformations. FRETpredict uses an established Rotamer Library Approach to describe the FRET probes covalently bound to the protein. The software efficiently operates on large conformational ensembles such as those generated by molecular dynamics simulations to facilitate the validation or refinement of molecular models and the interpretation of experimental data. We demonstrate the performance and accuracy of the software for different types of systems: a relatively structured peptide (polyproline 11), an intrinsically disordered protein (ACTR), and three folded proteins (HiSiaP, SBD2, and MalE). We also describe a general approach to generate new rotamer libraries for FRET probes of interest. FRETpredict is open source (GPLv3) and is available at github.com/KULL-Centre/FRETpredict and as a Python PyPI package at pypi.org/project/FRETpredict.

Methodology for rigorous modeling of protein conformational changes by Rosetta using DEER distance restraints

We describe an approach for integrating distance restraints from Double Electron-Electron Resonance (DEER) spectroscopy into Rosetta with the purpose of modeling alternative protein conformations from an initial experimental structure. Fundamental to this approach is a multilateration algorithm that harnesses sets of interconnected spin label pairs to identify optimal rotamer ensembles at each residue that fit the DEER decay in the time domain. Benchmarked relative to data analysis packages, the algorithm yields comparable distance distributions with the advantage that fitting the DEER decay and rotamer ensemble optimization are coupled. We demonstrate this approach by modeling the protonation-dependent transition of the multidrug transporter PfMATE to an inward facing conformation with a deviation to the experimental structure of less than 2Å Cα RMSD. By decreasing spin label rotamer entropy, this approach engenders more accurate Rosetta models that are also more closely clustered, thus setting the stage for more robust modeling of protein conformational changes.

DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles

Owing to their plasticity, intrinsically disordered and multidomain proteins require descriptions based on multiple conformations, thus calling for techniques and analysis tools that are capable of dealing with conformational ensembles rather than a single protein structure. Here, we introduce DEER-PREdict, a software program to predict Double Electron-Electron Resonance distance distributions as well as Paramagnetic Relaxation Enhancement rates from ensembles of protein conformations. DEER-PREdict uses an established rotamer library approach to describe the paramagnetic probes which are bound covalently to the protein.DEER-PREdict has been designed to operate efficiently on large conformational ensembles, such as those generated by molecular dynamics simulation, to facilitate the validation or refinement of molecular models as well as the interpretation of experimental data. The performance and accuracy of the software is demonstrated with experimentally characterized protein systems: HIV-1 protease, T4 Lysozyme and Acyl-CoA-binding protein. DEER-PREdict is open source (GPLv3) and available at github.com/KULL-Centre/DEERpredict and as a Python PyPI package pypi.org/project/DEERPREdict.

Atomistic Models from Orientation and Distance Constraints Using EPR of a Bifunctional Spin Label

We have used high-resolution orientation and distance measurements derived from electron paramagnetic resonance of a bifunctional spin label (BSL) to build and refine atomistic models of protein structure. We demonstrate this approach by investigating the effects of nucleotide binding on the structure of myosin's catalytic domain while myosin is in complex with actin. Constraints for orientation of individual helices were obtained in a previous study from continuous-wave electron paramagnetic resonance of myosin labeled at specific sites with BSLs in oriented muscle fibers. In this study, new distance constraints were derived from double electron-electron resonance on myosin constructs labeled with a BSL specifically at two sites. Using these complementary constraints together, we thoroughly characterize the BSL's rigid, highly stereoselective attachment to protein α-helices, which permits accurate measurements of orientation and distance. We also leverage these measurements to derive a novel, to our knowledge, structural model for myosin-II in complex with actin and MgADP and compare our model to other recent actomyosin structures. The described approach is applicable to any orientable complex (e.g., membranes or filaments) in which site-specific di-Cys mutation is feasible.

Confidence Analysis of DEER Data and Its Structural Interpretation with Ensemble-Biased Metadynamics

Given its ability to measure multicomponent distance distributions between electron-spin probes, double electron-electron resonance (DEER) spectroscopy has become a leading technique to assess the structural dynamics of biomolecules. However, methodologies to evaluate the statistical error of these distributions are not standard, often hampering a rigorous interpretation of the experimental results. Distance distributions are often determined from the experimental DEER data through a mathematical method known as Tikhonov regularization, but this approach makes rigorous error estimates difficult. Here, we build upon an alternative, model-based approach in which the distance probability distribution is represented as a sum of Gaussian components, and use propagation of errors to calculate an associated confidence band. Our approach considers all sources of uncertainty, including the experimental noise, the uncertainty in the fitted background signal, and the limited time span of the data collection. The resulting confidence band reveals the most and least reliable features of the probability distribution, thereby informing the structural interpretation of DEER experiments. To facilitate this interpretation, we also generalize the molecular simulation method known as ensemble-biased metadynamics (EBMetaD). This method, originally designed to generate maximal-entropy structural ensembles consistent with one or more probability distributions, now also accounts for the uncertainty in those target distributions exactly as dictated by their confidence bands. After careful benchmarks, we demonstrate the proposed techniques using DEER results from spin-labeled T4 lysozyme.

The contribution of modern EPR to structural biology

Electron paramagnetic resonance (EPR) spectroscopy combined with site-directed spin labelling is applicable to biomolecules and their complexes irrespective of system size and in a broad range of environments. Neither short-range nor long-range order is required to obtain structural restraints on accessibility of sites to water or oxygen, on secondary structure, and on distances between sites. Many of the experiments characterize a static ensemble obtained by shock-freezing. Compared with characterizing the dynamic ensemble at ambient temperature, analysis is simplified and information loss due to overlapping timescales of measurement and system dynamics is avoided. The necessity for labelling leads to sparse restraint sets that require integration with data from other methodologies for building models. The double electron–electron resonance experiment provides distance distributions in the nanometre range that carry information not only on the mean conformation but also on the width of the native ensemble. The distribution widths are often inconsistent with Anfinsen's concept that a sequence encodes a single native conformation defined at atomic resolution under physiological conditions.

Characterization of Bifunctional Spin Labels for Investigating the Structural and Dynamic Properties of Membrane Proteins Using EPR Spectroscopy

Site-directed spin labeling (SDSL) coupled with electron paramagnetic resonance (EPR) spectroscopy is a very powerful technique to study structural and dynamic properties of membrane proteins. The most widely used spin label is methanthiosulfonate (MTSL). However, the flexibility of this spin label introduces greater uncertainties in EPR measurements obtained for determining structures, side-chain dynamics, and backbone motion of membrane protein systems. Recently, a newer bifunctional spin label (BSL), 3,4-bis(methanethiosulfonylmethyl)-2,2,5,5-tetramethyl-2,5-dihydro-1H-pyrrol-1-yloxy, has been introduced to overcome the dynamic limitations associated with the MTSL spin label and has been invaluable in determining protein backbone dynamics and inter-residue distances due to its restricted internal motion and fewer size restrictions. While BSL has been successful in providing more accurate information about the structure and dynamics of several proteins, a detailed characterization of the spin label is still lacking. In this study, we characterized BSLs by performing CW-EPR spectral line shape analysis as a function of temperature on spin-labeled sites inside and outside of the membrane for the integral membrane protein KCNE1 in POPC/POPG lipid bilayers and POPC/POPG lipodisq nanoparticles. The experimental data revealed a powder pattern spectral line shape for all of the KCNE1-BSL samples at 296 K, suggesting the motion of BSLs approaches the rigid limit regime for these series of samples. BSLs were further utilized to report for the first time the distance measurement between two BSLs attached on an integral membrane protein KCNE1 in POPC/POPG lipid bilayers at room temperature using dipolar line broadening CW-EPR spectroscopy. The CW dipolar line broadening EPR data revealed a 15 ± 2 Å distance between doubly attached BSLs on KCNE1 (53/57-63/67) which is consistent with molecular dynamics modeling and the solution NMR structure of KCNE1 which yielded a distance of 17 Å. This study demonstrates the utility of investigating the structural and dynamic properties of membrane proteins in physiologically relevant membrane mimetics using BSLs.