Predicting protein backbone chemical shifts from Cα coordinates: extracting high resolution experimental observables from low resolution models

Refined Bonded Terms in Coarse-Grained Models for Intrinsically Disordered Proteins Improve Backbone Conformations

Coarse-grained models designed for intrinsically disordered proteins and regions (IDP/Rs) usually omit some bonded potentials (e.g., angular and dihedral potentials) as a conventional strategy to enhance backbone flexibility. However, a notable drawback of this approach is the generation of inaccurate backbone conformations. Here, we addressed this problem by introducing residue-specific angular, refined dihedral, and correction map (CMAP) potentials, derived based on the statistics from a customized coil database. These bonded potentials were integrated into the existing Mpipi model, resulting in a new model, denoted as the "Mpipi+" model. Results show that the Mpipi+ model can improve backbone conformations. More importantly, it can markedly improve the secondary structure propensity (SSP) based on the experimental chemical shift and, consequently, succeed in capturing transient secondary structures. Moreover, the Mpipi+ model preserves the liquid-liquid phase separation (LLPS) propensities of IDPs.

Brewing COFFEE: A Sequence-Specific Coarse-Grained Energy Function for Simulations of DNA-Protein Complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription and gene expression to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse-grained Force Field for Energy Estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the self-organized polymer model with side-chains for proteins and the three interaction site model for DNA in a modular fashion, without recalibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a data set of high-resolution crystal structures. The only parameter in COFFEE is the strength (λDNAPRO) of the DNA-protein contact potential. For an optimal choice of λDNAPRO, the crystallographic B-factors for DNA-protein complexes with varying sizes and topologies are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts scattering profiles that are in quantitative agreement with small-angle X-ray scattering experiments, as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which do not alter the balance of electrostatic interactions but affect chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Brewing COFFEE: A sequence-specific coarse-grained energy function for simulations of DNA-protein complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription, gene expression, to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse grained force field for energy estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the Self-Organized Polymer model with Side Chains for proteins and the Three Interaction Site model for DNA in a modular fashion, without re-calibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a dataset of high-resolution crystal structures. The only parameter in COFFEE is the strength (λ D N A P R O ) of the DNA-protein contact potential. For an optimal choice of λ D N A P R O , the crystallographic B-factors for DNA-protein complexes, with varying sizes and topologies, are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts the scattering profiles that are in quantitative agreement with SAXS experiments as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which does not alter the balance of electrostatic interactions, but affects chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Crystal structures of bacterial small multidrug resistance transporter EmrE in complex with structurally diverse substrates

Proteins from the bacterial small multidrug resistance (SMR) family are proton-coupled exporters of diverse antiseptics and antimicrobials, including polyaromatic cations and quaternary ammonium compounds. The transport mechanism of the Escherichia coli transporter, EmrE, has been studied extensively, but a lack of high-resolution structural information has impeded a structural description of its molecular mechanism. Here, we apply a novel approach, multipurpose crystallization chaperones, to solve several structures of EmrE, including a 2.9 Å structure at low pH without substrate. We report five additional structures in complex with structurally diverse transported substrates, including quaternary phosphonium, quaternary ammonium, and planar polyaromatic compounds. These structures show that binding site tryptophan and glutamate residues adopt different rotamers to conform to disparate structures without requiring major rearrangements of the backbone structure. Structural and functional comparison to Gdx-Clo, an SMR protein that transports a much narrower spectrum of substrates, suggests that in EmrE, a relatively sparse hydrogen bond network among binding site residues permits increased sidechain flexibility.

Differences in the free energies between the excited states of Aβ40 and Aβ42 monomers encode their aggregation propensities

The early events in the aggregation of the intrinsically disordered peptide, amyloid-β (Aβ), involve transitions from the disordered free energy ground state to assembly-competent states. Are the fingerprints of order found in the amyloid fibrils encoded in the conformations that the monomers access at equilibrium? If so, could the enhanced aggregation rate of Aβ42 compared to Aβ40 be rationalized from the sparsely populated high free energy states of the monomers? Here, we answer these questions in the affirmative using coarse-grained simulations of the self-organized polymer-intrinsically disordered protein (SOP-IDP) model of Aβ40 and Aβ42. Although both the peptides have practically identical ensemble-averaged properties, characteristic of random coils (RCs), the conformational ensembles of the two monomers exhibit sequence-specific heterogeneity. Hierarchical clustering of conformations reveals that both the peptides populate high free energy aggregation-prone ([Formula: see text]) states, which resemble the monomers in the fibril structure. The free energy gap between the ground (RC) and the [Formula: see text] states of Aβ42 peptide is smaller than that for Aβ40. By relating the populations of excited states of the two peptides to the fibril formation time scales using an empirical formula, we explain nearly quantitatively the faster aggregation rate of Aβ42 relative to Aβ40. The [Formula: see text] concept accounts for fibril polymorphs, leading to the prediction that the less stable [Formula: see text] state of Aβ42, encoding for the U-bend fibril, should form earlier than the structure with the S-bend topology, which is in accord with Ostwald's rule rationalizing crystal polymorph formation.

Classification of RNA backbone conformations into rotamers using 13C' chemical shifts: exploring how far we can go

The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical ¹³C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental ¹³C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of ¹³C′ chemical shifts.

Principles of protein structural ensemble determination

The biological functions of protein molecules are intimately dependent on their conformational dynamics. This aspect is particularly evident for disordered proteins, which constitute perhaps one-third of the human proteome. Therefore, structural ensembles often offer more useful representations of proteins than individual conformations. Here, we describe how the well-established principles of protein structure determination should be extended to the case of protein structural ensembles determination. These principles concern primarily how to deal with conformationally heterogeneous states, and with experimental measurements that are averaged over such states and affected by a variety of errors. We first review the growing literature of recent methods that combine experimental and computational information to model structural ensembles, highlighting their similarities and differences. We then address some conceptual problems in the determination of structural ensembles and define future goals towards the establishment of objective criteria for the comparison, validation, visualization and dissemination of such ensembles.

Microscopic mechanisms that govern the titration response and pKa values of buried residues in staphylococcal nuclease mutants

To probe the microscopic mechanisms that govern the titration behavior of buried ionizable groups, microsecond explicit solvent molecular dynamics simulations are carried out for several mutants of Staphylococcal nuclease using both fixed charge and polarizable force fields. While the ionization of Asp 66, Glu 66, and Lys 125 lead to enhanced structural fluctuations and partial unfolding of adjacent α-helical regions, the ionization of Lys 25 causes local unfolding of adjacent β sheets. Using the sampled conformational ensembles, good agreement with experimental pK_a values is obtained with Poisson-Boltzmann calculations using a protein dielectric constant of 2-4 for V66D/E; slightly larger dielectric constants are needed for Lys mutants especially L25K, suggesting that structural responses beyond microseconds are involved in ionization of Lys 25. Overall, the set of unbiased simulations provides insights into the spatial and temporal scales of protein and solvent motions that dictate the diverse titration behaviors of buried protein residues. Proteins 2017; 85:268-281. © 2016 Wiley Periodicals, Inc.

Capturing a Dynamic Chaperone-Substrate Interaction Using NMR-Informed Molecular Modeling

Chaperones maintain a healthy proteome by preventing aggregation and by aiding in protein folding. Precisely how chaperones influence the conformational properties of their substrates, however, remains unclear. To achieve a detailed description of dynamic chaperone-substrate interactions, we fused site-specific NMR information with coarse-grained simulations. Our model system is the binding and folding of a chaperone substrate, immunity protein 7 (Im7), with the chaperone Spy. We first used an automated procedure in which NMR chemical shifts inform the construction of system-specific force fields that describe each partner individually. The models of the two binding partners are then combined to perform simulations on the chaperone-substrate complex. The binding simulations show excellent agreement with experimental data from multiple biophysical measurements. Upon binding, Im7 interacts with a mixture of hydrophobic and hydrophilic residues on Spy's surface, causing conformational exchange within Im7 to slow down as Im7 folds. Meanwhile, the motion of Spy's flexible loop region increases, allowing for better interaction with different substrate conformations, and helping offset losses in Im7 conformational dynamics that occur upon binding and folding. Spy then preferentially releases Im7 into a well-folded state. Our strategy has enabled a residue-level description of a dynamic chaperone-substrate interaction, improving our understanding of how chaperones facilitate substrate folding. More broadly, we validate our approach using two other binding partners, showing that this approach provides a general platform from which to investigate other flexible biomolecular complexes through the integration of NMR data with efficient computational models.

Interaction Networks in Protein Folding via Atomic-Resolution Experiments and Long-Time-Scale Molecular Dynamics Simulations

The integration of atomic-resolution experimental and computational methods offers the potential for elucidating key aspects of protein folding that are not revealed by either approach alone. Here, we combine equilibrium NMR measurements of thermal unfolding and long molecular dynamics simulations to investigate the folding of gpW, a protein with two-state-like, fast folding dynamics and cooperative equilibrium unfolding behavior. Experiments and simulations expose a remarkably complex pattern of structural changes that occur at the atomic level and from which the detailed network of residue-residue couplings associated with cooperative folding emerges. Such thermodynamic residue-residue couplings appear to be linked to the order of mechanistically significant events that take place during the folding process. Our results on gpW indicate that the methods employed in this study are likely to prove broadly applicable to the fine analysis of folding mechanisms in fast folding proteins.