Mapping Protein Conformational Energy Landscapes Using NMR and Molecular Simulation

Uncovering allostery and regulation in SORCIN through molecular dynamics simulations

Soluble resistance-related calcium-binding protein or Sorcin is an allosteric, calcium-binding Penta-EF hand (PEF) family protein implicated in multi-drug resistant cancers. Sorcin is known to bind chemotherapeutic molecules such as Doxorubicin. This study uses in-silico molecular dynamics simulations to explore the dynamics and allosteric behavior of Sorcin in the context of Ca2+ uptake and Doxorubicin binding. The results show that Ca2+ binding induces large, but reversible conformational changes in the Sorcin structure which manifest as rigid body reorientations that preserve the local secondary structure. A reciprocal allosteric handshake centered around the EF5 hand is found to be key in Sorcin dimer formation and stabilization. Binding of Doxorubicin results in rearrangement of allosteric communities which disrupts long-range allosteric information transfer from the N-terminal domain to the middle lobe. However, this binding does not result in secondary structure destabilization. Sorcin does not appear to have a distinct Ca2+ activated mode of Doxorubicin binding.Communicated by Ramaswamy H. Sarma.

Recognition Mechanisms between a Nanobody and Disordered Epitopes of the Human Prion Protein: An Integrative Molecular Dynamics Study

Immunotherapy using antibodies to target the aggregation of flexible proteins holds promise for therapeutic interventions in neurodegenerative diseases caused by protein misfolding. Prions or PrP^Sc, the causal agents of transmissible spongiform encephalopathies (TSE), represent a model target for immunotherapies as TSE are prototypical protein misfolding diseases. The X-ray crystal structure of the wild-type (WT) human prion protein (HuPrP) bound to a camelid antibody fragment, denoted as Nanobody 484 (Nb484), has been previously solved. Nb484 was found to inhibit prion aggregation in vitro through a unique mechanism of structural stabilization of two disordered epitopes, that is, the palindromic motif (residues 113-120) and the β2-α2 loop region (residues 164-185). The study of the structural basis for antibody recognition of flexible proteins requires appropriate sampling techniques for the identification of conformational states occurring in disordered epitopes. To elucidate the Nb484-HuPrP recognition mechanisms, here we applied molecular dynamics (MD) simulations complemented with available NMR and X-ray crystallography data collected on the WT HuPrP to describe the conformational spaces occurring on HuPrP prior to Nb484 binding. We observe the experimentally determined binding competent conformations within the ensembles of pre-existing conformational states in solution before binding. We also described the Nb484 recognition mechanisms in two HuPrP carrying a polymorphism (E219K) and a TSE-causing mutation (V210I). Our hybrid approaches allow the identification of dynamic conformational landscapes existing on HuPrP and highly characterized by molecular disorder to identify physiologically relevant and druggable transitions.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

Accurate and cost-effective NMR chemical shift predictions for proteins using a molecules-in-molecules fragmentation-based method

We have developed an efficient protocol using our two-layer Molecules-in-Molecules (MIM2) fragmentation-based quantum chemical method for the prediction of NMR chemical shifts of large biomolecules. To investigate the performance of our fragmentation approach and demonstrate its applicability, MIM-NMR calculations are first calibrated on a test set of six proteins. The MIM2-NMR method yields a mean absolute deviation (MAD) from unfragmented full molecule calculations of 0.01 ppm for 1H and 0.06 ppm for 13C chemical shifts. Thus, the errors from fragmentation are only about 3% of our target accuracy of ∼0.3 ppm for 1H and 2-3 ppm for 13C chemical shifts. To compare with experimental chemical shifts, a standard protocol is first derived using two smaller proteins 2LHY (176 atoms) and 2LI1 (146 atoms) for obtaining an appropriate protein structure for NMR chemical shift calculations. The effect of the solvent environment on the calculated NMR chemical shifts is incorporated through implicit, explicit, or explicit-implicit solvation models. The expensive first solvation shell calculations are replaced by a micro-solvation model in which only the immediate interaction between the protein and the explicit solvation environment is considered. A single explicit water molecule for each amine and amide proton is found to be sufficient to yield accurate results for 1H chemical shifts. The 1H and 13C NMR chemical shifts calculated using our protocol give excellent agreement with experiments for two larger proteins, 2MC5 (the helical part with 265 atoms) and 3UMK (33 residue slice with 547 atoms). Overall, our target accuracy of ∼0.3 ppm for 1H and ∼2-3 ppm for 13C has been achieved for the larger proteins. The proposed MIM-NMR method is accurate and computationally cost-effective and should be applicable to study a wide range of large proteins.

q-Canonical Monte Carlo Sampling for Modeling the Linkage between Charge Regulation and Conformational Equilibria of Peptides

The overall charge content and the patterning of charged residues have a profound impact on the conformational ensembles adopted by intrinsically disordered proteins. These parameters can be altered by charge regulation, which refers to the effects of post-translational modifications, pH-dependent changes to charge, and conformational fluctuations that modify the pKa values of ionizable residues. Although atomistic simulations have played a prominent role in uncovering the major sequence-ensemble relationships of IDPs, most simulations assume fixed charge states for ionizable residues. This may lead to erroneous estimates for conformational equilibria if they are linked to charge regulation. Here, we report the development of a new method we term q-canonical Monte Carlo sampling for modeling the linkage between charge regulation and conformational equilibria. The method, which is designed to be interoperable with the ABSINTH implicit solvation model, operates as follows: For a protein sequence with n ionizable residues, we start with all 2n charge microstates and use a criterion based on model compound pKa values to prune down to a subset of thermodynamically relevant charge microstates. This subset is then grouped into mesostates, where all microstates that belong to a mesostate have the same net charge. Conformational distributions, drawn from a canonical ensemble, are generated for each of the charge microstates that make up a mesostate using a method we designate as proton walk sampling. This method combines Metropolis Monte Carlo sampling in conformational space with an auxiliary Markov process that enables interconversions between charge microstates along a mesostate. Proton walk sampling helps identify the most likely charge microstate per mesostate. We then use thermodynamic integration aided by the multistate Bennett acceptance ratio method to estimate the free energies for converting between mesostates. These free energies are then combined with the per-microstate weights along each mesostate to estimate standard state free energies and pH-dependent free energies for all thermodynamically relevant charge microstates. The results provide quantitative estimates of the probabilities and preferred conformations associated with every thermodynamically accessible charge microstate. We showcase the application of q-canonical sampling using two model systems. The results establish the soundness of the method and the importance of charge regulation in systems characterized by conformational heterogeneity.

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.

HIV-1 Capsid Function Is Regulated by Dynamics: Quantitative Atomic-Resolution Insights by Integrating Magic-Angle-Spinning NMR, QM/MM, and MD

HIV-1 CA capsid protein possesses intrinsic conformational flexibility, which is essential for its assembly into conical capsids and interactions with host factors. CA is dynamic in the assembled capsid, and residues in functionally important regions of the protein undergo motions spanning many decades of time scales. Chemical shift anisotropy (CSA) tensors, recorded in magic-angle-spinning NMR experiments, provide direct residue-specific probes of motions on nano- to microsecond time scales. We combined NMR, MD, and density-functional-theory calculations, to gain quantitative understanding of internal backbone dynamics in CA assemblies, and we found that the dynamically averaged 15N CSA tensors calculated by this joined protocol are in remarkable agreement with experiment. Thus, quantitative atomic-level understanding of the relationships between CSA tensors, local backbone structure, and motions in CA assemblies is achieved, demonstrating the power of integrating NMR experimental data and theory for characterizing atomic-resolution dynamics in biological systems.

Structure Calculation and Reconstruction of Discrete-State Dynamics from Residual Dipolar Couplings

Residual dipolar couplings (RDCs) acquired by nuclear magnetic resonance (NMR) spectroscopy are an indispensable source of information in investigation of molecular structures and dynamics. Here, we present a comprehensive strategy for structure calculation and reconstruction of discrete-state dynamics from RDC data that is based on the singular value decomposition (SVD) method of order tensor estimation. In addition to structure determination, we provide a mechanism of producing an ensemble of conformations for the dynamical regions of a protein from RDC data. The developed methodology has been tested on simulated RDC data with ±1 Hz of error from an 83 residue α protein (PDB ID 1A1Z ) and a 213 residue α/β protein DGCR8 (PDB ID 2YT4 ). In nearly all instances, our method reproduced the structure of the protein including the conformational ensemble to within less than 2 Å. On the basis of our investigations, arc motions with more than 30° of rotation are identified as internal dynamics and are reconstructed with sufficient accuracy. Furthermore, states with relative occupancies above 20% are consistently recognized and reconstructed successfully. Arc motions with a magnitude of 15° or relative occupancy of less than 10% are consistently unrecognizable as dynamical regions within the context of ±1 Hz of error.

Investigating protein conformational energy landscapes and atomic resolution dynamics from NMR dipolar couplings: a review

Nuclear magnetic resonance spectroscopy is exquisitely sensitive to protein dynamics. In particular inter-nuclear dipolar couplings, that become measurable in solution when the protein is dissolved in a dilute liquid crystalline solution, report on all conformations sampled up to millisecond timescales. As such they provide the opportunity to describe the Boltzmann distribution present in solution at atomic resolution, and thereby to map the conformational energy landscape in unprecedented detail. The development of analytical methods and approaches based on numerical simulation and their application to numerous biologically important systems is presented.

Relating sequence encoded information to form and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) showcase the importance of conformational plasticity and heterogeneity in protein function. We summarize recent advances that connect information encoded in IDP sequences to their conformational properties and functions. We focus on insights obtained through a combination of atomistic simulations and biophysical measurements that are synthesized into a coherent framework using polymer physics theories.

NMR studies of dynamic biomolecular conformational ensembles

Multidimensional heteronuclear NMR approaches can provide nearly complete sequential signal assignments of isotopically enriched biomolecules. The availability of assignments together with measurements of spin relaxation rates, residual spin interactions, J-couplings and chemical shifts provides information at atomic resolution about internal dynamics on timescales ranging from ps to ms, both in solution and in the solid state. However, due to the complexity of biomolecules, it is not possible to extract a unique atomic-resolution description of biomolecular motions even from extensive NMR data when many conformations are sampled on multiple timescales. For this reason, powerful computational approaches are increasingly applied to large NMR data sets to elucidate conformational ensembles sampled by biomolecules. In the past decade, considerable attention has been directed at an important class of biomolecules that function by binding to a wide variety of target molecules. Questions of current interest are: "Does the free biomolecule sample a conformational ensemble that encompasses the conformations found when it binds to various targets; and if so, on what time scale is the ensemble sampled?" This article reviews recent efforts to answer these questions, with a focus on comparing ensembles obtained for the same biomolecules by different investigators. A detailed comparison of results obtained is provided for three biomolecules: ubiquitin, calmodulin and the HIV-1 trans-activation response RNA.

Advances in the determination of nucleic acid conformational ensembles

Conformational changes in nucleic acids play a key role in the way genetic information is stored, transferred, and processed in living cells. Here, we describe new approaches that employ a broad range of experimental data, including NMR-derived chemical shifts and residual dipolar couplings, small-angle X-ray scattering, and computational approaches such as molecular dynamics simulations to determine ensembles of DNA and RNA at atomic resolution. We review the complementary information that can be obtained from diverse sets of data and the various methods that have been developed to combine these data with computational methods to construct ensembles and assess their uncertainty. We conclude by surveying RNA and DNA ensembles determined using these methods, highlighting the unique physical and functional insights obtained so far.