Ensemble-based interpretations of NMR structural data to describe protein internal dynamics

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences

Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3ϵ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX+ server.

Ensemble-Based Analysis of the Dynamic Allostery in the PSD-95 PDZ3 Domain in Relation to the General Variability of PDZ Structures

PDZ domains are abundant interaction hubs found in a number of different proteins and they exhibit characteristic differences in their structure and ligand specificity. Their internal dynamics have been proposed to contribute to their biological activity via changes in conformational entropy upon ligand binding and allosteric modulation. Here we investigate dynamic structural ensembles of PDZ3 of the postsynaptic protein PSD-95, calculated based on previously published backbone and side-chain S2 order parameters. We show that there are distinct but interdependent structural rearrangements in PDZ3 upon ligand binding and the presence of the intramolecular allosteric modulator helix α3. We have also compared these rearrangements in PDZ1-2 of PSD-95 and the conformational diversity of an extended set of PDZ domains available in the PDB database. We conclude that although the opening-closing rearrangement, occurring upon ligand binding, is likely a general feature for all PDZ domains, the conformer redistribution upon ligand binding along this mode is domain-dependent. Our findings suggest that the structural and functional diversity of PDZ domains is accompanied by a diversity of internal motional modes and their interdependence.

Evaluation and Selection of Dynamic Protein Structural Ensembles with CoNSEnsX. Methods Mol Biol 2020;2112:241-254. [PMID: 32006289 DOI: 10.1007/978-1-0716-0270-6_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Understanding protein function at atomistic detail is not possible without accounting for the internal dynamics of these molecules. Ensemble-based models are based on the premise that single conformers cannot account for all experimental observations on the given molecule. Rather, a suitable set of structures, representing the internal dynamics of the protein at a given timescale, are necessary to achieve correspondence to measurements. CoNSEnsX⁺ is a service specifically designed for the investigation of such ensembles for compliance with NMR-derived parameters. In contrast to common structure evaluation tools, all parameters are treated as an average over the ensemble, if are not themselves an ensemble property like order parameters. CoNSEnsX⁺ is also capable of selecting a sub-ensemble with increased correspondence to a set of user-defined experimental parameters. CoNSEnsX⁺ is available as a web server at http://consensx.itk.ppke.hu , and the full Python source code is available on GitHub.

Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach

We describe a Bayesian/Maximum entropy (BME) procedure and software to construct a conformational ensemble of a biomolecular system by integrating molecular simulations and experimental data. First, an initial conformational ensemble is constructed using, for example, Molecular Dynamics or Monte Carlo simulations. Due to potential inaccuracies in the model and finite sampling effects, properties predicted from simulations may not agree with experimental data. In BME we use the experimental data to refine the simulation so that the new conformational ensemble has the following properties: (1) the calculated averages are close to the experimental values taking uncertainty into account and (2) it maximizes the relative Shannon entropy with respect to the original simulation ensemble. The output of this procedure is a set of optimized weights that can be used to calculate other properties and distributions of these. Here, we provide a practical guide on how to obtain and use such weights, how to choose adjustable parameters and discuss shortcomings of the method.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Different modes of barrel opening suggest a complex pathway of ligand binding in human gastrotropin

Gastrotropin, the intracellular carrier of bile salts in the small intestine, binds two ligand molecules simultaneously in its internal cavity. The molecular rearrangements required for ligand entry are not yet fully clear. To improve our understanding of the binding process we combined molecular dynamics simulations with previously published structural and dynamic NMR parameters. The resulting ensembles reveal two distinct modes of barrel opening with one corresponding to the transition between the apo and holo states, whereas the other affecting different protein regions in both ligation states. Comparison of the calculated structures with NMR-derived parameters reporting on slow conformational exchange processes suggests that the protein undergoes partial unfolding along a path related to the second mode of the identified barrel opening motion.

Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

Both experimental and computational methods are available to gather information about a protein's conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

Active Site Breathing of Human Alkbh5 Revealed by Solution NMR and Accelerated Molecular Dynamics

AlkB homolog 5 (Alkbh5) is one of nine members of the AlkB family, which are nonheme Fe²⁺/α-ketoglutarate-dependent dioxygenases that catalyze the oxidative demethylation of modified nucleotides and amino acids. Alkbh5 is highly selective for the N⁶-methyladenosine modification, an epigenetic mark that has spawned significant biological and pharmacological interest because of its involvement in important physiological processes, such as carcinogenesis and stem cell differentiation. Herein, we investigate the structure and dynamics of human Alkbh5 in solution. By using ¹⁵N and ¹³C_methyl relaxation dispersion and ¹⁵N-R₁ and R_1ρ NMR experiments, we show that the active site of apo Alkbh5 experiences conformational dynamics on multiple timescales. Consistent with this observation, backbone amide residual dipolar couplings measured for Alkbh5 in phage pf1 are inconsistent with the static crystal structure of the enzyme. We developed a simple approach that combines residual dipolar coupling data and accelerated molecular dynamics simulations to calculate a conformational ensemble of Alkbh5 that is fully consistent with the experimental NMR data. Our structural model reveals that Alkbh5 is more disordered in solution than what is observed in the crystal state and undergoes breathing motions that expand the active site and allow access to α-ketoglutarate. Disordered-to-ordered conformational changes induced by sequential substrate/cofactor binding events have been often invoked to interpret biochemical data on the activity and specificity of AlkB proteins. The structural ensemble reported in this work provides the first atomic-resolution model of an AlkB protein in its disordered conformational state to our knowledge.

Conformational Ensembles Exhibit Extensive Molecular Recognition Features

Intrinsically disordered proteins (IDPs) are important for signaling and regulatory pathways. In contrast to folded proteins, they sample a diverse conformational space. IDPs have residue ranges within a sequence that have been referred to as molecular recognition features (MoRFs). A MoRF can be viewed as contiguous residues exhibiting a conformational disorder that become ordered upon binding to another protein or ligand. In this work, we introduce a structural characterization of MoRFs based on entropy and mutual information (MI). In this view, a MoRF is a set of contiguous residues that exhibit a large entropy (from rotameric residue sampling) and large MI, the latter indicating a dependence among the residues' rotameric sampling comprising the MoRF. The methodology is first applied to a number of ubiquitin ensembles that were obtained based on nuclear magnetic resonance experiments. One is a denatured Ub ensemble that has a large entropy for various unitSizes (number of contiguous residues) but essentially zero MI, indicting no dependence among the residue rotamer sampling. Another ensemble does exhibit extensive regions along the sequence where there are MoRFs centered on nonsecondary structure regions. The MoRFs are present for unitSizes 2-10. That a substantial number of MoRFs are present in Ub strongly suggests a conformational selection mechanism for this protein. Two additional ensembles for the cyclin-dependent kinase inhibitor Sic1 and for the amyloid protein α-synuclein, which have been shown to be IDPs, are also analyzed. Both exhibit MoRF-like character.

A Method for Determining Structure Ensemble of Large Disordered Protein: Application to a Mechanosensing Protein

Structure characterization of intrinsically disordered proteins (IDPs) remains a key obstacle in understanding their functional mechanisms. Due to the highly dynamic feature of IDPs, structure ensembles instead of static unique structures are often derived from experimental data. Several state-of-the-art computational methods have been developed to select an optimal ensemble from a pregenerated structure pool, but they suffer from low efficiency for large IDPs. Here we present a matching pursuit genetic algorithm (MPGA) for structure ensemble determination, which takes advantages from both matching pursuit (MP) to reduce the search space and genetic algorithm (GA) to reduce the restriction on constraint types. The MPGA method is validated using a reference ensemble with predefined structures. In comparison with the conventional GA, MPGA takes much less computational time for large IDPs. The utility of the method is demonstrated by application to structure ensemble determination of a mechanosensing protein domain with 306 amino acids. The structure ensemble determined reveals that the N-terminal region 1-240 is more compact than the C-terminal region 240-306. The unique structural feature explains why only a small portion of YXXP tyrosine residues can be phosphorylated easily by kinases in the absence of extension force and why the phosphorylation is force-dependent.

Molecular dynamics ensemble refinement of the heterogeneous native state of NCBD using chemical shifts and NOEs

Many proteins display complex dynamical properties that are often intimately linked to their biological functions. As the native state of a protein is best described as an ensemble of conformations, it is important to be able to generate models of native state ensembles with high accuracy. Due to limitations in sampling efficiency and force field accuracy it is, however, challenging to obtain accurate ensembles of protein conformations by the use of molecular simulations alone. Here we show that dynamic ensemble refinement, which combines an accurate atomistic force field with commonly available nuclear magnetic resonance (NMR) chemical shifts and NOEs, can provide a detailed and accurate description of the conformational ensemble of the native state of a highly dynamic protein. As both NOEs and chemical shifts are averaged on timescales up to milliseconds, the resulting ensembles reflect the structural heterogeneity that goes beyond that probed, e.g., by NMR relaxation order parameters. We selected the small protein domain NCBD as object of our study since this protein, which has been characterized experimentally in substantial detail, displays a rich and complex dynamical behaviour. In particular, the protein has been described as having a molten-globule like structure, but with a relatively rigid core. Our approach allowed us to describe the conformational dynamics of NCBD in solution, and to probe the structural heterogeneity resulting from both short- and long-timescale dynamics by the calculation of order parameters on different time scales. These results illustrate the usefulness of our approach since they show that NCBD is rather rigid on the nanosecond timescale, but interconverts within a broader ensemble on longer timescales, thus enabling the derivation of a coherent set of conclusions from various NMR experiments on this protein, which could otherwise appear in contradiction with each other.

Solution structure of an ultra-stable single-chain insulin analog connects protein dynamics to a novel mechanism of receptor binding

Domain-minimized insulin receptors (IRs) have enabled crystallographic analysis of insulin-bound "micro-receptors." In such structures, the C-terminal segment of the insulin B chain inserts between conserved IR domains, unmasking an invariant receptor-binding surface that spans both insulin A and B chains. This "open" conformation not only rationalizes the inactivity of single-chain insulin (SCI) analogs (in which the A and B chains are directly linked), but also suggests that connecting (C) domains of sufficient length will bind the IR. Here, we report the high-resolution solution structure and dynamics of such an active SCI. The hormone's closed-to-open transition is foreshadowed by segmental flexibility in the native state as probed by heteronuclear NMR spectroscopy and multiple conformer simulations of crystallographic protomers as described in the companion article. We propose a model of the SCI's IR-bound state based on molecular-dynamics simulations of a micro-receptor complex. In this model, a loop defined by the SCI's B and C domains encircles the C-terminal segment of the IR α-subunit. This binding mode predicts a conformational transition between an ultra-stable closed state (in the free hormone) and an active open state (on receptor binding). Optimization of this switch within an ultra-stable SCI promises to circumvent insulin's complex global cold chain. The analog's biphasic activity, which serendipitously resembles current premixed formulations of soluble insulin and microcrystalline suspension, may be of particular utility in the developing world.

Defining Low-Dimensional Projections to Guide Protein Conformational Sampling

Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner's performance are still poorly understood issues. In this article, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein's flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling and (ii) automatically constructing such projections is a reasonable alternative.

The Exact NOE as an Alternative in Ensemble Structure Determination

The structure-function paradigm is increasingly replaced by the structure-dynamics-function paradigm. All protein activity is steered by the interplay between enthalpy and entropy. Conformational dynamics serves as a proxy of conformational entropy. Therefore, it is essential to study not only the average conformation but also the spatial sampling of a protein on all timescales. To this purpose, we have established a protocol for determining multiple-state ensembles of proteins based on exact nuclear Overhauser effects (eNOEs). We have recently extended our previously reported eNOE data set for the protein GB3 by a very large set of backbone and side-chain residual dipolar couplings and three-bond J couplings. Here, we demonstrate that at least four structural states are required to represent the complete data set by dissecting the contributions to the CYANA target function, which quantifies restraint violations in structure calculation. We present a four-state ensemble of GB3, which largely preserves the characteristics obtained from eNOEs only. Due to the abundance of the input data, the ensemble and χ(1) angles in particular are well suited for cross-validation of the input data and comparison to x-ray structures. Principal component analysis is used to automatically identify and validate relevant states of the ensembles. Overall, our findings suggest that eNOEs are a valuable alternative to traditional NMR probes in spatial elucidation of proteins.

Hybrid Approaches to Structural Characterization of Conformational Ensembles of Complex Macromolecular Systems Combining NMR Residual Dipolar Couplings and Solution X-ray Scattering

Solving structures or structural ensembles of large macromolecular systems in solution poses a challenging problem. While NMR provides structural information at atomic resolution, increased spectral complexity, chemical shift overlap, and short transverse relaxation times (associated with slow tumbling) render application of the usual techniques that have been so successful for medium sized systems (<50 kDa) difficult. Solution X-ray scattering, on the other hand, is not limited by molecular weight but only provides low resolution structural information related to the overall shape and size of the system under investigation. Here we review how combining atomic resolution structures of smaller domains with sparse experimental data afforded by NMR residual dipolar couplings (which yield both orientational and shape information) and solution X-ray scattering data in rigid-body simulated annealing calculations provides a powerful approach for investigating the structural aspects of conformational dynamics in large multidomain proteins. The application of this hybrid methodology is illustrated for the 128 kDa dimer of bacterial Enzyme I which exists in a variety of open and closed states that are sampled at various points in the catalytic cycles, and for the capsid protein of the human immunodeficiency virus.

Ensembles of a small number of conformations with relative populations

In our previous work, we proposed a new way to represent protein native states, using ensembles of a small number of conformations with relative Populations, or ESP in short. Using Ubiquitin as an example, we showed that using a small number of conformations could greatly reduce the potential of overfitting and assigning relative populations to protein ensembles could significantly improve their quality. To demonstrate that ESP indeed is an excellent alternative to represent protein native states, in this work we compare the quality of two ESP ensembles of Ubiquitin with several well-known regular ensembles or average structure representations. Extensive amount of significant experimental data are employed to achieve a thorough assessment. Our results demonstrate that ESP ensembles, though much smaller in size comparing to regular ensembles, perform equally or even better sometimes in all four different types of experimental data used in the assessment, namely, the residual dipolar couplings, residual chemical shift anisotropy, hydrogen exchange rates, and solution scattering profiles. This work further underlines the significance of having relative populations in describing the native states.

Role of water in protein folding, oligomerization, amyloidosis and miniprotein

The essential involvement of water in most fundamental extra-cellular and intracellular processes of proteins is critically reviewed and evaluated in this article. The role of water in protein behavior displays structural ambivalence; it can protect the disordered peptide-chain by hydration or helps the globular chain-folding, but promotes also the protein aggregation, as well (see: diseases). A variety of amyloid diseases begins as benign protein monomers but develops then into toxic amyloid aggregates of fibrils. Our incomplete knowledge of this process emphasizes the essential need to reveal the principles of governing this oligomerization. To understand the biophysical basis of the simpler in vitro amyloid formation may help to decipher also the in vivo way. Nevertheless, to ignore the central role of the water's effect among these events means to receive an uncompleted picture of the true phenomenon. Therefore this review represents a stopgap role, because the most published studies--with a few exceptions--have been neglected the crucial importance of water in the protein research. The following questions are discussed from the water's viewpoint: (i) interactions between water and proteins, (ii) protein hydration/dehydration, (iii) folding of proteins and miniproteins, (iv) peptide/protein oligomerization, and (v) amyloidosis.

Application of the maximum entropy principle to determine ensembles of intrinsically disordered proteins from residual dipolar couplings

We present a method based on the maximum entropy principle that can re-weight an ensemble of protein structures based on data from residual dipolar couplings (RDCs).