Structural modeling from electron microscopy data

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy Topographic Images

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

Why are large conformational changes well described by harmonic normal modes?

Low-frequency normal modes generated by elastic network models tend to correlate strongly with large conformational changes of proteins, despite their reliance on the harmonic approximation, which is only valid in close proximity of the native structure. We consider 12 variants of the torsional network model (TNM), an elastic network model in torsion angle space, that adopt different sets of torsion angles as degrees of freedom and reproduce with similar quality the thermal fluctuations of proteins but present drastic differences in their agreement with conformational changes. We show that these differences are related to the extent of the deviations from the harmonic approximation, assessed through an anharmonic energy function whose harmonic approximation coincides with the TNM. Our results indicate that mode anharmonicity is more strongly related to its collectivity, i.e., the number of atoms displaced by the mode, than to its amplitude; low-frequency modes can remain harmonic even at large amplitudes, provided they are sufficiently collective. Finally, we assess the potential benefits of different strategies to minimize the impact of anharmonicity. The reduction of the number of degrees of freedom or their regularization by a torsional harmonic potential significantly improves the collectivity and harmonicity of normal modes and the agreement with conformational changes. In contrast, the correction of normal mode frequencies to partially account for anharmonicity does not yield substantial benefits. The TNM program is freely available at https://github.com/ugobas/tnm.

Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps

We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.

Simultaneous Determination of Protein Structure and Dynamics Using Cryo-Electron Microscopy

Cryo-electron microscopy is rapidly emerging as a powerful technique to determine the structures of complex macromolecular systems elusive to other techniques. Because many of these systems are highly dynamical, characterizing their movements is also a crucial step to unravel their biological functions. To achieve this goal, we report an integrative modeling approach to simultaneously determine structure and dynamics of macromolecular systems from cryo-electron microscopy density maps. By quantifying the level of noise in the data and dealing with their ensemble-averaged nature, this approach enables the integration of multiple sources of information to model ensembles of structures and infer their populations. We illustrate the method by characterizing structure and dynamics of the integral membrane receptor STRA6, thus providing insights into the mechanisms by which it interacts with retinol binding protein and translocates retinol across the membrane.

Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling

Cryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map as well as other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.

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.

Energy transport pathway in proteins: Insights from non-equilibrium molecular dynamics with elastic network model

Intra-molecular energy transport between distant functional sites plays important roles in allosterically regulating the biochemical activity of proteins. How to identify the specific intra-molecular signaling pathway from protein tertiary structure remains a challenging problem. In the present work, a non-equilibrium dynamics method based on the elastic network model (ENM) was proposed to simulate the energy propagation process and identify the specific signaling pathways within proteins. In this method, a given residue was perturbed and the propagation of energy was simulated by non-equilibrium dynamics in the normal modes space of ENM. After that, the simulation results were transformed from the normal modes space to the Cartesian coordinate space to identify the intra-protein energy transduction pathways. The proposed method was applied to myosin and the third PDZ domain (PDZ3) of PSD-95 as case studies. For myosin, two signaling pathways were identified, which mediate the energy transductions form the nucleotide binding site to the 50 kDa cleft and the converter subdomain, respectively. For PDZ3, one specific signaling pathway was identified, through which the intra-protein energy was transduced from ligand binding site to the distant opposite side of the protein. It is also found that comparing with the commonly used cross-correlation analysis method, the proposed method can identify the anisotropic energy transduction pathways more effectively.

Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem

To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the Topology Of Secondary Structures) is one tool that attempts to automate the creation of this mapping. By treating the correspondence between the detected structures and the structures predicted from sequence data as a constraint graph problem DP-TOSS achieved good accuracy in its original iteration. In this paper, we propose modifications to the scoring methodology of DP-TOSS to improve its accuracy. Three scoring schemes were applied to DP-TOSS and tested: (i) a skeleton-based scoring function; (ii) a geometry-based analytical function; and (iii) a multi-well potential energy-based function. A test of 25 proteins shows that a combination of these schemes can improve the performance of DP-TOSS to solve the topology determination problem for macromolecule proteins.

Tools for the cryo-EM gold rush: going from the cryo-EM map to the atomistic model

As cryo-electron microscopy (cryo-EM) enters mainstream structural biology, the demand for fitting methods is high. Here, we review existing flexible fitting methods for cryo-EM. We discuss their importance, potential concerns and assessment strategies. We aim to give readers concrete descriptions of cryo-EM flexible fitting methods with corresponding examples.

Flexible Fitting of Atomic Models into Cryo-EM Density Maps Guided by Helix Correspondences

Although electron cryo-microscopy (cryo-EM) has recently achieved resolutions of better than 3 Å, at which point molecular modeling can be done directly from the density map, analysis and annotation of a cryo-EM density map still primarily rely on fitting atomic or homology models to the density map. In this article, we present, to our knowledge, a new method for flexible fitting of known or modeled protein structures into cryo-EM density maps. Unlike existing methods that are guided by local density gradients, our method is guided by correspondences between the α-helices in the density map and model, and does not require an initial rigid-body fitting step. Compared with current methods on both simulated and experimental density maps, our method not only achieves greater accuracy for proteins with large deformations but also runs as fast or faster than many of the other flexible fitting routines.

Flexible fitting to cryo-EM density map using ensemble molecular dynamics simulations

Flexible fitting is a computational algorithm to derive a new conformational model that conforms to low-resolution experimental data by transforming a known structure. A common application is against data from cryo-electron microscopy to obtain conformational models in new functional states. The conventional flexible fitting algorithms cannot derive correct structures in some cases due to the complexity of conformational transitions. In this study, we show the importance of conformational ensemble in the refinement process by performing multiple fittings trials using a variety of different force constants. Application to simulated maps of Ca²⁺ ATPase and diphtheria toxin as well as experimental data of release factor 2 revealed that for these systems, multiple conformations with similar agreement with the density map exist and a large number of fitting trials are necessary to generate good models. Clustering analysis can be an effective approach to avoid over-fitting models. In addition, we show that an automatic adjustment of the biasing force constants during the fitting process, implemented as replica-exchange scheme, can improve the success rate. © 2017 Wiley Periodicals, Inc.

3D Structures of Responsive Nanocompartmentalized Microgels

Compartmentalization in soft matter is important for segregating and coordinating chemical reactions, sequestering (re)active components, and integrating multifunctionality. Advances depend crucially on quantitative 3D visualization in situ with high spatiotemporal resolution. Here, we show the direct visualization of different compartments within adaptive microgels using a combination of in situ electron and super-resolved fluorescence microscopy. We unravel new levels of structural details and address the challenge of reconstructing 3D information from 2D projections for nonuniform soft matter as opposed to monodisperse proteins. Moreover, we visualize the thermally induced shrinkage of responsive core-shell microgels live in water. This strategy opens doors for systematic in situ studies of soft matter systems and their application as smart materials.

HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1

Human herpesviruses are widespread human pathogens with a remarkable impact on worldwide public health. Despite intense decades of research, the molecular details in many aspects of their function remain to be fully characterized. To unravel the details of how these viruses operate, a thorough understanding of the relationships between the involved components is key. Here, we present HVint, a novel protein-protein intraviral interaction resource for herpes simplex virus type 1 (HSV-1) integrating data from five external sources. To assess each interaction, we used a scoring scheme that takes into consideration aspects such as the type of detection method and the number of lines of evidence. The coverage of the initial interactome was further increased using evolutionary information, by importing interactions reported for other human herpesviruses. These latter interactions constitute, therefore, computational predictions for potential novel interactions in HSV-1. An independent experimental analysis was performed to confirm a subset of our predicted interactions. This subset covers proteins that contribute to nuclear egress and primary envelopment events, including VP26, pUL31, pUL40, and the recently characterized pUL32 and pUL21. Our findings support a coordinated crosstalk between VP26 and proteins such as pUL31, pUS9, and the CSVC complex, contributing to the development of a model describing the nuclear egress and primary envelopment pathways of newly synthesized HSV-1 capsids. The results are also consistent with recent findings on the involvement of pUL32 in capsid maturation and early tegumentation events. Further, they open the door to new hypotheses on virus-specific regulators of pUS9-dependent transport. To make this repository of interactions readily accessible for the scientific community, we also developed a user-friendly and interactive web interface. Our approach demonstrates the power of computational predictions to assist in the design of targeted experiments for the discovery of novel protein-protein interactions.

Unraveling low-resolution structural data of large biomolecules by constructing atomic models with experiment-targeted parallel cascade selection simulations

Various low-resolution experimental techniques have gained more and more popularity in obtaining structural information of large biomolecules. In order to interpret the low-resolution structural data properly, one may need to construct an atomic model of the biomolecule by fitting the data using computer simulations. Here we develop, to our knowledge, a new computational tool for such integrative modeling by taking the advantage of an efficient sampling technique called parallel cascade selection (PaCS) simulation. For given low-resolution structural data, this PaCS-Fit method converts it into a scoring function. After an initial simulation starting from a known structure of the biomolecule, the scoring function is used to pick conformations for next cycle of multiple independent simulations. By this iterative screening-after-sampling strategy, the biomolecule may be driven towards a conformation that fits well with the low-resolution data. Our method has been validated using three proteins with small-angle X-ray scattering data and two proteins with electron microscopy data. In all benchmark tests, high-quality atomic models, with generally 1-3 Å from the target structures, are obtained. Since our tool does not need to add any biasing potential in the simulations to deform the structure, any type of low-resolution data can be implemented conveniently.

Numerical geometry of map and model assessment

We are describing best practices and assessment strategies for the atomic interpretation of cryo-electron microscopy (cryo-EM) maps. Multiscale numerical geometry strategies in the Situs package and in secondary structure detection software are currently evolving due to the recent increases in cryo-EM resolution. Criteria that aim to predict the accuracy of fitted atomic models at low (worse than 8Å) and medium (4-8 Å) resolutions remain challenging. However, a high level of confidence in atomic models can be achieved by combining such criteria. The observed errors are due to map-model discrepancies and due to the effect of imperfect global docking strategies. Extending the earlier motion capture approach developed for flexible fitting, we use simulated fiducials (pseudoatoms) at varying levels of coarse-graining to track the local drift of structural features. We compare three tracking approaches: naïve vector quantization, a smoothly deformable model, and a tessellation of the structure into rigid Voronoi cells, which are fitted using a multi-fragment refinement approach. The lowest error is an upper bound for the (small) discrepancy between the crystal structure and the EM map due to different conditions in their structure determination. When internal features such as secondary structures are visible in medium-resolution EM maps, it is possible to extend the idea of point-based fiducials to more complex geometric representations such as helical axes, strands, and skeletons. We propose quantitative strategies to assess map-model pairs when such secondary structure patterns are prominent.

Validation methods for low-resolution fitting of atomic structures to electron microscopy data

Fitting of atomic-resolution structures into reconstructions from electron cryo-microscopy is routinely used to understand the structure and function of macromolecular machines. Despite the fact that a plethora of fitting methods has been developed over recent years, standard protocols for quality assessment and validation of these fits have not been established. Here, we present the general concepts underlying current validation ideas as they relate to fitting of atomic-resolution models into electron cryo-microscopy reconstructions, with an emphasis on reconstructions with resolutions below the sub-nanometer range.