Integrative Modeling of Biomolecular Complexes: HADDOCKing with Cryo-Electron Microscopy Data

Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the data. Furthermore, these techniques struggle to represent the conformational heterogeneity averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a Bayesian inference approach to determine single-structure models as well as structural ensembles from cryo-EM maps. EMMIVox automatically balances experimental information with accurate physico-chemical models of the system and the surrounding environment, including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as inference of accurate B-factors enable determination of structural models and ensembles with both excellent fit to the data and high stereochemical quality, thus outperforming state-of-the-art refinement techniques. EMMIVox represents a flexible approach to determine high-quality structural models that will contribute to advancing our understanding of the molecular mechanisms underlying biological functions.

Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the "resolution revolution," their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.

Docking and molecular dynamics studies of human ezrin protein with a modelled SARS-CoV-2 endodomain and their interaction with potential invasion inhibitors

Human ezrin protein interacts with SARS-CoV S endodomain and restricts virus fusion, entry, and early events of infection. In general, their binding strength and their structural stability determines their successful entry into the host cells. However, the binding affinity of these two endodomains with the ezrin protein has been elusive due to a paucity of knowledge on the 3D structure. This study modelled the endodomains of both SARS-CoV-1 and SARS-CoV-2 and then docked these models with human ezrin protein. This study establishes that the modelled endodomains of both SARS-CoV-1 and SARS-Cov-2 consisted of three disulphide bridges for self-stabilization. Protein-protein docking listed four salt bridges with a higher buried surface area between ezrin-SARS-CoV-1 endodomain compared to that of ezrin-SARS-CoV-2 with six salt bridges with lower buried surface area. Molecular simulation of the ezrin-SARS-CoV-1 endodomain showed better structural stability with lower Root Mean Square Deviation score compared to that of ezrin-SARS-CoV-2 endodomain due to the substitution of alanine with cysteine residue. Protein-ligand docking studies confirmed better ezrin-drug interaction for quercetin, minocycline, calcifediol, calcitriol, selamectin, ivermectin and ergocalciferol. However, protein–ligand simulation confirmed strong drug-protein interaction during simulation for all the above-listed drugs except for ergocalciferol which could not establish its interaction with the protein during simulation. Strong drug binding within the active site pocket therefore restricts the interaction of viral endodomain and simultaneously stabilizes the ezrin protein. Furthermore, the higher stability between the ezrin after their interaction with the drug moiety could restrict the virus fusion and the infection. This study provides a basis for further development of these drug molecules to clinical trials aiming to identify potential drug molecules which can treat COVID-19 infection.

MarkovFit: Structure Fitting for Protein Complexes in Electron Microscopy Maps Using Markov Random Field

An increasing number of protein complex structures are determined by cryo-electron microscopy (cryo-EM). When individual protein structures have been determined and are available, an important task in structure modeling is to fit the individual structures into the density map. Here, we designed a method that fits the atomic structures of proteins in cryo-EM maps of medium to low resolutions using Markov random fields, which allows probabilistic evaluation of fitted models. The accuracy of our method, MarkovFit, performed better than existing methods on datasets of 31 simulated cryo-EM maps of resolution 10 Å , nine experimentally determined cryo-EM maps of resolution less than 4 Å , and 28 experimentally determined cryo-EM maps of resolution 6 to 20 Å .

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR

Hydrogen-deuterium exchange (HDX) measured by nuclear magnetic resonance (NMR) provides structural information for proteins relating to solvent accessibility and flexibility. While this structural information is beneficial, the data cannot be used exclusively to elucidate structures. However, the structural information provided by the HDX-NMR data can be supplemented by computational methods. In previous work, we developed an algorithm in Rosetta to predict structures using qualitative HDX-NMR data (categories of exchange rate). Here we expand on the effort, and utilize quantitative protection factors (PFs) from HDX-NMR for structure prediction. From observed correlations between PFs and solvent accessibility/flexibility measures, we present a scoring function to quantify the agreement with HDX data. Using a benchmark set of 10 proteins, an average improvement of 5.13 Å in root-mean-square deviation (RMSD) is observed for cases of inaccurate Rosetta predictions. Ultimately, seven out of 10 predictions are accurate without including HDX data, and nine out of 10 are accurate when using our PF-based HDX score.

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.

Protein Docking Model Evaluation by Graph Neural Networks

Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions, it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning-based approach named Graph Neural Network-based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph, respectively. GNN-DOVE was trained, validated, and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods, including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models.

Improving integrative 3D modeling into low- to medium-resolution electron microscopy structures with evolutionary couplings

Electron microscopy (EM) continues to provide near-atomic resolution structures for well-behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low- to medium-resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co-evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co-evolution of intermolecular amino acids as a new type of distance restraint in the integrative modeling platform in order to build three-dimensional models of atomic structures into EM maps ranging from 10-14 Å in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo-translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium- to low-resolution EM maps, providing additional information to integrative models lacking in spatial data.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

Protein docking model evaluation by 3D deep convolutional neural networks

MOTIVATION

Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complement experimental methods, many computational methods have been developed to predict structures of protein complexes. One of the challenges in computational protein complex structure prediction is to identify near-native models from a large pool of generated models.

RESULTS

We developed a convolutional deep neural network-based approach named DOcking decoy selection with Voxel-based deep neural nEtwork (DOVE) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein-protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions.

AVAILABILITY AND IMPLEMENTATION

Codes available at http://github.com/kiharalab/DOVE, http://kiharalab.org/dove/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

Exploring Mechanisms of Communication Switching in the Hsp90-Cdc37 Regulatory Complexes with Client Kinases through Allosteric Coupling of Phosphorylation Sites: Perturbation-Based Modeling and Hierarchical Community Analysis of Residue Interaction Networks

Understanding molecular principles underlying chaperone-based modulation of kinase client activity is critically important to dissect functions and activation mechanisms of many oncogenic proteins. The recent experimental studies have suggested that phosphorylation sites in the Hsp90 and Cdc37 proteins can serve as conformational communication switches of chaperone regulation and kinase interactions. However, a mechanism of allosteric coupling between phosphorylation sites in the Hsp90 and Cdc37 during client binding is poorly understood, and the molecular signatures underpinning specific roles of phosphorylation sites in the Hsp90 regulation remain unknown. In this work, we employed a combination of evolutionary analysis, coarse-grained molecular simulations together with perturbation-based network modeling and scanning of the unbound and bound Hsp90 and Cdc37 structures to quantify allosteric effects of phosphorylation sites and identify unique signatures that are characteristic for communication switches of kinase-specific client binding. By using network-based metrics of the dynamic intercommunity bridgeness and community centrality, we characterize specific signatures of phosphorylation switches involved in allosteric regulation. Through perturbation-based analysis of the dynamic residue interaction networks, we show that mutations of kinase-specific phosphorylation switches can induce long-range effects and lead to a global rewiring of the allosteric network and signal transmission in the Hsp90-Cdc37-kinase complex. We determine a specific group of phosphorylation sites in the Hsp90 where mutations may have a strong detrimental effect on allosteric interaction network, providing insight into the mechanism of phosphorylation-induced communication switching. The results demonstrate that kinase-specific phosphorylation switches of communications in the Hsp90 may be partly predisposed for their regulatory role based on preexisting allosteric propensities.

Structural Biology in the Multi-Omics Era

Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.

Integrative biology of native cell extracts: a new era for structural characterization of life processes

Advances in electron microscopy have provided unprecedented access to the structural characterization of large, flexible and heterogeneous complexes. Until recently, cryo-electron microscopy (cryo-EM) has been applied to understand molecular organization in either highly purified, isolated biomolecules or in situ. An emerging field is developing, bridging the gap between the two approaches, and focuses on studying molecular organization in native cell extracts. This field has demonstrated its potential by resolving the structure of fungal fatty acid synthase (FAS) at 4.7 Å [Fourier shell correlation (FSC) = 0.143]; FAS was not only less than 50% enriched, but also retained higher-order binders, previously unknown. Although controversial in the sense that the lysis step might introduce artifacts, cell extracts preserve aspects of cellular function. In addition, cell extracts are accessible, besides cryo-EM, to modern proteomic methods, chemical cross-linking, network biology and biophysical modeling. We expect that automation in imaging cell extracts, along with the integration of molecular/cell biology approaches, will provide remarkable achievements in the study of closer-to-life biomolecular states of pronounced biotechnological and medical importance. Such steps will, eventually, bring us a step closer to the biophysical description of cellular processes in an integrative, holistic approach.

Modeling of Multimolecular Complexes

Macromolecular complexes play a key role in cellular function. Predicting the structure and dynamics of these complexes is one of the key challenges in structural biology. Docking applications have traditionally been used to predict pairwise interactions between proteins. However, few methods exist for modeling multi-protein assemblies. Here we present two methods, CombDock and DockStar, that can predict multi-protein assemblies starting from subunit structural models. CombDock can assemble subunits without any assumptions about the pairwise interactions between subunits, while DockStar relies on the interaction graph or, alternatively, a homology model or a cryo-electron microscopy (EM) density map of the entire complex. We demonstrate the two methods using RNA polymerase II with 12 subunits and TRiC/CCT chaperonin with 16 subunits.

Protein-Protein Modeling Using Cryo-EM Restraints

Recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low- to medium-resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modeling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, this often restricts the modeling to a unique source of data, the EM density map.In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modeling process. In the first step, rigid-body fitting is performed with PowerFit in order to identify the most likely locations of the molecules into the map. These are then used as centroids to which distance restraints are defined from the center of mass of the components of the complex for the initial rigid-body docking. The EM density is then directly used as an additional restraint energy term, which can be combined with all the other types of data supported by HADDOCK. This protocol relies on the new version 2.4 of both the HADDOCK webserver and software. Preparation steps consisting of cropping the EM map and rigid-body fitting of the atomic structure are explained. Then, the EM-driven docking protocol using HADDOCK is illustrated.

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.

Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures

Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.

Modelling structures in cryo-EM maps

Recent advances in structure determination of sub-cellular structures using cryo-electron microscopy and tomography have enabled us to understand their architecture in a more detailed manner and gain insight into their function. The choice of approach to use for atomic model building, fitting, refinement and validation in the 3D map resulting from these experiments depends primarily on the resolution of the map and the prior information on the corresponding model. Here, we survey some of such methods and approaches and highlight their uses in specific recent examples.

Interpretation of medium resolution cryoEM maps of multi-protein complexes

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CryoEM maps at medium (3.5–6 Å) resolution can be challenging to interpret.

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Integration of multiple methods can inform cryoEM studies.

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Mass spectrometry and biochemistry facilitate map interpretation and model building.

Electron cryo-microscopy (cryoEM) is used to determine structures of biological molecules, including multi-protein complexes. Maps at better than 3.0 Å resolution are relatively straightforward to interpret since atomic models of proteins and nucleic acids can be built directly. Still, these resolutions are often difficult to achieve, and map quality frequently varies within a structure. This results in data that are challenging to interpret, especially when crystal structures or suitable homology models are not available. Recent advances in mass spectrometry techniques, computational methods and model building tools facilitate subunit/domain fitting into maps, elucidation of protein contacts, and de novo generation of atomic models. Here, we review techniques for map interpretation and provide examples from recent studies of multi-protein complexes.

Archiving and disseminating integrative structure models

Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.

Integrative Structure Modeling: Overview and Assessment

Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.

West-Life: A Virtual Research Environment for structural biology

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Data processing and data management services for structural biology.

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Enhancements to existing web services for structure solution and analysis.

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New pipelines to link these services into more complex higher-level workflows.

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New data management facilities.

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Making the benefits of European e-Infrastructures more accessible to structural biologists.

The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

Tracking Higher Order Protein Structure by Hydrogen-Deuterium Exchange Mass Spectrometry

BACKGROUND

Structural biology has provided a fundamental understanding of protein structure and mechanistic insight into their function. However, high-resolution structures alone are insufficient for a complete understanding of protein behavior. Higher energy conformations, conformational changes, and subtle structural fluctuations that underlie the proper function of proteins are often difficult to probe using traditional structural approaches. Hydrogen/Deuterium Exchange with Mass Spectrometry (HDX-MS) provides a way to probe the accessibility of backbone amide protons under native conditions, which reports on local structural dynamics of solution protein structure that can be used to track complex structural rearrangements that occur in the course of a protein's function.

CONCLUSION

In the last 20 years the advances in labeling techniques, sample preparation, instrumentation, and data analysis have enabled HDX to gain insights into very complex biological systems. Analysis of challenging targets such as membrane protein complexes is now feasible and the field is paving the way to the analysis of more and more complex systems.

Distinct Interaction Sites of Rac GTPase with WAVE Regulatory Complex Have Non-redundant Functions in Vivo

Cell migration often involves the formation of sheet-like lamellipodia generated by branched actin filaments. The branches are initiated when Arp2/3 complex [1] is activated by WAVE regulatory complex (WRC) downstream of small GTPases of the Rac family [2]. Recent structural studies defined two independent Rac binding sites on WRC within the Sra-1/PIR121 subunit of the pentameric WRC [3, 4], but the functions of these sites in vivo have remained unknown. Here we dissect the mechanism of WRC activation and the in vivo relevance of distinct Rac binding sites on Sra-1, using CRISPR/Cas9-mediated gene disruption of Sra-1 and its paralog PIR121 in murine B16-F1 cells combined with Sra-1 mutant rescue. We show that the A site, positioned adjacent to the binding region of WAVE-WCA mediating actin and Arp2/3 complex binding, is the main site for allosteric activation of WRC. In contrast, the D site toward the C terminus is dispensable for WRC activation but required for optimal lamellipodium morphology and function. These results were confirmed in evolutionarily distant Dictyostelium cells. Moreover, the phenotype seen in D site mutants was recapitulated in Rac1 E31 and F37 mutants; we conclude these residues are important for Rac-D site interaction. Finally, constitutively activated WRC was able to induce lamellipodia even after both Rac interaction sites were lost, showing that Rac interaction is not essential for membrane recruitment. Our data establish that physical interaction with Rac is required for WRC activation, in particular through the A site, but is not mandatory for WRC accumulation in the lamellipodium.

CryoEM-based hybrid modeling approaches for structure determination

Recent advances in cryo-electron microscopy (cryoEM) have dramatically improved the resolutions at which vitrified biological specimens can be studied, revealing new structural and mechanistic insights over a broad range of spatial scales. Bolstered by these advances, much effort has been directed toward the development of hybrid modeling methodologies for the construction and refinement of high-fidelity atomistic models from cryoEM data. In this brief review, we will survey the key elements of cryoEM-based hybrid modeling, providing an overview of available computational tools and strategies as well as several recent applications.

Protein-peptide docking: opportunities and challenges

Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.

Development of a Prototype System for Archiving Integrative/Hybrid Structure Models of Biological Macromolecules

Essential processes in biology are carried out by large macromolecular assemblies, whose structures are often difficult to determine by traditional methods. Increasingly, researchers combine measured data and computed information from several complementary methods to obtain "hybrid" or "integrative" structural models of macromolecules and their assemblies. These integrative/hybrid (I/H) models are not archived in the PDB because of the absence of standard data representations and processing mechanisms. Here we present the development of data standards and a prototype system for archiving I/H models. The data standards provide the definitions required for representing I/H models that span multiple spatiotemporal scales and conformational states, as well as spatial restraints derived from different experimental techniques. Based on these data definitions, we have built a prototype system called PDB-Dev, which provides the infrastructure necessary to archive I/H structural models. PDB-Dev is now accepting structures and is open to the community for new submissions.

Simultaneous detection of intra- and inter-molecular paramagnetic relaxation enhancements in protein complexes

Paramagnetic relaxation enhancement (PRE) measurements constitute a powerful approach for detecting both permanent and transient protein-protein interactions. Typical PRE experiments require an intrinsic or engineered paramagnetic site on one of the two interacting partners; while a second, diamagnetic binding partner is labeled with stable isotopes (¹⁵N or ¹³C). Multiple paramagnetic labeled centers or reversed labeling schemes are often necessary to obtain sufficient distance restraints to model protein-protein complexes, making this approach time consuming and expensive. Here, we show a new strategy that combines a modified pulse sequence (¹H_N-Γ₂-CCLS) with an asymmetric labeling scheme to enable the detection of both intra- and inter-molecular PREs simultaneously using only one sample preparation. We applied this strategy to the non-covalent dimer of ubiquitin. Our method confirmed the previously identified binding interface for the transient di-ubiquitin complex, and at the same time, unveiled the internal structural dynamics rearrangements of ubiquitin upon interaction. In addition to reducing the cost of sample preparation and speed up PRE measurements, by detecting the intra-molecular PRE this new strategy will make it possible to measure and calibrate inter-molecular distances more accurately for both symmetric and asymmetric protein-protein complexes.

PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps. Corrigendum

A revised Table 6 and Supporting Information are provided for the article by Kuzu et al. [(2016), Acta Cryst. D72, 1137-1148].

Anticipating innovations in structural biology

In this review, we describe how the interplay among science, technology and community interests contributed to the evolution of four structural biology data resources. We present the method by which data deposited by scientists are prepared for worldwide distribution, and argue that data archiving in a trusted repository must be an integral part of any scientific investigation.

Rigid-Body Fitting of Atomic Models on 3D Density Maps of Electron Microscopy

Cryo electron microscopy has revolutionarily evolved for the determination of the 3D structure of macromolecular complexes. The modeling procedures on the 3D density maps of electron microscopy are roughly classified into three categories: fitting, de novo modeling and refinement. The registered atomic models from the maps have mostly been hand-built and auto-refined. Several programs aiming at automatic modeling have also been developed using various kinds of molecular representations. Among these three classes of the modeling procedures, the rigid body fitting is reviewed here, because it is the most basic modeling process applied before the other steps. The fitting problems are classified as the fittings of single subunit or multiple subunits, and the fittings on global or local parts of maps. A higher resolution map enables more local fitting. Various molecular representations have been employed in the fitting programs. A point and digital image models are generally used to represent molecules, but new representations, such as the Gaussian mixture model, have been applied recently.

NMR Reveals the Interplay among the AMSH SH3 Binding Motif, STAM2, and Lys63-Linked Diubiquitin

AMSH [associated molecule with a Src homology 3 domain of signal transducing adaptor molecule (STAM)] is one of the deubiquitinating enzymes associated in the regulation of endocytic cargo trafficking. It shows an exquisite selectivity for Lys63-linked polyubiquitin chains that are the main chains involved in cargo sorting. The first step requires the ESCRT-0 complex that comprises the STAM and hepatocyte growth factor-regulated substrate (Hrs) proteins. Previous studies have shown that the presence of the STAM protein increases the efficiency of Lys63-linked polyubiquitin chain cleavage by AMSH, one of the deubiquitinating enzyme involved in lysosomal degradation. In the present study, we are seeking to understand if a particular structural organization among these three key players is responsible for the stimulation of the catalytic activity of AMSH. To address this question, we first monitored the interaction between the ubiquitin interacting motif (UIM)-SH3 construct of STAM2 and the Lys63-linked diubiquitin (Lys63-Ub₂) chains by means of NMR. We show that Lys63-Ub₂ is able to bind either the UIM or the SH3 domain without any selectivity. We further demonstrate that the SH3 binding motif (SBM) of AMSH (AMSH-SBM) outcompetes Lys63-Ub₂ for binding SH3. Additionally, we show how different AMSH-SBM variants, modified by their sequence and length, exhibit similar equilibrium dissociation constants when binding SH3 but significantly differ in their dissociation rate constants. Finally, we report the solution NMR structure of the AMSH-SBM/SH3 complex and propose a structural organization where the AMSH-SBM interacts with the STAM2-SH3 domain and contributes to the correct positioning of AMSH prior to polyubiquitin chains' cleavage.

PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps

The structures of protein assemblies are important for elucidating cellular processes at the molecular level. Three-dimensional electron microscopy (3DEM) is a powerful method to identify the structures of assemblies, especially those that are challenging to study by crystallography. Here, a new approach, PRISM-EM, is reported to computationally generate plausible structural models using a procedure that combines crystallographic structures and density maps obtained from 3DEM. The predictions are validated against seven available structurally different crystallographic complexes. The models display mean deviations in the backbone of <5 Å. PRISM-EM was further tested on different benchmark sets; the accuracy was evaluated with respect to the structure of the complex, and the correlation with EM density maps and interface predictions were evaluated and compared with those obtained using other methods. PRISM-EM was then used to predict the structure of the ternary complex of the HIV-1 envelope glycoprotein trimer, the ligand CD4 and the neutralizing protein m36.

Use of evolutionary information in the fitting of atomic level protein models in low resolution cryo-EM map of a protein assembly improves the accuracy of the fitting

Protein-protein interface residues, especially those at the core of the interface, exhibit higher conservation than residues in solvent exposed regions. Here, we explore the ability of this differential conservation to evaluate fittings of atomic models in low-resolution cryo-EM maps and select models from the ensemble of solutions that are often proposed by different model fitting techniques. As a prelude, using a non-redundant and high-resolution structural dataset involving 125 permanent and 95 transient complexes, we confirm that core interface residues are conserved significantly better than nearby non-interface residues and this result is used in the cryo-EM map analysis. From the analysis of inter-component interfaces in a set of fitted models associated with low-resolution cryo-EM maps of ribosomes, chaperones and proteasomes we note that a few poorly conserved residues occur at interfaces. Interestingly a few conserved residues are not in the interface, though they are close to the interface. These observations raise the potential requirement of refitting the models in the cryo-EM maps. We show that sampling an ensemble of models and selection of models with high residue conservation at the interface and in good agreement with the density helps in improving the accuracy of the fit. This study indicates that evolutionary information can serve as an additional input to improve and validate fitting of atomic models in cryo-EM density maps.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling

Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es.

γ-TEMPy: Simultaneous Fitting of Components in 3D-EM Maps of Their Assembly Using a Genetic Algorithm

We have developed a genetic algorithm for building macromolecular complexes using only a 3D-electron microscopy density map and the atomic structures of the relevant components. For efficient sampling the method uses map feature points calculated by vector quantization. The fitness function combines a mutual information score that quantifies the goodness of fit with a penalty score that helps to avoid clashes between components. Testing the method on ten assemblies (containing 3–8 protein components) and simulated density maps at 10, 15, and 20 Å resolution resulted in identification of the correct topology in 90%, 70%, and 60% of the cases, respectively. We further tested it on four assemblies with experimental maps at 7.2–23.5 Å resolution, showing the ability of the method to identify the correct topology in all cases. We have also demonstrated the importance of the map feature-point quality on assembly fitting in the lack of additional experimental information.

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γ-TEMPy uses a genetic algorithm to fit multiple components into 3D-EM density maps

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The fitness score is a combination of a Mutual Information score and a clash penalty

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Efficient sampling is aided by using map feature points from vector quantization

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Native topologies for assemblies containing up to eight components can be predicted