Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling

Bonds and bytes: The odyssey of structural biology

Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively reveals the behavior of biological systems across various spatiotemporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative structural biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in structural biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatiotemporal modeling, and provides an outlook into future directions of this field.

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Protein-Protein Docking: Past, Present, and Future

The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.

CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Motivation

Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme.

Results

We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software.

Availability and implementation

www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe.

Supplementary information

Supplementary data are available at Bioinformatics online.

Transmembrane Protein Docking with JabberDock

Transmembrane proteins act as an intermediary for a broad range of biological process. Making up 20% to 30% of the proteome, their ubiquitous nature has resulted in them comprising 50% of all targets in drug design. Despite their importance, they make up only 4% of all structures in the PDB database, primarily owing to difficulties associated with isolating and characterizing them. Membrane protein docking algorithms could help to fill this knowledge gap, yet only few exist. Moreover, these existing methods achieve success rates lower than the current best soluble proteins docking software. We present and test a pipeline using our software, JabberDock, to dock membrane proteins. JabberDock docks shapes representative of membrane protein structure and dynamics in their biphasic environment. We verify JabberDock's ability to yield accurate predictions by applying it to a benchmark of 20 transmembrane dimers, returning a success rate of 75.0%. This makes our software very competitive among available membrane protein-protein docking tools.

The Polyglutamine Expansion at the N-Terminal of Huntingtin Protein Modulates the Dynamic Configuration and Phosphorylation of the C-Terminal HEAT Domain

The polyQ expansion in huntingtin protein (HTT) is the prime cause of Huntington's disease (HD). The recent cryoelectron microscopy (cryo-EM) structure of HTT-HAP40 complex provided the structural information on its HEAT-repeat domains. Here, we present analyses of the impact of polyQ length on the structure and function of HTT via an integrative structural and biochemical approach. The cryo-EM analysis of normal (Q23) and disease (Q78) type HTTs shows that the structures of apo HTTs significantly differ from the structure of HTT in a HAP40 complex and that the polyQ expansion induces global structural changes in the relative movements among the HTT domains. In addition, we show that the polyQ expansion alters the phosphorylation pattern across HTT and that Ser2116 phosphorylation in turn affects the global structure and function of HTT. These results provide a molecular basis for the effect of the polyQ segment on HTT structure and activity, which may be important for HTT pathology.

Principles for Integrative Structural Biology Studies

Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.

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.

Protein Docking Using a Single Representation for Protein Surface, Electrostatics, and Local Dynamics

Predicting the assembly of multiple proteins into specific complexes is critical to understanding their biological function in an organism and thus the design of drugs to address their malfunction. Proteins are flexible molecules, which inherently pose a problem to any protein docking computational method, where even a simple rearrangement of the side chain and backbone atoms at the interface of binding partners complicates the successful determination of the correct docked pose. Herein, we present a means of representing protein surface, electrostatics, and local dynamics within a single volumetric descriptor. We show that our representations can be physically related to the surface-accessible solvent area and mass of the protein. We then demonstrate that the application of this representation into a protein-protein docking scenario bypasses the need to compensate for, and predict, specific side chain packing at the interface of binding partners. This representation is leveraged in our de novo protein docking software, JabberDock, which can accurately and robustly predict difficult target complexes with an average success rate of >54%, which is comparable to or greater than the currently available methods.

Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space

Flexibility is often a key determinant of protein function. To elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational space. Extensive sampling is, however, required to obtain reliable results, useful to rationalize experimental data or predict outcomes before experiments are carried out. We demonstrate that a generative neural network trained on protein structures produced by molecular simulation can be used to obtain new, plausible conformations complementing pre-existing ones. To demonstrate this, we show that a trained neural network can be exploited in a protein-protein docking scenario to account for broad hinge motions taking place upon binding. Overall, this work shows that neural networks can be used as an exploratory tool for the study of molecular conformational space.

Structural principles that enable oligomeric small heat-shock protein paralogs to evolve distinct functions

Oligomeric proteins assemble with exceptional selectivity, even in the presence of closely related proteins, to perform their cellular roles. We show that most proteins related by gene duplication of an oligomeric ancestor have evolved to avoid hetero-oligomerization and that this correlates with their acquisition of distinct functions. We report how coassembly is avoided by two oligomeric small heat-shock protein paralogs. A hierarchy of assembly, involving intermediates that are populated only fleetingly at equilibrium, ensures selective oligomerization. Conformational flexibility at noninterfacial regions in the monomers prevents coassembly, allowing interfaces to remain largely conserved. Homomeric oligomers must overcome the entropic benefit of coassembly and, accordingly, homomeric paralogs comprise fewer subunits than homomers that have no paralogs.

Modeling the assembly order of multimeric heteroprotein complexes

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.

Integrative modelling of cellular assemblies

A wide variety of experimental techniques can be used for understanding the precise molecular mechanisms underlying the activities of cellular assemblies. The inherent limitations of a single experimental technique often requires integration of data from complementary approaches to gain sufficient insights into the assembly structure and function. Here, we review popular computational approaches for integrative modelling of cellular assemblies, including protein complexes and genomic assemblies. We provide recent examples of integrative models generated for such assemblies by different experimental techniques, especially including data from 3D electron microscopy (3D-EM) and chromosome conformation capture experiments, respectively. We highlight general concepts in integrative modelling and discuss the need for careful formulation and merging of different types of information.

Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies

Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.

Cryo-EM structure of aerolysin variants reveals a novel protein fold and the pore-formation process

Owing to their pathogenical role and unique ability to exist both as soluble proteins and transmembrane complexes, pore-forming toxins (PFTs) have been a focus of microbiologists and structural biologists for decades. PFTs are generally secreted as water-soluble monomers and subsequently bind the membrane of target cells. Then, they assemble into circular oligomers, which undergo conformational changes that allow membrane insertion leading to pore formation and potentially cell death. Aerolysin, produced by the human pathogen Aeromonas hydrophila, is the founding member of a major PFT family found throughout all kingdoms of life. We report cryo-electron microscopy structures of three conformational intermediates and of the final aerolysin pore, jointly providing insight into the conformational changes that allow pore formation. Moreover, the structures reveal a protein fold consisting of two concentric β-barrels, tightly kept together by hydrophobic interactions. This fold suggests a basis for the prion-like ultrastability of aerolysin pore and its stoichiometry.

Aerolysin is a secreted bacterial pore forming toxin that inserts into the host plasma membrane, potentially leading to cell death. Here the authors present Cryo-EM structures of aerolysin arrested at different stages of the pore formation process that provide insight into the conformational changes that allow pore formation.

Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?

The importance of dynamics in integrative modeling of supramolecular assemblies

Revealing the atomistic architecture of supramolecular complexes is a fundamental step toward a deeper understanding of cellular functioning. To date, this formidable task is facilitated by an emerging array of integrative modeling approaches that combine experimental data from different sources. One major challenge these methods have to face is the treatment of the dynamic rearrangements of the individual subunits upon assembly. While this flexibility can be sampled at different levels, integrating native dynamic determinants with available experimental inputs can provide an effective way to reveal the molecular recognition mechanisms at the basis of supramolecular assembly.

Perturbations of the straight transmembrane α-helical structure of the amyloid precursor protein affect its processing by γ-secretase

The amyloid precursor protein (APP) is a widely expressed type I transmembrane (TM) glycoprotein present at the neuronal synapse. The proteolytic cleavage by γ-secretase of its C-terminal fragment produces amyloid-β (Aβ) peptides of different lengths, the deposition of which is an early indicator of Alzheimer disease. At present, there is no consensus on the conformation of the APP-TM domain at the biological membrane. Although structures have been determined by NMR in detergent micelles, their conformation is markedly different. Here we show by using molecular simulations that the APP-TM region systematically prefers a straight α-helical conformation once embedded in a membrane bilayer. However, APP-TM is highly flexible, and its secondary structure is strongly influenced by the surrounding lipid environment, as when enclosed in detergent micelles. This behavior is confirmed when analyzing in silico the atomistic APP-TM population observed by residual dipolar couplings and double electron-electron resonance spectroscopy. These structural and dynamic features are critical in the proteolytic processing of APP by the γ-secretase enzyme, as suggested by a series of Gly(700) mutants. Affecting the hydration and flexibility of APP-TM, these mutants invariantly show an increase in the production of Aβ38 compared with Aβ40 peptides, which is reminiscent of the effect of γ-secretase modulators inhibitors.

Artificial evolution by viability rather than competition

Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions.

Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints.

Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.

Structural biology: Torqueing about pores

Cryo-EM, crystallography, biochemical experiments and computational approaches have been used to study different intermediate states of the Aeromonas hydrophila toxin aerolysin en route to pore formation. These results reveal that an unexpected and marked rotation of the core aerolysin machinery is required to unleash the membrane-spanning regions.

Molecular assembly of the aerolysin pore reveals a swirling membrane-insertion mechanism

Aerolysin is the founding member of a superfamily of β-pore-forming toxins whose pore structure is unknown. We have combined X-ray crystallography, cryo-EM, molecular dynamics and computational modeling to determine the structures of aerolysin mutants in their monomeric and heptameric forms, trapped at various stages of the pore formation process. A dynamic modeling approach based on swarm intelligence was applied, whereby the intrinsic flexibility of aerolysin extracted from new X-ray structures was used to fully exploit the cryo-EM spatial restraints. Using this integrated strategy, we obtained a radically new arrangement of the prepore conformation and a near-atomistic structure of the aerolysin pore, which is fully consistent with all of the biochemical data available so far. Upon transition from the prepore to pore, the aerolysin heptamer shows a unique concerted swirling movement, accompanied by a vertical collapse of the complex, ultimately leading to the insertion of a transmembrane β-barrel.

In situ structural analysis of the Yersinia enterocolitica injectisome

Injectisomes are multi-protein transmembrane machines allowing pathogenic bacteria to inject effector proteins into eukaryotic host cells, a process called type III secretion. Here we present the first three-dimensional structure of Yersinia enterocolitica and Shigella flexneri injectisomes in situ and the first structural analysis of the Yersinia injectisome. Unexpectedly, basal bodies of injectisomes inside the bacterial cells showed length variations of 20%. The in situ structures of the Y. enterocolitica and S. flexneri injectisomes had similar dimensions and were significantly longer than the isolated structures of related injectisomes. The crystal structure of the inner membrane injectisome component YscD appeared elongated compared to a homologous protein, and molecular dynamics simulations documented its elongation elasticity. The ring-shaped secretin YscC at the outer membrane was stretched by 30–40% in situ, compared to its isolated liposome-embedded conformation. We suggest that elasticity is critical for some two-membrane spanning protein complexes to cope with variations in the intermembrane distance.

DOI:http://dx.doi.org/10.7554/eLife.00792.001

Humans and other animals can use the five senses—touch, taste, sight, smell, and hearing—to interpret the world around them. Single-celled organisms, however, must rely on molecular cues to understand their immediate surroundings. In particular, bacteria gather information about external conditions, including potential hosts nearby, by secreting protein sensors that can relay messages back to the cell.

Bacteria export these sensors via secretion systems that enable the organism both to receive information about the environment and to invade a host cell. A total of seven separate secretion systems, known as types I–VII, have been identified. These different secretion systems handle distinct cargoes, allowing the bacterial cell to respond to a range of feedback from the external milieu.

The type III secretion system, also known as the ‘injectisome’, is found in bacterial species that are enclosed by two membranes separated by a periplasmic space. The injectisome comprises different components that combine to form the basal body, which spans the inner and outer membranes, and a projection from the basal body, called the hollow needle, that mediates the export of cargo from a bacterium to its host or the local environment.

The distance between the inner and outer membranes may vary across species or according to environmental conditions, so the basal body must be able to accommodate these changes. However, no mechanism has yet been established that might introduce such elasticity into the injectisome. Now, Kudryashev et al. have generated three-dimensional structures for the injectisomes of two species of bacteria, Shigella flexneri and Yersinia enterocolitica, and shown that the size of the basal body can fluctuate by up to 20%.

Kudryashev et al. imaged whole injectisomes in these two species and found that the height of the basal body was proportional to the distance between the inner and outer membranes. To probe how this could occur, the properties of two proteins that are important components of the basal body were studied in greater detail. YscD, a protein that extends across the periplasmic space, was crystallized and its structure was then determined and used to develop a computer model to assess its compressibility: this model indicated that YscD could stretch or contract by up to 50% of its total length. The outer membrane component YscC also appeared elastic: when the protein was isolated and introduced into synthetic membranes, its length was reduced 30–40% relative to that observed in intact bacterial membranes.

A further experiment confirmed the adaptability of the basal body: when the separation of the membranes was deliberately increased by placing bacteria in a high-salt medium, the basal body extended approximately 10% in length. Cumulatively, therefore, these experiments suggest that the in-built flexibility of the basal body of the injectisome allows bacteria to adjust to environmental changes while maintaining their sensory abilities and host-invasion potential.

DOI:http://dx.doi.org/10.7554/eLife.00792.002