Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies

Computational modeling of RNA 3D structure based on experimental data

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase

Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

De Novo Structural Pattern Mining in Cellular Electron Cryotomograms

Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called "Multi-Pattern Pursuit" for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis.

Unspinning chromatin: Revealing the dynamic nucleosome landscape by NMR

NMR is an essential technique for obtaining information at atomic resolution on the structure, motions and interactions of biomolecules. Here, we review the contribution of NMR to our understanding of the fundamental unit of chromatin: the nucleosome. Nucleosomes compact the genome by wrapping the DNA around a protein core, the histone octamer, thereby protecting genomic integrity. Crucially, the imposed barrier also allows strict regulation of gene expression, DNA replication and DNA repair processes through an intricate system of histone and DNA modifications and a wide range of interactions between nucleosomes and chromatin factors. In this review, we describe how NMR has contributed to deciphering the molecular basis of nucleosome function. Starting from pioneering studies in the 1960s using natural abundance NMR studies, we focus on the progress in sample preparation and NMR methodology that has allowed high-resolution studies on the nucleosome and its subunits. We summarize the results and approaches of state-of-the-art NMR studies on nucleosomal DNA, histone complexes, nucleosomes and nucleosomal arrays. These studies highlight the particular strength of NMR in studying nucleosome dynamics and nucleosome-protein interactions. Finally, we look ahead to exciting new possibilities that will be afforded by on-going developments in solution and solid-state NMR. By increasing both the depth and breadth of nucleosome NMR studies, it will be possible to offer a unique perspective on the dynamic landscape of nucleosomes and its interacting proteins.

Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

Optimizing model representation for integrative structure determination of macromolecular assemblies

Integrative structure determination of macromolecular assemblies requires specifying the representation of the modeled structure, a scoring function for ranking alternative models based on diverse types of data, and a sampling method for generating these models. Structures are often represented at atomic resolution, although ad hoc simplified representations based on generic guidelines and/or trial and error are also used. In contrast, we introduce here the concept of optimizing representation. To illustrate this concept, the optimal representation is selected from a set of candidate representations based on an objective criterion that depends on varying amounts of information available for different parts of the structure. Specifically, an optimal representation is defined as the highest-resolution representation for which sampling is exhaustive at a precision commensurate with the precision of the representation. Thus, the method does not require an input structure and is applicable to any input information. We consider a space of representations in which a representation is a set of nonoverlapping, variable-length segments (i.e., coarse-grained beads) for each component protein sequence. We also implement a method for efficiently finding an optimal representation in our open-source Integrative Modeling Platform (IMP) software (https://integrativemodeling.org/). The approach is illustrated by application to three complexes of two subunits and a large assembly of 10 subunits. The optimized representation facilitates exhaustive sampling and thus can produce a more accurate model and a more accurate estimate of its uncertainty for larger structures than were possible previously.

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.

Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.

Neutron scattering in the biological sciences: progress and prospects

The scattering of neutrons can be used to provide information on the structure and dynamics of biological systems on multiple length and time scales. Pursuant to a National Science Foundation-funded workshop in February 2018, recent developments in this field are reviewed here, as well as future prospects that can be expected given recent advances in sources, instrumentation and computational power and methods. Crystallography, solution scattering, dynamics, membranes, labeling and imaging are examined. For the extraction of maximum information, the incorporation of judicious specific deuterium labeling, the integration of several types of experiment, and interpretation using high-performance computer simulation models are often found to be particularly powerful.

A Metastable Contact and Structural Disorder in the Estrogen Receptor Transactivation Domain

The N-terminal transactivation domain (NTD) of estrogen receptor alpha, a well-known member of the family of intrinsically disordered proteins, mediates the receptor's transactivation function. However, an accurate molecular dissection of NTD's structure-function relationships remains elusive. Here, we show that the NTD adopts a mostly disordered, unexpectedly compact conformation that undergoes structural expansion on chemical denaturation. By combining small-angle X-ray scattering, hydroxyl radical protein footprinting, and computational modeling, we derive the ensemble-structures of the NTD and determine its ensemble-contact map revealing metastable long-range contacts, e.g., between residues I33 and S118. We show that mutation at S118, a known phosphorylation site, promotes conformational changes and increases coactivator binding. We further demonstrate via fluorine-19 (¹⁹F) nuclear magnetic resonance that mutations near I33 alter ¹⁹F chemical shifts at S118, confirming the proposed I33-S118 contact in the ensemble of structural disorder. These findings extend our understanding of how specific contact metastability mediates critical functions of disordered proteins.

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.

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

Mechanism of activating mutations and allosteric drug inhibition of the phosphatase SHP2

Protein tyrosine phosphatase SHP2 functions as a key regulator of cell cycle control, and activating mutations cause several cancers. Here, we dissect the energy landscape of wild-type SHP2 and the oncogenic mutation E76K. NMR spectroscopy and X-ray crystallography reveal that wild-type SHP2 exchanges between closed, inactive and open, active conformations. E76K mutation shifts this equilibrium toward the open state. The previously unknown open conformation is characterized, including the active-site WPD loop in the inward and outward conformations. Binding of the allosteric inhibitor SHP099 to E76K mutant, despite much weaker, results in an identical structure as the wild-type complex. A conformational selection to the closed state reduces drug affinity which, combined with E76K’s much higher activity, demands significantly greater SHP099 concentrations to restore wild-type  activity levels. The differences in structural ensembles and drug-binding kinetics of cancer-associated SHP2 forms may stimulate innovative ideas for developing more potent inhibitors for activated SHP2 mutants.

The protein tyrosine phosphatase SHP2 is a key regulator of cell cycle control. Here the authors combine NMR measurements and X-ray crystallography and show that wild-type SHP2 dynamically exchanges between a closed inactive conformation and an open activated form and that the oncogenic E76K mutation shifts the equilibrium to the open state, which is reversed by binding of the allosteric inhibitor SHP099.

Structural dynamics of the E6AP/UBE3A-E6-p53 enzyme-substrate complex

Deregulation of the ubiquitin ligase E6AP is causally linked to the development of human disease, including cervical cancer. In complex with the E6 oncoprotein of human papillomaviruses, E6AP targets the tumor suppressor p53 for degradation, thereby contributing to carcinogenesis. Moreover, E6 acts as a potent activator of E6AP by a yet unknown mechanism. However, structural information explaining how the E6AP-E6-p53 enzyme-substrate complex is assembled, and how E6 stimulates E6AP, is largely missing. Here, we develop and apply different crosslinking mass spectrometry-based approaches to study the E6AP-E6-p53 interplay. We show that binding of E6 induces conformational rearrangements in E6AP, thereby positioning E6 and p53 in the immediate vicinity of the catalytic center of E6AP. Our data provide structural and functional insights into the dynamics of the full-length E6AP-E6-p53 enzyme-substrate complex, demonstrating how E6 can stimulate the ubiquitin ligase activity of E6AP while facilitating ubiquitin transfer from E6AP onto p53.

Protein Dynamics in Solution by Quantitative Crosslinking/Mass Spectrometry

The dynamics of protein structures and their interactions are responsible for many cellular processes. The rearrangements and interactions of proteins, which are often transient, occur in solution and may require a biological environment that is difficult to maintain in traditional structural biological approaches. Quantitative crosslinking/mass spectrometry (QCLMS) has emerged as an excellent method to fill this gap. Numerous recent applications of the technique have demonstrated that protein dynamics can now be studied in solution at sufficient resolution to gain valuable biological insights, suggesting that extending these investigations to native environments is possible. These breakthroughs have been based on the maturation of CLMS at large, and its recent fusion with quantitative proteomics. We provide here an overview of the current state of the technique, the available workflows and their applications, and remaining challenges.

In-solution dynamics of protein structures and their interactions can be studied by QCLMS.

Successful applications of QCLMS provide insights into multiple different biological processes.

Recent advances in QCLMS allow analyses in the context of native cellular environments, including living cells.

Alternative workflows allow researchers to tailor the analysis to their biological question.

Progress in data processing now offers this technique to researchers with limited initial expertise in crosslinking and quantitative proteomics.

Single-molecule studies of flavivirus envelope dynamics: Experiment and computation

Flaviviruses are simple enveloped viruses exhibiting complex structural and functional heterogeneities. Decades of research have provided crucial basic insights, antiviral medication and moderately successful gene therapy trials. The most infectious particle is, however, not always the most abundant one in a population, questioning the utility of classic ensemble-averaging virology approaches. Indeed, viral replication is often not particularly efficient, prone to errors or containing parallel routes. Here, we review different single-molecule sensitive fluorescence methods that are employed to investigate flaviviruses. In particular, we review how (i) time-resolved Förster resonance energy transfer (trFRET) was applied to probe dengue envelope conformations; (ii) FRET-fluorescence correlation spectroscopy to investigate dengue envelope intrinsic dynamics and (iii) single particle tracking to follow the path of dengue viruses in cells. We also discuss how such methods may be supported by molecular dynamics (MD) simulations over a range of spatio-temporal scales, to provide complementary data on the structure and dynamics of flaviviral systems. We describe recent improvements in multiscale MD approaches that allowed the simulation of dengue particle envelopes in near-atomic resolution. We hope this review is an incentive for setting up and applying similar single-molecule studies and combine them with MD simulations to investigate structural dynamics of entire flavivirus particles over the nanosecond-to-millisecond time-scale and follow viruses during infection in cells over milliseconds to minutes.

Searching for 3D structural models from a library of biological shapes using a few 2D experimental images

Background

Advancements in biophysical experimental techniques have pushed the limits in terms of the types of phenomena that can be characterized, the amount of data that can be produced and the resolution at which we can visualize them. Single particle techniques such as Electron Microscopy (EM) and X-ray free electron laser (XFEL) scattering require a large number of 2D images collected to resolve three-dimensional (3D) structures. In this study, we propose a quick strategy to retrieve potential 3D shapes, as low-resolution models, from a few 2D experimental images by searching a library of 2D projection images generated from existing 3D structures.

Results

We developed the protocol to assemble a non-redundant set of 3D shapes for generating the 2D image library, and to retrieve potential match 3D shapes for query images, using EM data as a test. In our strategy, we disregard differences in volume size, giving previously unknown structures and conformations a greater number of 3D biological shapes as possible matches. We tested the strategy using images from three EM models as query images for searches against a library of 22750 2D projection images generated from 250 random EM models. We found that our ability to identify 3D shapes that match the query images depends on how complex the outline of the 2D shapes are and whether they are represented in the search image library.

Conclusions

Through our computational method, we are able to quickly retrieve a 3D shape from a few 2D projection images. Our approach has the potential for exploring other types of 2D single particle structural data such as from XFEL scattering experiments, for providing a tool to interpret low-resolution data that may be insufficient for 3D reconstruction, and for estimating the mixing of states or conformations that could exist in such experimental data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2358-0) contains supplementary material, which is available to authorized users.

Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1

Tight binding of Gdown1 represses RNA polymerase II (Pol II) function in a manner that is reversed by Mediator, but the structural basis of these processes is unclear. Although Gdown1 is intrinsically disordered, its Pol II interacting domains were localized and shown to occlude transcription factor IIF (TFIIF) and transcription factor IIB (TFIIB) binding by perfect positioning on their Pol II interaction sites. Robust binding of Gdown1 to Pol II is established by cooperative interactions of a strong Pol II binding region and two weaker binding modulatory regions, thus providing a mechanism both for tight Pol II binding and transcription inhibition and for its reversal. In support of a physiological function for Gdown1 in transcription repression, Gdown1 co-localizes with Pol II in transcriptionally silent nuclei of early Drosophila embryos but re-localizes to the cytoplasm during zygotic genome activation. Our study reveals a self-inactivation through Gdown1 binding as a unique mode of repression in Pol II function.

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

Biophysical experiments and biomolecular simulations: A perfect match?

A fundamental challenge in biological research is achieving an atomic-level description and mechanistic understanding of the function of biomolecules. Techniques for biomolecular simulations have undergone substantial developments, and their accuracy and scope have expanded considerably. Progress has been made through an increasingly tight integration of experiments and simulations, with experiments being used to refine simulations and simulations used to interpret experiments. Here we review the underpinnings of this progress, including methods for more efficient conformational sampling, accuracy of the physical models used, and theoretical approaches to integrate experiments and simulations. These developments are enabling detailed studies of complex biomolecular assemblies.

Quantitative imaging of chromatin decompaction in living cells

Chromatin organization is highly dynamic and regulates transcription. Upon transcriptional activation, chromatin is remodeled and referred to as "open," but quantitative and dynamic data of this decompaction process are lacking. Here, we have developed a quantitative high resolution-microscopy assay in living yeast cells to visualize and quantify chromatin dynamics using the GAL7-10-1 locus as a model system. Upon transcriptional activation of these three clustered genes, we detect an increase of the mean distance across this locus by >100 nm. This decompaction is linked to active transcription but is not sensitive to the histone deacetylase inhibitor trichostatin A or to deletion of the histone acetyl transferase Gcn5. In contrast, the deletion of SNF2 (encoding the ATPase of the SWI/SNF chromatin remodeling complex) or the deactivation of the histone chaperone complex FACT lead to a strongly reduced decompaction without significant effects on transcriptional induction in FACT mutants. Our findings are consistent with nucleosome remodeling and eviction activities being major contributors to chromatin reorganization during transcription but also suggest that transcription can occur in the absence of detectable decompaction.

Lipidated apolipoprotein E4 structure and its receptor binding mechanism determined by a combined cross-linking coupled to mass spectrometry and molecular dynamics approach

Apolipoprotein E (apoE) is a forefront actor in the transport of lipids and the maintenance of cholesterol homeostasis, and is also strongly implicated in Alzheimer’s disease. Upon lipid-binding apoE adopts a conformational state that mediates the receptor-induced internalization of lipoproteins. Due to its inherent structural dynamics and the presence of lipids, the structure of the biologically active apoE remains so far poorly described. To address this issue, we developed an innovative hybrid method combining experimental data with molecular modeling and dynamics to generate comprehensive models of the lipidated apoE4 isoform. Chemical cross-linking combined with mass spectrometry provided distance restraints, characterizing the three-dimensional organization of apoE4 molecules at the surface of lipidic nanoparticles. The ensemble of spatial restraints was then rationalized in an original molecular modeling approach to generate monomeric models of apoE4 that advocated the existence of two alternative conformations. These two models point towards an activation mechanism of apoE4 relying on a regulation of the accessibility of its receptor binding region. Further, molecular dynamics simulations of the dimerized and lipidated apoE4 monomeric conformations revealed an elongation of the apoE N-terminal domain, whereby helix 4 is rearranged, together with Arg172, into a proper orientation essential for lipoprotein receptor association. Overall, our results show how apoE4 adapts its conformation for the recognition of the low density lipoprotein receptor and we propose a novel mechanism of activation for apoE4 that is based on accessibility and remodeling of the receptor binding region.

Among the proteins involved in the transport of lipids and their distribution to the cells, apolipoprotein E (apoE) mediates the internalization of cholesterol rich lipoproteins by acting as a ligand for cell-surface receptors. In the central nervous system, while apoE is the major cholesterol transport protein, a dysfunction of apoE in the transport and metabolism of lipids is associated with Alzheimer’s disease. A molecular understanding of the mechanisms underlying the receptor binding abilities of apoE is crucial to address its biological functions, but is so far hindered by the dynamic and complex nature of these assemblies. We have designed an original hybrid approach combining experimental data and bioinformatics tools to generate high resolution models of lipidated apoE. Based on these models, we can propose how apoE adapts its conformation at the surface of lipid nanoparticles. Further, we propose a novel mechanism of regulation of the activation and receptor recognition of apoE that could prove valuable to interpret its role in Alzheimer and apoE-related cardiovascular diseases.

An expanded allosteric network in PTP1B by multitemperature crystallography, fragment screening, and covalent tethering

Allostery is an inherent feature of proteins, but it remains challenging to reveal the mechanisms by which allosteric signals propagate. A clearer understanding of this intrinsic circuitry would afford new opportunities to modulate protein function. Here, we have identified allosteric sites in protein tyrosine phosphatase 1B (PTP1B) by combining multiple-temperature X-ray crystallography experiments and structure determination from hundreds of individual small-molecule fragment soaks. New modeling approaches reveal 'hidden' low-occupancy conformational states for protein and ligands. Our results converge on allosteric sites that are conformationally coupled to the active-site WPD loop and are hotspots for fragment binding. Targeting one of these sites with covalently tethered molecules or mutations allosterically inhibits enzyme activity. Overall, this work demonstrates how the ensemble nature of macromolecular structure, revealed here by multitemperature crystallography, can elucidate allosteric mechanisms and open new doors for long-range control of protein function.

Proteins perform many important jobs in each of the cells in our bodies, such as transporting other molecules and helping chemical reactions to occur. The part of the protein directly involved in these tasks is called the active site. Other areas of the protein can communicate with the active site to switch the protein on or off. This method of control is known as allostery.

Switching proteins on and off could help us to develop treatments for certain diseases. For example, a protein called PTP1B reduces how well cells can respond to insulin. Switching this protein off could therefore help to treat diabetes. However, much like it’s hard to guess how a light switch is wired to a light bulb without seeing behind the walls, it is hard to predict which remote areas of a protein are ‘wired’ to the active site.

Keedy, Hill et al. have now used two complementary methods to examine the structure of PTP1B and find new allosteric sites. The first method captured a series of X-ray images from crystallized molecules of the protein held at different temperatures. This revealed areas of PTP1B that can move like windshield wipers to communicate with each other. The second method soaked PTP1B crystals in trays with hundreds of drug-sized molecules and assessed which sites on the protein the molecules bound to. The molecules generally bound to just a few sites of the protein. Further tests on one of these sites showed that it can communicate with the active site to turn the protein on or off.

Further work will be needed to develop drugs that could treat diabetes by binding to the newly identified allosteric sites in PTP1B. More generally, the methods developed by Keedy, Hill et al. could be used to study allostery in other important proteins.

Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry

Modeling macromolecular assemblies with restraints from crosslinking mass spectrometry (XL-MS) tends to focus solely on distance violation. Recently, we identified three different modeling features inherent in crosslink data: (1) expected distance between crosslinked residues; (2) violation of the crosslinker's maximum bound; and (3) solvent accessibility of crosslinked residues. Here, we implement these features in a scoring function. cMNXL, and demonstrate that it outperforms the commonlyused crosslink distance violation. We compare the different methods of calculating the distance between crosslinked residues, which shows no significant change in performance when using Euclidean distance compared with the solvent-accessible surface distance. Finally, we create a combined score that incorporates information from 3D electron microscopy maps as well as crosslinking. This achieves, on average, better results than either information type alone and demonstrates the potential of integrative modeling with XL-MS and low-resolution cryoelectron microscopy.

Quantitative imaging of chromatin decompaction in living cells

Chromatin organization is highly dynamic and regulates transcription. Upon transcriptional activation, chromatin is remodeled and referred to as “open,” but quantitative and dynamic data of this decompaction process are lacking. Here, we have developed a quantitative high resolution–microscopy assay in living yeast cells to visualize and quantify chromatin dynamics using the GAL7-10-1 locus as a model system. Upon transcriptional activation of these three clustered genes, we detect an increase of the mean distance across this locus by >100 nm. This decompaction is linked to active transcription but is not sensitive to the histone deacetylase inhibitor trichostatin A or to deletion of the histone acetyl transferase Gcn5. In contrast, the deletion of SNF2 (encoding the ATPase of the SWI/SNF chromatin remodeling complex) or the deactivation of the histone chaperone complex FACT lead to a strongly reduced decompaction without significant effects on transcriptional induction in FACT mutants. Our findings are consistent with nucleosome remodeling and eviction activities being major contributors to chromatin reorganization during transcription but also suggest that transcription can occur in the absence of detectable decompaction.

Scientific iconoclasm and active imagination: synthetic cells as techno-scientific mandalas

Metaphors allow us to come to terms with abstract and complex information, by comparing it to something which is structured, familiar and concrete. Although modern science is "iconoclastic", as Gaston Bachelard phrases it (i.e. bent on replacing living entities by symbolic data: e.g. biochemical and mathematical symbols and codes), scientists are at the same time prolific producers of metaphoric images themselves. Synthetic biology is an outstanding example of a technoscientific discourse replete with metaphors, including textual metaphors such as the "Morse code" of life, the "barcode" of life and the "book" of life. This paper focuses on a different type of metaphor, however, namely on the archetypal metaphor of the mandala as a symbol of restored unity and wholeness. Notably, mandala images emerge in textual materials (papers, posters, PowerPoints, etc.) related to one of the new "frontiers" of contemporary technoscience, namely the building of a synthetic cell: a laboratory artefact that functions like a cell and is even able to replicate itself. The mandala symbol suggests that, after living systems have been successfully reduced to the elementary building blocks and barcodes of life, the time has now come to put these fragments together again. We can only claim to understand life, synthetic cell experts argue, if we are able to technically reproduce a fully functioning cell. This holistic turn towards the cell as a meaningful whole (a total work of techno-art) also requires convergence at the "subject pole": the building of a synthetic cell as a practice of the self, representing a turn towards integration, of multiple perspectives and various forms of expertise.

A Parametric Rosetta Energy Function Analysis with LK Peptides on SAM Surfaces

Although structures have been determined for many soluble proteins and an increasing number of membrane proteins, experimental structure determination methods are limited for complexes of proteins and solid surfaces. An economical alternative or complement to experimental structure determination is molecular simulation. Rosetta is one software suite that models protein-surface interactions, but Rosetta is normally benchmarked on soluble proteins. For surface interactions, the validity of the energy function is uncertain because it is a combination of independent parameters from energy functions developed separately for solution proteins and mineral surfaces. Here, we assess the performance of the RosettaSurface algorithm and test the accuracy of its energy function by modeling the adsorption of leucine/lysine (LK)-repeat peptides on methyl- and carboxy-terminated self-assembled monolayers (SAMs). We investigated how RosettaSurface predictions for this system compare with the experimental results, which showed that on both surfaces, LK-α peptides folded into helices and LK-β peptides held extended structures. Utilizing this model system, we performed a parametric analysis of Rosetta's Talaris energy function and determined that adjusting solvation parameters offered improved predictive accuracy. Simultaneously increasing lysine carbon hydrophilicity and the hydrophobicity of the surface methyl head groups yielded computational predictions most closely matching the experimental results. De novo models still should be interpreted skeptically unless bolstered in an integrative approach with experimental data.

Computational 3D genome modeling using Chrom3D

Chrom3D is a computational platform for 3D genome modeling that simulates the spatial positioning of chromosome domains relative to each other and relative to the nuclear periphery. In Chrom3D, chromosomes are modeled as chains of contiguous beads, in which each bead represents a genomic domain. In this protocol, a bead represents a topologically associated domain (TAD) mapped from ensemble Hi-C data. Chrom3D takes as input data significant pairwise TAD-TAD interactions determined from a Hi-C contact matrix, and TAD interactions with the nuclear periphery, determined by ChIP-sequencing of nuclear lamins to define lamina-associated domains (LADs). Chrom3D is based on Monte Carlo simulations initiated from a starting random bead configuration. During the optimization process, TAD-TAD interactions constrain bead positions relative to each other, whereas LAD information constrains the corresponding bead toward the nuclear periphery. Optimization can be repeated many times to generate an ensemble of 3D genome models. Analyses of the models enable estimations of the radial positioning of genomic sites in the nucleus across cells in a population. Chrom3D provides opportunities to reveal spatial relationships between TADs and LADs. More generally, predictions from Chrom3D models can be experimentally tested in the laboratory. We describe the entire Chrom3D protocol for modeling a 3D diploid human genome, from the creation of input files to the final rendering of 3D genome structures. The procedure takes ∼18 h. Chrom3D is freely available on GitHub.

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.

Producing genome structure populations with the dynamic and automated PGS software

Chromosome conformation capture technologies such as Hi-C are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and interchromosomal interactions. We pioneered a probabilistic approach for the generation of a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome's organization in space and time. We provide a user-friendly software package, called PGS, which runs on local machines (for practice runs) and high-performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix, along with information about genome segmentation, and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command-line knowledge is sufficient for using this software. A typical running time of the pipeline is ∼3 d with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at topological-associated domain (TAD)-level resolution.

Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.

A Structural Model of the Urease Activation Complex Derived from Ion Mobility-Mass Spectrometry and Integrative Modeling

The synthesis of active Klebsiella aerogenes urease via an 18-subunit enzyme apoprotein-accessory protein pre-activation complex has been well studied biochemically, but thus far this complex has remained refractory to direct structural characterization. Using ion mobility-mass spectrometry, we characterized several protein complexes between the core urease apoprotein and its accessory proteins, including the 610-kDa (UreABC)₃(UreDFG)₃ complex. Using our recently developed computational modeling workflow, we generated ensembles of putative (UreABC)₃(UreDFG)₃ species consistent with experimental restraints and characterized the structural ambiguity present in these models. By integrating structural information from previous studies, we increased the resolution of the ion mobility-mass spectrometry-derived models substantially, and we observe a discrete population of structures consistent with all of the available data for this complex.

Integrative structure and functional anatomy of a nuclear pore complex

Despite the central role of Nuclear Pore Complexes (NPCs) as gatekeepers of RNA and protein transport between the cytoplasm and nucleoplasm, their large size and dynamic nature have impeded a full structural and functional elucidation. Here, we have determined a subnanometer precision structure for the entire 552-protein yeast NPC by satisfying diverse data including stoichiometry, a cryo-electron tomography map, and chemical cross-links. The structure reveals the NPC’s functional elements in unprecedented detail. The NPC is built of sturdy diagonal columns to which are attached connector cables, imbuing both strength and flexibility, while tying together all other elements of the NPC, including membrane-interacting regions and RNA processing platforms. Inwardly-directed anchors create a high density of transport factor-docking Phe-Gly repeats in the central channel, organized in distinct functional units. Taken together, this integrative structure allows us to rationalize the architecture, transport mechanism, and evolutionary origins of the NPC.

PDB-Dev: a Prototype System for Depositing Integrative/Hybrid Structural Models

Burley et al. (leadership of the Worldwide PDB [wwPDB] Partnership [wwpdb.org] and the wwPDB Integrative/Hybrid Methods Task Force) announce public release of a prototype system for depositing integrative/hybrid structural models, PDB-Development (PDB-Dev; https://pdb-dev.wwpdb.org).

4Cin: A computational pipeline for 3D genome modeling and virtual Hi-C analyses from 4C data

The use of 3C-based methods has revealed the importance of the 3D organization of the chromatin for key aspects of genome biology. However, the different caveats of the variants of 3C techniques have limited their scope and the range of scientific fields that could benefit from these approaches. To address these limitations, we present 4Cin, a method to generate 3D models and derive virtual Hi-C (vHi-C) heat maps of genomic loci based on 4C-seq or any kind of 4C-seq-like data, such as those derived from NG Capture-C. 3D genome organization is determined by integrative consideration of the spatial distances derived from as few as four 4C-seq experiments. The 3D models obtained from 4C-seq data, together with their associated vHi-C maps, allow the inference of all chromosomal contacts within a given genomic region, facilitating the identification of Topological Associating Domains (TAD) boundaries. Thus, 4Cin offers a much cheaper, accessible and versatile alternative to other available techniques while providing a comprehensive 3D topological profiling. By studying TAD modifications in genomic structural variants associated to disease phenotypes and performing cross-species evolutionary comparisons of 3D chromatin structures in a quantitative manner, we demonstrate the broad potential and novel range of applications of our method.

Chromatin conformation capture (3C) methods have revealed the importance of the 3D organization of the chromatin, which is key to understand many aspects of genome biology. But each of these methods have their own limitations. Here we present 4Cin, a software that generates 3D models of the chromatin from a small number of 4C-seq experiments, a 3C-based method that provides the frequency of contacts between one fragments and the genome (one vs all). These 3D models are used to infer all chromosomal contacts within a given genomic region (many vs many). The contact maps facilitate the identification of Topological Associating Domains boundaries. Our software offers a much cheaper, accessible and versatile alternative to other available techniques while providing a comprehensive 3D topological profiling. We applied our software to two different loci to study modifications in genomic structural variants associated to disease phenotypes and to compare the chromatin organization in two different species in a quantitative manner.

Regulation of Rvb1/Rvb2 by a Domain within the INO80 Chromatin Remodeling Complex Implicates the Yeast Rvbs as Protein Assembly Chaperones

The hexameric AAA+ ATPases Rvb1 and Rvb2 (Rvbs) are essential for diverse processes ranging from metabolic signaling to chromatin remodeling, but their functions are unknown. While originally thought to act as helicases, recent proposals suggest that Rvbs act as protein assembly chaperones. However, experimental evidence for chaperone-like behavior is lacking. Here, we identify a potent protein activator of the Rvbs, a domain in the Ino80 ATPase subunit of the INO80 chromatin-remodeling complex, termed Ino80INS. Ino80INS stimulates Rvbs' ATPase activity by 16-fold while concomitantly promoting their dodecamerization. Using mass spectrometry, cryo-EM, and integrative modeling, we find that Ino80INS binds asymmetrically along the dodecamerization interface, resulting in a conformationally flexible dodecamer that collapses into hexamers upon ATP addition. Our results demonstrate the chaperone-like potential of Rvb1/Rvb2 and suggest a model where binding of multiple clients such as Ino80 stimulates ATP-driven cycling between hexamers and dodecamers, providing iterative opportunities for correct subunit assembly.

Gaussian-input Gaussian mixture model for representing density maps and atomic models

A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well; however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a down-sampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm.

Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data

This protocol describes a workflow for creating structural models of proteins or protein complexes using distance restraints derived from cross-linking mass spectrometry experiments. The distance restraints are used (i) to adjust preliminary models that are calculated on the basis of a homologous template and primary sequence, and (ii) to select the model that is in best agreement with the experimental data. In the case of protein complexes, the cross-linking data are further used to dock the subunits to one another to generate models of the interacting proteins. Predicting models in such a manner has the potential to indicate multiple conformations and dynamic changes that occur in solution. This modeling protocol is compatible with many cross-linking workflows and uses open-source programs or programs that are free for academic users and do not require expertise in computational modeling. This protocol is an excellent additional application with which to use cross-linking results for building structural models of proteins. The established protocol is expected to take 6-12 d to complete, depending on the size of the proteins and the complexity of the cross-linking data.

A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data

Background

The development of chromosomal conformation capture techniques, particularly, the Hi-C technique, has made the analysis and study of the spatial conformation of a genome an important topic in bioinformatics and computational biology. Aided by high-throughput next generation sequencing techniques, the Hi-C technique can generate genome-wide, large-scale intra- and inter-chromosomal interaction data capable of describing in details the spatial interactions within a genome. These data can be used to reconstruct 3D structures of chromosomes that can be used to study DNA replication, gene regulation, genome interaction, genome folding, and genome function.

Results

Here, we introduce a maximum likelihood algorithm called 3DMax to construct the 3D structure of a chromosome from Hi-C data. 3DMax employs a maximum likelihood approach to infer the 3D structures of a chromosome, while automatically re-estimating the conversion factor (α) for converting Interaction Frequency (IF) to distance. Our results show that the models generated by 3DMax from a simulated Hi-C dataset match the true models better than most of the existing methods. 3DMax is more robust to structural variability and noise. Compared on a real Hi-C dataset, 3DMax constructs chromosomal models that fit the data better than most methods, and it is faster than all other methods. The models reconstructed by 3DMax were consistent with fluorescent in situ hybridization (FISH) experiments and existing knowledge about the organization of human chromosomes, such as chromosome compartmentalization.

Conclusions

3DMax is an effective approach to reconstructing 3D chromosomal models. The results, and the models generated for the simulated and real Hi-C datasets are available here: http://sysbio.rnet.missouri.edu/bdm_download/3DMax/. The source code is available here: https://github.com/BDM-Lab/3DMax. A short video demonstrating how to use 3DMax can be found here: https://youtu.be/ehQUFWoHwfo.

Solution structure of the cytochrome P450 reductase-cytochrome c complex determined by neutron scattering

Electron transfer in all living organisms critically relies on formation of complexes between the proteins involved. The function of these complexes requires specificity of the interaction to allow for selective electron transfer but also a fast turnover of the complex, and they are therefore often transient in nature, making them challenging to study. Here, using small-angle neutron scattering with contrast matching with deuterated protein, we report the solution structure of the electron transfer complex between cytochrome P450 reductase (CPR) and its electron transfer partner cytochrome c This is the first reported solution structure of a complex between CPR and an electron transfer partner. The structure shows that the interprotein interface includes residues from both the FMN- and FAD-binding domains of CPR. In addition, the FMN is close to the heme of cytochrome c but distant from the FAD, indicating that domain movement is required between the electron transfer steps in the catalytic cycle of CPR. In summary, our results reveal key details of the CPR catalytic mechanism, including interactions of two domains of the reductase with cytochrome c and motions of these domains relative to one another. These findings shed light on interprotein electron transfer in this system and illustrate a powerful approach for studying solution structures of protein-protein complexes.

Data-assisted protein structure modeling by global optimization in CASP12

In CASP12, 2 types of data-assisted protein structure modeling were experimented. Either SAXS experimental data or cross-linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross-linking data to drive the model structures into more native-like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue-residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross-linking contact information provides only very low-resolution distance information. For this reason, we combined the SAXS or cross-linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X-ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross-linking data into the protein structure modeling.

Prediction of enzymatic pathways by integrative pathway mapping

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology.

Archiving of Integrative Structural Models

Integrative or hybrid structural biology involves the determination of three-dimensional structures of macromolecular assemblies by combining information from a variety of experimental and computational methods. Archiving the results of integrative/hybrid modeling methods have complex requirements and existing archiving mechanisms are insufficient to handle these pre-requisites. Three concepts important for archiving integrative/hybrid models are presented in this chapter: (1) building a federated network of structural model and experimental data archives, (2) development of a common set of data standards, and (3) creation of mechanisms for interoperation and data exchange among the repositories in a federation. Methods proposed for achieving these objectives are also discussed.