The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling

The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling

Desmosomes mediate cell-cell adhesion and are prevalent in tissues under mechanical stress. However, their detailed structural characterization is not available. Here, we characterized the molecular architecture of the desmosomal outer dense plaque (ODP) using Bayesian integrative structural modeling via the Integrative Modeling Platform. Starting principally from the structural interpretation of an electron cryo-tomogram, we integrated information from X-ray crystallography, an immuno-electron microscopy study, biochemical assays, in-silico predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The integrative structure was validated by information from imaging, tomography, and biochemical studies that were not used in modeling. The ODP resembles a densely packed cylinder with a PKP layer and a PG layer; the desmosomal cadherins and PKP span these two layers. Our integrative approach allowed us to localize disordered regions, such as N-PKP and PG-C. We refined previous protein-protein interactions between desmosomal proteins and provided possible structural hypotheses for defective cell-cell adhesion in several diseases by mapping disease-related mutations on the structure. Finally, we point to features of the structure that could confer resilience to mechanical stress. Our model provides a basis for generating experimentally verifiable hypotheses on the structure and function of desmosomal proteins in normal and disease states.

The Use of Yeast in Biosensing

Yeast has been used as a model for several diseases as it is the simplest unicellular eukaryote, safe and easy to culture and harbors most of the fundamental processes that are present in almost all higher eukaryotes, including humans. From understanding the pathogenesis of disease to drug discovery studies, yeast has served as an important biosensor. It is not only due to the conservation of genetics, amenable modification of its genome and easily accessible analytical methods, but also some characteristic features such as its ability to survive with defective mitochondria, making it a highly flexible microbe for designing whole-cell biosensing systems. The aim of this review is to report on how yeasts have been utilized as biosensors, reporting on responses to various stimuli.

Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination

The nucleosome remodeling and deacetylase (NuRD) complex is a chromatin-modifying assembly that regulates gene expression and DNA damage repair. Despite its importance, limited structural information describing the complete NuRD complex is available and a detailed understanding of its mechanism is therefore lacking. Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4, MTA1N -HDAC1-MBD3GATAD2CC , and MTA1-HDAC1-RBBP4-MBD3-GATAD2A [nucleosome deacetylase (NuDe)]. The integrative structures were corroborated by examining independent crosslinks, cryo-EM maps, biochemical assays, known cancer-associated mutations, and structure predictions from AlphaFold. The robustness of the models was assessed by jack-knifing. Localization of the full-length MBD3, which connects the deacetylase and chromatin remodeling modules in NuRD, has not previously been possible; our models indicate two different locations for MBD3, suggesting a mechanism by which MBD3 in the presence of GATAD2A asymmetrically bridges the two modules in NuRD. Further, our models uncovered three previously unrecognized subunit interfaces in NuDe: HDAC1C -MTA1BAH , MTA1BAH -MBD3MBD , and HDAC160-100 -MBD3MBD . Our approach also allowed us to localize regions of unknown structure, such as HDAC1C and MBD3IDR , thereby resulting in the most complete and robustly cross-validated structural characterization of these NuRD sub-complexes so far.

Moonlighting at the Poles: Non-Canonical Functions of Centrosomes

Centrosomes are best known as the microtubule organizing centers (MTOCs) of eukaryotic cells. In addition to their classic role in chromosome segregation, centrosomes play diverse roles unrelated to their MTOC activity during cell proliferation and quiescence. Metazoan centrosomes and their functional doppelgängers from lower eukaryotes, the spindle pole bodies (SPBs), act as important structural platforms that orchestrate signaling events essential for cell cycle progression, cellular responses to DNA damage, sensory reception and cell homeostasis. Here, we provide a critical overview of the unconventional and often overlooked roles of centrosomes/SPBs in the life cycle of eukaryotic cells.

PrISM: Precision for Integrative Structural Models

MOTIVATION

A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information.

RESULTS

We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models.

AVAILABILITY

PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Leveraging crosslinking mass spectrometry in structural and cell biology

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

A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies

Integrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid bodies, for sampling to be efficient and accurate. Currently, these parameters are tuned manually. To automate this process, we developed a general heuristic for derivative-free, global, stochastic, parallel, multiobjective optimization, termed StOP (Stochastic Optimization of Parameters) and applied it to optimize sampling-related parameters for the Integrative Modeling Platform (IMP). Given an integrative modeling setup, list of parameters to optimize, their domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision.

Identification and characterization of metamorphic proteins: Current and future perspectives

Proteins that can reversibly alternate between distinctly different folds under native conditions are described as being metamorphic. The "metamorphome" is the collection of all metamorphic proteins in the proteome, but it remains unknown the extent to which the proteome is populated by this class of proteins. We propose that uncovering the metamorphome will require a synergy of computational screening of protein sequences to identify potential metamorphic behavior and validation through experimental techniques. This perspective discusses computational and experimental approaches that are currently used to predict and characterize metamorphic proteins as well as the need for developing improved methodologies. Since metamorphic proteins act as molecular switches, understanding their properties and behavior could lead to novel applications of these proteins as sensors in biological or environmental contexts.

Integration of Mass Spectrometry Data for Structural Biology

Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.

Microtubule pivoting enables mitotic spindle assembly in S. cerevisiae

To assemble a bipolar spindle, microtubules emanating from two poles must bundle into an antiparallel midzone, where plus end–directed motors generate outward pushing forces to drive pole separation. Midzone cross-linkers and motors display only modest preferences for antiparallel filaments, and duplicated poles are initially tethered together, an arrangement that instead favors parallel interactions. Pivoting of microtubules around spindle poles might help overcome this geometric bias, but the intrinsic pivoting flexibility of the microtubule–pole interface has not been directly measured, nor has its importance during early spindle assembly been tested. By measuring the pivoting of microtubules around isolated yeast spindle poles, we show that pivoting flexibility can be modified by mutating a microtubule-anchoring pole component, Spc110. By engineering mutants with different flexibilities, we establish the importance of pivoting in vivo for timely pole separation. Our results suggest that passive thermal pivoting can bring microtubules from side-by-side poles into initial contact, but active minus end–directed force generation will be needed to achieve antiparallel alignment.

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.

Information-driven modeling of biomolecular complexes

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

CM1-driven assembly and activation of yeast γ-tubulin small complex underlies microtubule nucleation

Microtubule (MT) nucleation is regulated by the γ-tubulin ring complex (γTuRC), conserved from yeast to humans. In Saccharomyces cerevisiae, γTuRC is composed of seven identical γ-tubulin small complex (γTuSC) sub-assemblies, which associate helically to template MT growth. γTuRC assembly provides a key point of regulation for the MT cytoskeleton. Here, we combine crosslinking mass spectrometry, X-ray crystallography, and cryo-EM structures of both monomeric and dimeric γTuSCs, and open and closed helical γTuRC assemblies in complex with Spc110p to elucidate the mechanisms of γTuRC assembly. γTuRC assembly is substantially aided by the evolutionarily conserved CM1 motif in Spc110p spanning a pair of adjacent γTuSCs. By providing the highest resolution and most complete views of any γTuSC assembly, our structures allow phosphorylation sites to be mapped, surprisingly suggesting that they are mostly inhibitory. A comparison of our structures with the CM1 binding site in the human γTuRC structure at the interface between GCP2 and GCP6 allows for the interpretation of significant structural changes arising from CM1 helix binding to metazoan γTuRC.

From integrative structural biology to cell biology

Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.

Using Integrative Modeling Platform to compute, validate, and archive a model of a protein complex structure

Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org).

Anatomy of the fungal microtubule organizing center, the spindle pole body

The fungal kingdom is large and diverse, representing extremes of ecology, life cycles and morphology. At a cellular level, the diversity among fungi is particularly apparent at the spindle pole body (SPB). This nuclear envelope embedded structure, which is essential for microtubule nucleation, shows dramatically different morphologies between different fungi. However, despite phenotypic diversity, many SPB components are conserved, suggesting commonalities in structure, function and duplication. Here, I review the organization of the most well-studied SPBs and describe how advances in genomics, genetics and cell biology have accelerated knowledge of SPB architecture in other fungi, providing insights into microtubule nucleation and other processes conserved across eukaryotes.

Yeast pericentrin/Spc110 contains multiple domains required for tethering the γ-tubulin complex to the centrosome

The Saccharomyces cerevisiae spindle pole body (SPB) serves as the sole microtubule-organizing center of the cell, nucleating both cytoplasmic and nuclear microtubules. Yeast pericentrin, Spc110, binds to and activates the γ-tubulin complex via its N terminus, allowing nuclear microtubule polymerization to occur. The Spc110 C terminus links the γ-tubulin complex to the central plaque of the SPB by binding to Spc42, Spc29, and calmodulin (Cmd1). Here, we show that overexpression of the C terminus of Spc110 is toxic to cells and correlates with its localization to the SPB. Spc110 domains that are required for SPB localization and toxicity include its Spc42-, Spc29-, and Cmd1-binding sites. Overexpression of the Spc110 C terminus induces SPB defects and disrupts microtubule organization in both cycling and G2/M arrested cells. Notably, the two mitotic SPBs are affected in an asymmetric manner such that one SPB appears to be pulled away from the nucleus toward the cortex but remains attached via a thread of nuclear envelope. This SPB also contains relatively fewer microtubules and less endogenous Spc110. Our data suggest that overexpression of the Spc110 C terminus acts as a dominant-negative mutant that titrates endogenous Spc110 from the SPB causing spindle defects.

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.

A Practical Guide to the Simultaneous Determination of Protein Structure and Dynamics Using Metainference

Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented using the metadynamics approach, which enables the computational study of complex biological systems requiring extensive conformational sampling. In this chapter, we provide a step-by-step guide to perform and analyse metadynamic metainference simulations using the ISDB module of the open-source PLUMED library, as well as a series of practical tips to avoid common mistakes. Specifically, we will guide the reader in the process of learning how to model the structural ensemble of a small disordered peptide by combining state-of-the-art molecular mechanics force fields with nuclear magnetic resonance data, including chemical shifts, scalar couplings and residual dipolar couplings.

The spindle pole body of Aspergillus nidulans is asymmetrical and contains changing numbers of γ-tubulin complexes

Centrosomes are important microtubule-organizing centers (MTOCs) in animal cells. In addition, non-centrosomal MTOCs (ncMTOCs) are found in many cell types. Their composition and structure are only poorly understood. Here, we analyzed nuclear MTOCs (spindle-pole bodies, SPBs) and septal MTOCs in Aspergillus nidulans They both contain γ-tubulin along with members of the family of γ-tubulin complex proteins (GCPs). Our data suggest that SPBs consist of γ-tubulin small complexes (γ-TuSCs) at the outer plaque, and larger γ-tubulin ring complexes (γ-TuRC) at the inner plaque. We show that the MztA protein, an ortholog of the human MOZART protein (also known as MZT1), interacted with the inner plaque receptor PcpA (the homolog of fission yeast Pcp1) at SPBs, while no interaction nor colocalization was detected between MztA and the outer plaque receptor ApsB (fission yeast Mto1). Septal MTOCs consist of γ-TuRCs including MztA but are anchored through AspB and Spa18 (fission yeast Mto2). MztA is not essential for viability, although abnormal spindles were observed frequently in cells lacking MztA. Quantitative PALM imaging revealed unexpected dynamics of the protein composition of SPBs, with changing numbers of γ-tubulin complexes over time during interphase and constant numbers during mitosis.This article has an associated First Person interview with the first author of the paper.

AI-Assisted Forward Modeling of Biological Structures

The rise of machine learning and deep learning technologies have allowed researchers to automate image classification. We describe a method that incorporates automated image classification and principal component analysis to evaluate computational models of biological structures. We use a computational model of the kinetochore to demonstrate our artificial-intelligence (AI)-assisted modeling method. The kinetochore is a large protein complex that connects chromosomes to the mitotic spindle to facilitate proper cell division. The kinetochore can be divided into two regions: the inner kinetochore, including proteins that interact with DNA; and the outer kinetochore, comprised of microtubule-binding proteins. These two kinetochore regions have been shown to have different distributions during metaphase in live budding yeast and therefore act as a test case for our forward modeling technique. We find that a simple convolutional neural net (CNN) can correctly classify fluorescent images of inner and outer kinetochore proteins and show a CNN trained on simulated, fluorescent images can detect difference in experimental images. A polymer model of the ribosomal DNA locus serves as a second test for the method. The nucleolus surrounds the ribosomal DNA locus and appears amorphous in live-cell, fluorescent microscopy experiments in budding yeast, making detection of morphological changes challenging. We show a simple CNN can detect subtle differences in simulated images of the ribosomal DNA locus, demonstrating our CNN-based classification technique can be used on a variety of biological structures.

FRET Microscopy in Yeast

Förster resonance energy transfer (FRET) microscopy is a powerful fluorescence microscopy method to study the nanoscale organization of multiprotein assemblies in vivo. Moreover, many biochemical and biophysical processes can be followed by employing sophisticated FRET biosensors directly in living cells. Here, we summarize existing FRET experiments and biosensors applied in yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe, two important models of fundamental biomedical research and efficient platforms for analyses of bioactive molecules. We aim to provide a practical guide on suitable FRET techniques, fluorescent proteins, and experimental setups available for successful FRET experiments in yeasts.

A microtubule crosslinking protocol for integrative structural modeling activities

Microtubules (MTs) are key components in the cytoskeleton of the eukaryotic cell, and play roles in processes such as intracellular transport and cell division. An improved understanding MT regulation requires structural analysis of the extensive interactions between the MT lattice and its regulatory proteins, but MT interactions are challenging for even the most advanced structural methods to characterize. Integrative methods involving crosslinking mass spectrometry (XL-MS) can extend structural analysis to many interaction classes, but the representation of MTs in crosslinking data-sets has been surprisingly low. Here, we explore the basis for the underrepresentation of the MT lattice and present an enhanced method for mapping MT structural features using an optimized set of reagents, together with fluorescence detection to ensure MT structural integrity. Through the application of stringent identification criteria, 91 unique crosslinks were identified, 78 of which were uniquely matched to 7 distinct structural features of the MT lattice. Of note, 4 crosslinks were detected for the lattice-A protofilament organization. The lattice-A structure defines a "seam" or discontinuity in MTs and is an emerging site of interest for MT regulation. Our methodology should be broadly applicable to integrative structural studies involving any MT-protein interaction.

Archiving and disseminating integrative structure models

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

Structure and function of Spc42 coiled-coils in yeast centrosome assembly and duplication

Centrosomes and spindle pole bodies (SPBs) are membraneless organelles whose duplication and assembly is necessary for bipolar mitotic spindle formation. The structural organization and functional roles of major proteins in these organelles can provide critical insights into cell division control. Spc42, a phosphoregulated protein with an N-terminal dimeric coiled-coil (DCC), assembles into a hexameric array at the budding yeast SPB core, where it functions as a scaffold for SPB assembly. Here, we present in vitro and in vivo data to elucidate the structural arrangement and biological roles of Spc42 elements. Crystal structures reveal details of two additional coiled-coils in Spc42: a central trimeric coiled-coil and a C-terminal antiparallel DCC. Contributions of the three Spc42 coiled-coils and adjacent undetermined regions to the formation of an ∼145 Å hexameric lattice in an in vitro lipid monolayer assay and to SPB duplication and assembly in vivo reveal structural and functional redundancy in Spc42 assembly. We propose an updated model that incorporates the inherent symmetry of these Spc42 elements into a lattice, and thereby establishes the observed sixfold symmetry. The implications of this model for the organization of the central SPB core layer are discussed.

Modeling Biological Complexes Using Integrative Modeling Platform

Integrative structure modeling provides 3D models of macromolecular systems that are based on information from multiple types of experiments, physical principles, statistical inferences, and prior structural models. Here, we provide a hands-on realistic example of integrative structure modeling of the quaternary structure of the actin, tropomyosin, and gelsolin protein assembly based on electron microscopy, solution X-ray scattering, and chemical crosslinking data for the complex as well as excluded volume, sequence connectivity, and rigid atomic X-ray structures of the individual subunits. We follow the general four-stage process for integrative modeling, including gathering the input information, converting the input information into a representation of the system and a scoring function, sampling alternative model configurations guided by the scoring function, and analyzing the results. The computational aspects of this approach are implemented in our open-source Integrative Modeling Platform (IMP), a comprehensive and extensible software package for integrative modeling ( https://integrativemodeling.org ). In particular, we rely on the Python Modeling Interface (PMI) module of IMP that provides facile mixing and matching of macromolecular representations, restraints based on different types of information, sampling algorithms, and analysis including validations of the input data and output models. Finally, we also outline how to deposit an integrative structure and corresponding experimental data into PDB-Dev, the nascent worldwide Protein Data Bank (wwPDB) resource for archiving and disseminating integrative structures ( https://pdb-dev.wwpdb.org ). The example application provides a starting point for a user interested in using IMP for integrative modeling of other biomolecular systems.

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.

From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives

Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.

Novel phosphorylation states of the yeast spindle pole body

Phosphorylation regulates yeast spindle pole body (SPB) duplication and separation and likely regulates microtubule nucleation. We report a phosphoproteomic analysis using tandem mass spectrometry of enriched Saccharomyces cerevisiae SPBs for two cell cycle arrests, G1/S and the mitotic checkpoint, expanding on previously reported phosphoproteomic data sets. We present a novel phosphoproteomic state of SPBs arrested in G1/S by a cdc4-1 temperature-sensitive mutation, with particular focus on phosphorylation events on the γ-tubulin small complex (γ-TuSC). The cdc4-1 arrest is the earliest arrest at which microtubule nucleation has occurred at the newly duplicated SPB. Several novel phosphorylation sites were identified in G1/S and during mitosis on the microtubule nucleating γ-TuSC. These sites were analyzed in vivo by fluorescence microscopy and were shown to be required for proper regulation of spindle length. Additionally, in vivo analysis of two mitotic sites in Spc97 found that phosphorylation of at least one of these sites is required for progression through the cell cycle. This phosphoproteomic data set not only broadens the scope of the phosphoproteome of SPBs, it also identifies several γ-TuSC phosphorylation sites that influence microtubule formation.

Summary: A phosphoproteome of yeast spindle pole bodies in G1/S or M phase identifies phosphorylation sites involved in spindle length control and provides direction for future phosphorylation analyses of spindle pole components.

Key phosphorylation events in Spc29 and Spc42 guide multiple steps of yeast centrosome duplication

Phosphorylation modulates many cellular processes during cell cycle progression. The yeast centrosome (called the spindle pole body, SPB) is regulated by the protein kinases Mps1 and Cdc28/Cdk1 as it nucleates microtubules to separate chromosomes during mitosis. Previously we completed an SPB phosphoproteome, identifying 297 sites on 17 of the 18 SPB components. Here we describe mutagenic analysis of phosphorylation events on Spc29 and Spc42, two SPB core components that were shown in the phosphoproteome to be heavily phosphorylated. Mutagenesis at multiple sites in Spc29 and Spc42 suggests that much of the phosphorylation on these two proteins is not essential but enhances several steps of mitosis. Of the 65 sites examined on both proteins, phosphorylation of the Mps1 sites Spc29-T18 and Spc29-T240 was shown to be critical for function. Interestingly, these two sites primarily influence distinct successive steps; Spc29-T240 is important for the interaction of Spc29 with Spc42, likely during satellite formation, and Spc29-T18 facilitates insertion of the new SPB into the nuclear envelope and promotes anaphase spindle elongation. Phosphorylation sites within Cdk1 motifs affect function to varying degrees, but mutations only have significant effects in the presence of an MPS1 mutation, supporting a theme of coregulation by these two kinases.

Fluorescence techniques in developmental biology

Advanced fluorescence techniques, commonly known as the F-techniques, measure the kinetics and the interactions of biomolecules with high sensitivity and spatiotemporal resolution. Applications of the F-techniques, which were initially limited to cells, were further extended to study in vivo protein organization and dynamics in whole organisms. The integration of F-techniques with multi-photon microscopy and light-sheet microscopy widened their applications in the field of developmental biology. It became possible to penetrate the thick tissues of living organisms and obtain good signal-to-noise ratio with reduced photo-induced toxicity. In this review, we discuss the principle and the applications of the three most commonly used F-techniques in developmental biology: Fluorescence Recovery After Photo-bleaching (FRAP), Fo¨ rster Resonance Energy Transfer (FRET), and Fluorescence Correlation and Cross-Correlation Spectroscopy (FCS and FCCS).

Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures

Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive.

Integrative structure modeling with the Integrative Modeling Platform

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.