Modeling of proteins and their assemblies with the Integrative Modeling Platform

Solution X-ray scattering highlights discrepancies in Plasmodium multi-aminoacyl-tRNA synthetase complexes

tRip is a tRNA import protein specific to Plasmodium, the causative agent of malaria. In addition to its membrane localization and tRNA trafficking properties, tRip has the capacity to associate with three aminoacyl-tRNA synthetases (aaRS), the glutamyl- (ERS), glutaminyl- (QRS), and methionyl- (MRS) tRNA synthetases. In eukaryotes, such multi-aaRSs complexes (MSC) regulate the moonlighting activities of aaRSs. In Plasmodium, tRip and the three aaRSs all contain an N-terminal GST-like domain involved in the assembly of two independent complexes: the Q-complex (tRip:ERS:QRS) and the M-complex (tRip:ERS:MRS) with a 2:2:2 stoichiometry and in which the association of the GST-like domains of tRip and ERS (tRip-N:ERS-N) is central. In this study, the crystal structure of the N-terminal GST-like domain of ERS was solved and made possible further investigation of the solution architecture of the Q- and M-complexes by small-angle x-ray scattering (SAXS). This strategy relied on the engineering of a tRip-N-ERS-N chimeric protein to study the structural scaffold of both Plasmodium MSCs and confirm the unique homodimerization pattern of tRip in solution. The biological impact of these structural arrangements is discussed.

In silico ADMET, molecular docking and molecular simulation-based study of glabridin’s natural and semisynthetic derivatives as potential tyrosinase inhibitors

Hyper-pigmentation conditions may develop due to erroneous melanogenesis cascade which leads to excess melanin production. Recently, inhibition of tyrosinase is the main focus of investigation as it majorly contributes to melanin production. This inhibition property can be exploited in medicine, agriculture, and in cosmetics. Present study aims to find a natural and safe alternative molecule as tyrosinase inhibitor. In this study, human tyrosinase enzyme was modelled due to unavailability of its crystal structure to look into the degree of efficacy of glabridin and its 15 derivatives as tyrosinase inhibitor. Docking was performed by Autodock Vina at the catalytic core enzyme. Glabridin effects on melanoma cell lines was also elucidated by analysing cytotoxicity and effect on melanin production. Computational ADME analysis was done by SwissADME. Molecular dynamic simulation was also performed to further evaluate the interaction profile of these molecules and kojic acid (positive inhibitor) with respect to apo protein. Notably, four derivatives 5′-formylglabridin, glabridin dimer, 5′-prenyl glabridin and R-glabridin exhibited better binding affinity than glabridin. Glabridin effectively inhibited melanin production in a dose dependent manner. Among these, 5′-formylglabridin displayed highest binding affinity with docking score − 9.2 kcal/mol. Molecular properties and bioactivity analysis by Molinspiration web server and by SwissADME also presented these molecules as potential drug candidates. The study explores the understanding for the development of suitable tyrosinase inhibitor/s for the prevention of hyperpigmentation. However, a detailed in vivo study is required for glabridin derivatives to suggest these molecules as anti-melanogenic compound.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Structural Analysis of the Regulatory GAF Domains of cGMP Phosphodiesterase Elucidates the Allosteric Communication Pathway

Regulation of photoreceptor phosphodiesterase (PDE6) activity is responsible for the speed, sensitivity, and recovery of the photoresponse during visual signaling in vertebrate photoreceptor cells. It is hypothesized that physiological differences in the light responsiveness of rods and cones may result in part from differences in the structure and regulation of the distinct isoforms of rod and cone PDE6. Although rod and cone PDE6 catalytic subunits share a similar domain organization consisting of tandem GAF domains (GAFa and GAFb) and a catalytic domain, cone PDE6 is a homodimer whereas rod PDE6 consists of two homologous catalytic subunits. Here we provide the x-ray crystal structure of cone GAFab regulatory domain solved at 3.3 Å resolution, in conjunction with chemical cross-linking and mass spectrometric analysis of conformational changes to GAFab induced upon binding of cGMP and the PDE6 inhibitory γ-subunit (Pγ). Ligand-induced changes in cross-linked residues implicate multiple conformational changes in the GAFa and GAFb domains in forming an allosteric communication network. Molecular dynamics simulations of cone GAFab revealed differences in conformational dynamics of the two subunits forming the homodimer and allosteric perturbations on cGMP binding. Cross-linking of Pγ to GAFab in conjunction with solution NMR spectroscopy of isotopically labeled Pγ identified the central polycationic region of Pγ interacting with the GAFb domain. These results provide a mechanistic basis for developing allosteric activators of PDE6 with therapeutic implications for halting the progression of several retinal degenerative diseases.

Probing Protein Interaction Networks by Combining MS-Based Proteomics and Structural Data Integration

Protein-protein interactions play a major role in the molecular machinery of life, and various techniques such as AP-MS are dedicated to their identification. However, those techniques return lists of proteins devoid of organizational structure, not detailing which proteins interact with which others. Proposing a hierarchical view of the interactions between the members of the flat list becomes highly tedious for large data sets when done by hand. To help hierarchize this data, we introduce a new bioinformatics protocol that integrates information of the multimeric protein 3D structures available in the Protein Data Bank using remote homology detection, as well as information related to Short Linear Motifs and interaction data from the BioGRID. We illustrate on two unrelated use-cases of different complexity how our approach can be useful to decipher the network of interactions hidden in the list of input proteins, and how it provides added value compared to state-of-the-art resources such as Interactome3D or STRING. Particularly, we show the added value of using homology detection to distinguish between orthologs and paralogs, and to distinguish between core obligate and more facultative interactions. We also demonstrate the potential of considering interactions occurring through Short Linear Motifs.

The molecular architecture of photoreceptor phosphodiesterase 6 (PDE6) with activated G protein elucidates the mechanism of visual excitation

Photoreceptor phosphodiesterase 6 (PDE6) is the central effector of the visual excitation pathway in both rod and cone photoreceptors, and PDE6 mutations that alter PDE6 structure or regulation can result in several human retinal diseases. The rod PDE6 holoenzyme consists of two catalytic subunits (Pαβ) whose activity is suppressed in the dark by binding of two inhibitory γ-subunits (Pγ). Upon photoactivation of rhodopsin, the heterotrimeric G protein (transducin) is activated, resulting in binding of the activated transducin α-subunit (Gt_α) to PDE6, displacement of Pγ from the PDE6 active site, and enzyme activation. Although the biochemistry of this pathway is understood, a lack of detailed structural information about the PDE6 activation mechanism hampers efforts to develop therapeutic interventions for managing PDE6-associated retinal diseases. To address this gap, here we used a cross-linking MS-based approach to create a model of the entire interaction surface of Pγ with the regulatory and catalytic domains of Pαβ in its nonactivated state. Following reconstitution of PDE6 and activated Gt_α with liposomes and identification of cross-links between Gt_α and PDE6 subunits, we determined that the PDE6-Gt_α protein complex consists of two Gt_α-binding sites per holoenzyme. Each Gt_α interacts with the catalytic domains of both catalytic subunits and induces major changes in the interaction sites of the Pγ subunit with the catalytic subunits. These results provide the first structural model for the activated state of the transducin-PDE6 complex during visual excitation, enhancing our understanding of the molecular etiology of inherited retinal diseases.

Evolving SAXS versatility: solution X-ray scattering for macromolecular architecture, functional landscapes, and integrative structural biology

Small-angle X-ray scattering (SAXS) has emerged as an enabling integrative technique for comprehensive analyses of macromolecular structures and interactions in solution. Over the past two decades, SAXS has become a mainstay of the structural biologist's toolbox, supplying multiplexed measurements of molecular shape and dynamics that unveil biological function. Here, we discuss evolving SAXS theory, methods, and applications that extend the field of small-angle scattering beyond simple shape characterization. SAXS, coupled with size-exclusion chromatography (SEC-SAXS) and time-resolved (TR-SAXS) methods, is now providing high-resolution insight into macromolecular flexibility and ensembles, delineating biophysical landscapes, and facilitating high-throughput library screening to assess macromolecular properties and to create opportunities for drug discovery. Looking forward, we consider SAXS in the integrative era of hybrid structural biology methods, its potential for illuminating cellular supramolecular and mesoscale structures, and its capacity to complement high-throughput bioinformatics sequencing data. As advances in the field continue, we look forward to proliferating uses of SAXS based upon its abilities to robustly produce mechanistic insights for biology and medicine.

Molecular architecture of the Bardet-Biedl syndrome protein 2-7-9 subcomplex

Bardet-Biedl syndrome (BBS) is a genetic disorder characterized by malfunctions in primary cilia resulting from mutations that disrupt the function of the BBSome, an 8-subunit complex that plays an important role in protein transport in primary cilia. To better understand the molecular basis of BBS, here we used an integrative structural modeling approach consisting of EM and chemical cross-linking coupled with MS analyses, to analyze the structure of a BBSome 2-7-9 subcomplex consisting of three homologous BBS proteins, BBS2, BBS7, and BBS9. The resulting molecular model revealed an overall structure that resembles a flattened triangle. We found that within this structure, BBS2 and BBS7 form a tight dimer through a coiled-coil interaction and that BBS9 associates with the dimer via an interaction with the α-helical domain of BBS2. Interestingly, a BBS-associated mutation of BBS2 (R632P) is located in its α-helical domain at the interface between BBS2 and BBS9, and binding experiments indicated that this mutation disrupts the BBS2-BBS9 interaction. This finding suggests that BBSome assembly is disrupted by the R632P substitution, providing molecular insights that may explain the etiology of BBS in individuals harboring this mutation.

A hybrid, bottom-up, structurally accurate, Go¯-like coarse-grained protein model

Coarse-grained (CG) protein models in the structural biology literature have improved over the years from being simple tools to understand general folding and aggregation driving forces to capturing detailed structures achieved by actual folding sequences. Here, we ask whether such models can be developed systematically from recent advances in bottom-up coarse-graining methods without relying on bioinformatic data (e.g., protein data bank statistics). We use relative entropy coarse-graining to develop a hybrid CG but Go¯-like CG peptide model, hypothesizing that the landscape of proteinlike folds is encoded by the backbone interactions, while the sidechain interactions define which of these structures globally minimizes the free energy in a unique native fold. To construct a model capable of capturing varied secondary structures, we use a new extended ensemble relative entropy method to coarse-grain based on multiple reference atomistic simulations of short polypeptides with varied α and β character. Subsequently, we assess the CG model as a putative protein backbone forcefield by combining it with sidechain interactions based on native contacts but not incorporating native distances explicitly, unlike standard Go¯ models. We test the model's ability to fold a range of proteins and find that it achieves high accuracy (∼2 Å root mean square deviation resolution for both short sequences and large globular proteins), suggesting the strong role that backbone conformational preferences play in defining the fold landscape. This model can be systematically extended to non-natural amino acids and nonprotein polymers and sets the stage for extensions to non-Go¯ models with sequence-specific sidechain interactions.

Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data

In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.

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.

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.

The NOS-like protein from the microalgae Ostreococcus tauri is a genuine and ultrafast NO-producing enzyme

The exponential increase of genomes' sequencing has revealed the presence of NO-Synthases (NOS) throughout the tree of life, uncovering an extraordinary diversity of genetic structure and biological functions. Although NO has been shown to be a crucial mediator in plant physiology, NOS sequences seem present solely in green algae genomes, with a first identification in the picoplankton species Ostreococcus tauri. There is no rationale so far to account for the presence of NOS in this early-diverging branch of the green lineage and its absence in land plants. To address the biological function of algae NOS, we cloned, expressed and characterized the NOS oxygenase domain from Ostreococcus tauri (OtNOSoxy). We launched a phylogenetic and structural analysis of algae NOS, and achieved a 3D model of OtNOSoxy by homology modeling. We used a combination of various spectroscopies to characterize the structural and electronic fingerprints of some OtNOSoxy reaction intermediates. The analysis of OtNOSoxy catalytic activity and kinetic efficiency was achieved by stoichiometric stopped-flow. Our results highlight the conserved and particular features of OtNOSoxy structure that might explain its ultrafast NO-producing capacity. This integrative Structure-Catalysis-Function approach could be extended to the whole NOS superfamily and used for predicting potential biological activity for any new NOS.

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.

Proteins physically associate into complexes in order to carry out the essential functions of life. Knowing how proteins are physically arranged three dimensionally in these complexes provides clues towards how they work. In principle, the associations between proteins in large-scale proteomics datasets should often reflect direct physical contacts between proteins in each complex. Here, we describe a statistical method to discover which subunits within complexes directly contact each other based on their co-purification behavior in published co-fractionation mass spectrometry datasets. Within our predictions, we recover many known protein-protein contacts, serving to validate our method, as well as unknown contacts that can inform future studies of these complexes. Specifically, we observe confident contacts between subunits within the exocyst and tRNA multi-synthetase complexes, two complexes that have incomplete structural information. Using our method, we further provide structural information for a previously missing subunit of the EKC/KEOPS complex. We anticipate that this method and the associated predictions will help to better inform our understanding of the functions and structures of diverse protein complexes.

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.

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

Rosetta Broker for membrane protein structure prediction: concentrative nucleoside transporter 3 and corticotropin-releasing factor receptor 1 test cases

Background

Membrane proteins are difficult targets for structure prediction due to the limited structural data deposited in Protein Data Bank. Most computational methods for membrane protein structure prediction are based on the comparative modeling. There are only few de novo methods targeting that distinct protein family. In this work an example of such de novo method was used to structurally and functionally characterize two representatives of distinct membrane proteins families of solute carrier transporters and G protein-coupled receptors. The well-known Rosetta program and one of its protocols named Broker was used in two test cases. The first case was de novo structure prediction of three N-terminal transmembrane helices of the human concentrative nucleoside transporter 3 (hCNT3) homotrimer belonging to the solute carrier 28 family of transporters (SLC28). The second case concerned the large scale refinement of transmembrane helices of a homology model of the corticotropin-releasing factor receptor 1 (CRFR1) belonging to the G protein-coupled receptors family.

Results

The inward-facing model of the hCNT3 homotrimer was used to propose the functional impact of its single nucleotide polymorphisms. Additionally, the 100 ns molecular dynamics simulation of the unliganded hCNT3 model confirmed its validity and revealed mobility of the selected binding site and homotrimer interface residues. The large scale refinement of transmembrane helices of the CRFR1 homology model resulted in the significant improvement of its accuracy with respect to the crystal structure of CRFR1, especially in the binding site area. Consequently, the antagonist CP-376395 could be docked with Autodock VINA to the CRFR1 model without any steric clashes.

Conclusions

The presented work demonstrated that Rosetta Broker can be a versatile tool for solving various issues referring to protein biology. Two distinct examples of de novo membrane protein structure prediction presented here provided important insights into three major areas of protein biology. Namely, the dynamics of the inward-facing hCNT3 homotrimer system, the structural changes of the CRFR1 receptor upon the antagonist binding and finally, the role of single nucleotide polymorphisms in both, hCNT3 and CRFR1 proteins, were investigated.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-017-0078-8) contains supplementary material, which is available to authorized users.

Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics

Accurate and precise structural ensembles of proteins and macromolecular complexes can be obtained with metainference, a recently proposed Bayesian inference method that integrates experimental information with prior knowledge and deals with all sources of errors in the data as well as with sample heterogeneity. The study of complex macromolecular systems, however, requires an extensive conformational sampling, which represents a separate challenge. To address such challenge and to exhaustively and efficiently generate structural ensembles we combine metainference with metadynamics and illustrate its application to the calculation of the free energy landscape of the alanine dipeptide.

High Sensitivity Crosslink Detection Coupled With Integrative Structure Modeling in the Mass Spec Studio

The Mass Spec Studio package was designed to support the extraction of hydrogen-deuterium exchange and covalent labeling data for a range of mass spectrometry (MS)-based workflows, to integrate with restraint-driven protein modeling activities. In this report, we present an extension of the underlying Studio framework and provide a plug-in for crosslink (XL) detection. To accommodate flexibility in XL methods and applications, while maintaining efficient data processing, the plug-in employs a peptide library reduction strategy via a presearch of the tandem-MS data. We demonstrate that prescoring linear unmodified peptide tags using a probabilistic approach substantially reduces search space by requiring both crosslinked peptides to generate sparse data attributable to their linear forms. The method demonstrates highly sensitive crosslink peptide identification with a low false positive rate. Integration with a Haddock plug-in provides a resource that can combine multiple sources of data for protein modeling activities. We generated a structural model of porcine transferrin bound to TbpB, a membrane-bound receptor essential for iron acquisition in Actinobacillus pleuropneumoniae Using mutational data and crosslinking restraints, we confirm the mechanism by which TbpB recognizes the iron-loaded form of transferrin, and note the requirement for disparate sources of restraint data for accurate model construction. The software plugin is freely available at www.msstudio.ca.

ProXL (Protein Cross-Linking Database): A Platform for Analysis, Visualization, and Sharing of Protein Cross-Linking Mass Spectrometry Data

ProXL is a Web application and accompanying database designed for sharing, visualizing, and analyzing bottom-up protein cross-linking mass spectrometry data with an emphasis on structural analysis and quality control. ProXL is designed to be independent of any particular software pipeline. The import process is simplified by the use of the ProXL XML data format, which shields developers of data importers from the relative complexity of the relational database schema. The database and Web interfaces function equally well for any software pipeline and allow data from disparate pipelines to be merged and contrasted. ProXL includes robust public and private data sharing capabilities, including a project-based interface designed to ensure security and facilitate collaboration among multiple researchers. ProXL provides multiple interactive and highly dynamic data visualizations that facilitate structural-based analysis of the observed cross-links as well as quality control. ProXL is open-source, well-documented, and freely available at https://github.com/yeastrc/proxl-web-app.

A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors

The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT1B and 5-HT2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT1B and 5-HT2B demonstrated that both the receptor structure and the ligand binding mode can be predicted with the atomic-detail accuracy, as long as the target-template sequence similarity is relatively high. On the other hand, the observation of a low target-template sequence similarity, e.g., between SMO from the frizzled GPCR family and members of the rhodopsin family, hampers the GPCR structure prediction and ligand docking. Indeed, in GPCR Dock 2013, accurate prediction of the SMO target was still beyond the capabilities of most research groups. Another bottleneck in the current GPCR research, as demonstrated by the 5-HT2B target, is the reliable prediction of global conformational changes induced by activation of GPCRs. In this work, we report details of our protocol used during GPCR Dock 2013. Our structure prediction and ligand docking protocol was especially successful in the case of 5-HT1B and 5-HT2B-ergotamine complexes for which we provide one of the most accurate predictions. In addition to a description of the GPCR Dock 2013 results, we propose a novel hybrid computational methodology to improve GPCR structure and function prediction. This computational methodology employs two separate rankings for filtering GPCR models. The first ranking is ligand-based while the second is based on the scoring scheme of the recently published BCL method. In this work, we prove that the use of knowledge-based potentials implemented in BCL is an efficient way to cope with major bottlenecks in the GPCR structure prediction. Thereby, we also demonstrate that the knowledge-based potentials for membrane proteins were significantly improved, because of the recent surge in available experimental structures.

The molecular architecture of the Dam1 kinetochore complex is defined by cross-linking based structural modelling

Accurate segregation of chromosomes during cell division is essential. The Dam1 complex binds kinetochores to microtubules and its oligomerization is required to form strong attachments. It is a key target of Aurora B kinase, which destabilizes erroneous attachments allowing subsequent correction. Understanding the roles and regulation of the Dam1 complex requires structural information. Here we apply cross-linking/mass spectrometry and structural modelling to determine the molecular architecture of the Dam1 complex. We find microtubule attachment is accompanied by substantial conformational changes, with direct binding mediated by the carboxy termini of Dam1p and Duo1p. Aurora B phosphorylation of Dam1p C terminus weakens direct interaction with the microtubule. Furthermore, the Dam1p amino terminus forms an interaction interface between Dam1 complexes, which is also disrupted by phosphorylation. Our results demonstrate that Aurora B inhibits both direct interaction with the microtubule and oligomerization of the Dam1 complex to drive error correction during mitosis.

High-throughput studies of protein shapes and interactions by synchrotron small-angle X-ray scattering

Solution-based small angle X-ray scattering (SAXS) affords the opportunity to extract accurate structural parameters and global shape information from diverse biological macromolecular systems. SAXS is an ideal complementary technique to other structural and biophysical methods but it can also be applied alone to access structural information that is otherwise unobtainable using high-resolution methods. Macromolecular structures ranging from kilodaltons to gigadaltons can be analyzed, which encompasses the size of most proteins and functional cellular complexes. The SAXS analysis is performed using only a few microliters of solution containing microgram quantities of purified material in sample environments that can be tailored to mimic physiological conditions or altered to suit a particular question. High-brilliance synchrotron X-ray sources and parallel advances in hardware and computing have reduced data acquisition times to the millisecond range and the application of automated methods have allowed data processing and low resolution shape modelling to be completed within minutes. These developments have paved the way for high-throughput studies that generate significant quantities of structural information over a short period of time. Here, we briefly consider the basics of SAXS and describe major methods and protocols employed in high-throughput SAXS studies.

Sequence co-evolution gives 3D contacts and structures of protein complexes

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.