ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis.

The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; PDDFFIT, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in DATMW for Bayesian consensus-based concentration-independent molecular weight estimation; DATMIF, an ab initio shape analysis method that optimizes the search model directly against the scattering data; DAMEMB, an application to set up the initial search volume for multiphase modelling of membrane proteins; ELLLIP, to perform quasi-atomistic modelling of liposomes with elliptical shapes; NMATOR, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; DAMMIX, which reconstructs the shape of an unknown intermediate in an evolving system; and LIPMIX and BILMIX, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the PRIMUS graphical interface to Qt5, updating SASpy - a PyMOL plugin to run a subset of ATSAS tools - to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future ATSAS releases. All these features are implemented in ATSAS 3.0, freely available for academic users at https://www.embl-hamburg.de/biosaxs/software.html.

Btk SH2-kinase interface is critical for allosteric kinase activation and its targeting inhibits B-cell neoplasms.

Bruton's tyrosine kinase (Btk) is critical for B-cell maturation and activation. Btk loss-of-function mutations cause human X-linked agammaglobulinemia (XLA). In contrast, Btk signaling sustains growth of several B-cell neoplasms which may be treated with tyrosine kinase inhibitors (TKIs). Here, we uncovered the structural mechanism by which certain XLA mutations in the SH2 domain strongly perturb Btk activation. Using a combination of molecular dynamics (MD) simulations and small-angle X-ray scattering (SAXS), we discovered an allosteric interface between the SH2 and kinase domain required for Btk activation and to which multiple XLA mutations map. As allosteric interactions provide unique targeting opportunities, we developed an engineered repebody protein binding to the SH2 domain and able to disrupt the SH2-kinase interaction. The repebody prevents activation of wild-type and TKI-resistant Btk, inhibiting Btk-dependent signaling and proliferation of malignant B-cells. Therefore, the SH2-kinase interface is critical for Btk activation and a targetable site for allosteric inhibition.

Shedding Light on the Interaction of Human Anti-Apoptotic Bcl-2 Protein with Ligands through Biophysical and in Silico Studies.

Bcl-2 protein is involved in cell apoptosis and is considered an interesting target for anti-cancer therapy. The present study aims to understand the stability and conformational changes of Bcl-2 upon interaction with the inhibitor venetoclax, and to explore other drug-target regions. We combined biophysical and in silico approaches to understand the mechanism of ligand binding to Bcl-2. Thermal shift assay (TSA) and urea electrophoresis showed a significant increase in protein stability upon venetoclax incubation, which is corroborated by molecular docking and molecular dynamics simulations. An 18 °C shift in Bcl-2 melting temperature was observed in the TSA, corresponding to a binding affinity multiple times higher than that of any other reported Bcl-2 inhibitor. This protein-ligand interaction does not implicate alternations in protein conformation, as suggested by SAXS. Additionally, bioinformatics approaches were used to identify deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of Bcl-2 and their impact on venetoclax binding, suggesting that venetoclax interaction is generally favored against these deleterious nsSNPs. Apart from the BH3 binding groove of Bcl-2, the flexible loop domain (FLD) also plays an important role in regulating the apoptotic process. High-throughput virtual screening (HTVS) identified 5 putative FLD inhibitors from the Zinc database, showing nanomolar affinity toward the FLD of Bcl-2.

CHROMIXS: automatic and interactive analysis of chromatography-coupled small-angle X-ray scattering data.

Summary: Size-exclusion chromatography (SEC) coupled to small-angle X-ray scattering (SAXS), also known as inline SEC-SAXS, is being increasingly used for the structural analysis of biological macromolecules, complexes and mixtures in solution. A single SEC-SAXS run generates thousands of individual SAXS profiles from the eluting solute and their analysis requires a correct identification of buffer and sample regions, a rather laborous task. We present CHROMIXS (as in CHROMatography Inline X-ray Scattering), a program for rapid reduction and analysis, both automatically and interactively, of SEC-SAXS data. Availability and implementation: CHROMIXS is freely available to academic users as part of the ATSAS software suite (www.embl-hamburg.de/biosaxs/download.html). Contact: apanjkovich@embl-hamburg.de or svergun@embl-hamburg.de.

Structural and functional dissection of the DH and PH domains of oncogenic Bcr-Abl tyrosine kinase.

The two isoforms of the Bcr-Abl tyrosine kinase, p210 and p190, are associated with different leukemias and have a dramatically different signaling network, despite similar kinase activity. To provide a molecular rationale for these observations, we study the Dbl-homology (DH) and Pleckstrin-homology (PH) domains of Bcr-Abl p210, which constitute the only structural differences to p190. Here we report high-resolution structures of the DH and PH domains and characterize conformations of the DH-PH unit in solution. Our structural and functional analyses show no evidence that the DH domain acts as a guanine nucleotide exchange factor, whereas the PH domain binds to various phosphatidylinositol-phosphates. PH-domain mutants alter subcellular localization and result in decreased interactions with p210-selective interaction partners. Hence, the PH domain, but not the DH domain, plays an important role in the formation of the differential p210 and p190 Bcr-Abl signaling networks.

Highly selective tungstate transporter protein TupA from Desulfovibrio alaskensis G20.

Molybdenum and tungsten are taken up by bacteria and archaea as their soluble oxyanions through high affinity transport systems belonging to the ATP-binding cassette (ABC) transporters. The component A (ModA/TupA) of these transporters is the first selection gate from which the cell differentiates between MoO42-, WO42- and other similar oxyanions. We report the biochemical characterization and the crystal structure of the apo-TupA from Desulfovibrio desulfuricans G20, at 1.4 Å resolution. Small Angle X-ray Scattering data suggests that the protein adopts a closed and more stable conformation upon ion binding. The role of the arginine 118 in the selectivity of the oxyanion was also investigated and three mutants were constructed: R118K, R118E and R118Q. Isothermal titration calorimetry clearly shows the relevance of this residue for metal discrimination and oxyanion binding. In this sense, the three variants lost the ability to coordinate molybdate and the R118K mutant keeps an extremely high affinity for tungstate. These results contribute to an understanding of the metal-protein interaction, making it a suitable candidate for a recognition element of a biosensor for tungsten detection.

SASpy: a PyMOL plugin for manipulation and refinement of hybrid models against small angle X-ray scattering data.

UNLABELLED: Complex formation and conformational transitions of biological macromolecules in solution can be effectively studied using the information about overall shape and size provided by small angle X-ray scattering (SAXS). Hybrid modeling is often applied to integrate high-resolution models into SAXS data analysis. To facilitate this task, we present SASpy, a PyMOL plugin that provides an easy-to-use graphical interface for SAXS-based hybrid modeling. Through a few mouse clicks in SASpy, low-resolution models can be superimposed to high-resolution structures, theoretical scattering profiles and fits can be calculated and displayed on-the-fly. Mouse-based manual rearrangements of complexes are conveniently applied to rapidly check and interactively refine tentative models. Interfaces to automated rigid-body and flexible refinement of macromolecular models against the experimental SAXS data are provided. AVAILABILITY AND IMPLEMENTATION: SASpy is available as open source at: github.com/emblsaxs/saspy/. Working installations of both PyMOL (www.pymol.org) and ATSAS (www.embl-hamburg.de/biosaxs/download.html) are required. CONTACT: apanjkovich@embl-hamburg.de or svergun@embl-hamburg.de.

DARA: a web server for rapid search of structural neighbours using solution small angle X-ray scattering data.

MOTIVATION: Small angle X-ray scattering (SAXS) is an established method for studying biological macromolecules in solution, whereby the experimental scattering patterns relate to the quaternary and tertiary structure of the macromolecule. Here we present DARA, a web-server, that queries over 150 000 scattering profiles pre-computed from the high resolution models of macromolecules and biological assemblies in the Protein Data Bank, to rapidly find nearest neighbours of a given experimental or theoretical SAXS pattern. Identification of the best scattering equivalents provides a straightforward and automated way of structural assessment of macromolecules based on a SAXS profile. DARA results are useful e.g. for fold recognition and finding of biologically active oligomers. AVAILABILITY AND IMPLEMENTATION: http://dara.embl-hamburg.de/.

Deciphering conformational transitions of proteins by small angle X-ray scattering and normal mode analysis.

Structural flexibility and conformational rearrangements are often related to important functions of biological macromolecules, but the experimental characterization of such transitions with high-resolution techniques is challenging. At a lower resolution, small angle X-ray scattering (SAXS) can be used to obtain information on biomolecular shapes and transitions in solution. Here, we present SREFLEX, a hybrid modeling approach that uses normal mode analysis (NMA) to explore the conformational space of high-resolution models and refine the structure guided by the agreement with the experimental SAXS data. The method starts from a given conformation of the protein (which does not agree with the SAXS data). The structure is partitioned into pseudo-domains either using structural classification databases or automatically from the protein dynamics as predicted by the NMA. The algorithm proceeds hierarchically employing NMA to first probe large rearrangements and progresses into smaller and more localized movements. At the large rearrangements stage the pseudo-domains stay as rigid bodies allowing one to avoid structural disruptions inherent to the earlier NMA-based algorithms. To validate the approach, we compiled a representative benchmark set of 88 conformational states known experimentally at high resolution. The performance of the algorithm is demonstrated in the simulated data on the benchmark set and also in a number of experimental examples. SREFLEX is included into the ATSAS program package freely available to the academic users, both for download and in the on-line mode.

Molecular determinants of magnesium-dependent synaptic plasticity at electrical synapses formed by connexin36.

Neuronal gap junction (GJ) channels composed of connexin36 (Cx36) play an important role in neuronal synchronization and network dynamics. Here we show that Cx36-containing electrical synapses between inhibitory neurons of the thalamic reticular nucleus are bidirectionally modulated by changes in intracellular free magnesium concentration ([Mg(2+)]i). Chimeragenesis demonstrates that the first extracellular loop of Cx36 contains a Mg(2+)-sensitive domain, and site-directed mutagenesis shows that the pore-lining residue D47 is critical in determining high Mg(2+)-sensitivity. Single-channel analysis of Mg(2+)-sensitive chimeras and mutants reveals that [Mg(2+)]i controls the strength of electrical coupling mostly via gating mechanisms. In addition, asymmetric transjunctional [Mg(2+)]i induces strong instantaneous rectification, providing a novel mechanism for electrical rectification in homotypic Cx36 GJs. We suggest that Mg(2+)-dependent synaptic plasticity of Cx36-containing electrical synapses could underlie neuronal circuit reconfiguration via changes in brain energy metabolism that affects neuronal levels of intracellular ATP and [Mg(2+)]i.

PARS: a web server for the prediction of Protein Allosteric and Regulatory Sites.

The regulation of protein activity is a key aspect of life at the molecular level. Unveiling its details is thus crucial to understanding signalling and metabolic pathways. The most common and powerful mechanism of protein-function regulation is allostery, which has been increasingly calling the attention of medicinal chemists due to its potential for the discovery of novel therapeutics. In this context, PARS is a simple and fast method that queries protein dynamics and structural conservation to identify pockets on a protein structure that may exert a regulatory effect on the binding of a small-molecule ligand.

antibacTR: dynamic antibacterial-drug-target ranking integrating comparative genomics, structural analysis and experimental annotation.

BACKGROUND: Development of novel antibacterial drugs is both an urgent healthcare necessity and a partially neglected field. The last decades have seen a substantial decrease in the discovery of novel antibiotics, which combined with the recent thrive of multi-drug-resistant pathogens have generated a scenario of general concern. The procedures involved in the discovery and development of novel antibiotics are economically challenging, time consuming and lack any warranty of success. Furthermore, the return-on-investment for an antibacterial drug is usually marginal when compared to other therapeutics, which in part explains the decrease of private investment. RESULTS: In this work we present antibacTR, a computational pipeline designed to aid researchers in the selection of potential drug targets, one of the initial steps in antibacterial-drug discovery. The approach was designed and implemented as part of two publicly funded initiatives aimed at discovering novel antibacterial targets, mechanisms and drugs for a priority list of Gram-negative pathogens: Acinetobacter baumannii, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. However, at present this list has been extended to cover a total of 74 fully sequenced Gram-negative pathogens. antibacTR is based on sequence comparisons and queries to multiple databases (e.g. gene essentiality, virulence factors) to rank proteins according to their potential as antibacterial targets. The dynamic ranking of potential drug targets can easily be executed, customized and accessed by the user through a web interface which also integrates computational analyses performed in-house and visualizable on-site. These include three-dimensional modeling of protein structures and prediction of active sites among other functionally relevant ligand-binding sites. CONCLUSIONS: Given its versatility and ease-of-use at integrating both experimental annotation and computational analyses, antibacTR may effectively assist microbiologists, medicinal-chemists and other researchers working in the field of antibacterial drug-discovery. The public web-interface for antibacTR is available at 'http://bioinf.uab.cat/antibactr'.

Exploiting protein flexibility to predict the location of allosteric sites.

BACKGROUND: Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. RESULTS: By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. CONCLUSIONS: We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.

A systematic study of the energetics involved in structural changes upon association and connectivity in protein interaction networks.

The study of protein binding mechanisms is a major topic of research in structural biology. Here, we implement a combination of metrics to systematically assess the cost of backbone conformational changes that protein domains undergo upon association. Through the analyses of 2090 unique unbound → bound transitions, from over 12,000 structures, we show that two-thirds of these proteins do not suffer significant structural changes upon binding, and could thus fit the lock-and-key model well. Among the remaining proteins, one-third explores the bound conformation in the unbound state (conformational selection model) and, while most transitions are possible from an energetic perspective, a few do require external help to break the thermodynamic barrier (induced fit model). We also analyze the relationship between conformational transitions and protein connectivity, finding that, in general, domains interacting with many partners undergo smaller changes upon association, and are less likely to freely explore larger conformational changes.

Assessing the structural conservation of protein pockets to study functional and allosteric sites: implications for drug discovery.

BACKGROUND: With the classical, active-site oriented drug-development approach reaching its limits, protein ligand-binding sites in general and allosteric sites in particular are increasingly attracting the interest of medicinal chemists in the search for new types of targets and strategies to drug development. Given that allostery represents one of the most common and powerful means to regulate protein function, the traditional drug discovery approach of targeting active sites can be extended by targeting allosteric or regulatory protein pockets that may allow the discovery of not only novel drug-like inhibitors, but activators as well. The wealth of available protein structural data can be exploited to further increase our understanding of allosterism, which in turn may have therapeutic applications. A first step in this direction is to identify and characterize putative effector sites that may be present in already available structural data. RESULTS: We performed a large-scale study of protein cavities as potential allosteric and functional sites, by integrating publicly available information on protein sequences, structures and active sites for more than a thousand protein families. By identifying common pockets across different structures of the same protein family we developed a method to measure the pocket's structural conservation. The method was first parameterized using known active sites. We characterized the predicted pockets in terms of sequence and structural conservation, backbone flexibility and electrostatic potential. Although these different measures do not tend to correlate, their combination is useful in selecting functional and regulatory sites, as a detailed analysis of a handful of protein families shows. We finally estimated the numbers of potential allosteric or regulatory pockets that may be present in the data set, finding that pockets with putative functional and effector characteristics are widespread across protein families. CONCLUSIONS: Our results show that structurally conserved pockets are a common feature of protein families. The structural conservation of protein pockets, combined with other characteristics, can be exploited in drug discovery procedures, in particular for the selection of the most appropriate target protein and pocket for the design of drugs against entire protein families or subfamilies (e.g. for the development of broad-spectrum antimicrobials) or against a specific protein (e.g. in attempting to reduce side effects).

Predicting protein-protein interaction specificity through the integration of three-dimensional structural information and the evolutionary record of protein domains.

Protein-protein interactions are central to most biological processes. Although much recent effort has been put into experimental and computational methods to identify and model interacting partners, there has been a limited focus on how these interactions achieve the high degree of specificity observed. Accordingly, we describe a computational strategy that scores protein-protein interactions by means of a statistical potential combined with homology modelling of the putative complexes. The novelty of the method lies in the fact that it considers the evolutionary conservation of the residue contacts participating in the binding interfaces, following the hypothesis that those contacts that are conserved across a large fraction of interologues (i.e. homologous interacting protein pairs) might be responsible for the binding, while those that are specific for each interaction pair would determine the specificity. We evaluate the method on a non-redundant set of all interacting protein families of known three-dimensional structure and on specific cases where interaction specificities have been experimentally characterised, such as the Skp1, Ras and fibroblast growth factor families. Our results show that it is indeed possible to increase the accuracy of in silico prediction of protein-protein interactions by considering the relationship between interaction specificity and the degree of contact conservation in the binding interfaces.

3did Update: domain-domain and peptide-mediated interactions of known 3D structure.

The database of 3D interacting domains (3did) is a collection of protein interactions for which high-resolution 3D structures are known. 3did exploits structural information to provide the crucial molecular details necessary for understanding how protein interactions occur. Besides interactions between globular domains, the new release of 3did also contains a hand-curated set of transient peptide-mediated interactions. The interactions are grouped in Interaction Types, based on the mode of binding, and the different binding interfaces used in each type are also identified and catalogued. A web-based tool to query 3did is available at http://3did.irbbarcelona.org.

Evolutionary potentials: structure specific knowledge-based potentials exploiting the evolutionary record of sequence homologs.

We introduce a new type of knowledge-based potentials for protein structure prediction, called 'evolutionary potentials', which are derived using a single experimental protein structure and all three-dimensional models of its homologous sequences. The new potentials have been benchmarked against other knowledge-based potentials, resulting in a significant increase in accuracy for model assessment. In contrast to standard knowledge-based potentials, we propose that evolutionary potentials capture key determinants of thermodynamic stability and specific sequence constraints required for fast folding.

dnaMATE: a consensus melting temperature prediction server for short DNA sequences.

An accurate and robust large-scale melting temperature prediction server for short DNA sequences is dispatched. The server calculates a consensus melting temperature value using the nearest-neighbor model based on three independent thermodynamic data tables. The consensus method gives an accurate prediction of melting temperature, as it has been recently demonstrated in a benchmark performed using all available experimental data for DNA sequences within the length range of 16-30 nt. This constitutes the first web server that has been implemented to perform a large-scale calculation of melting temperatures in real time (up to 5000 DNA sequences can be submitted in a single run). The expected accuracy of calculations carried out by this server in the range of 50-600 mM monovalent salt concentration is that 89% of the melting temperature predictions will have an error or deviation of <5 degrees C from experimental data. The server can be freely accessed at http://dna.bio.puc.cl/tm.html. The standalone executable versions of this software for LINUX, Macintosh and Windows platforms are also freely available at the same web site. Detailed further information supporting this server is available at the same web site referenced above.

Comparison of different melting temperature calculation methods for short DNA sequences.

MOTIVATION: The overall performance of several molecular biology techniques involving DNA/DNA hybridization depends on the accurate prediction of the experimental value of a critical parameter: the melting temperature Tm. Till date, many computer software programs based on different methods and/or parameterizations are available for the theoretical estimation of the experimental Tm value of any given short oligonucleotide sequence. However, in most cases, large and significant differences in the estimations of Tm were obtained while using different methods. Thus, it is difficult to decide which Tm value is the accurate one. In addition, it seems that most people who use these methods are unaware about the limitations, which are well described in the literature but not stated properly or restricted the inputs of most of the web servers and standalone software programs that implement them. RESULTS: A quantitative comparison on the similarities and differences among some of the published DNA/DNA Tm calculation methods is reported. The comparison was carried out for a large set of short oligonucleotide sequences ranging from 16 to 30 nt long, which span the whole range of CG-content. The results showed that significant differences were observed in all the methods, which in some cases depend on the oligonucleotide length and CG-content in a non-trivial manner. Based on these results, the regions of consensus and disagreement for the methods in the oligonucleotide feature space were reported. Owing to the lack of sufficient experimental data, a fair and complete assessment of accuracy for the different methods is not yet possible. Inspite of this limitation, a consensus Tm with minimal error probability was calculated by averaging the values obtained from two or more methods that exhibit similar behavior to each particular combination of oligonucleotide length and CG-content class. Using a total of 348 DNA sequences in the size range between 16mer and 30mer, for which the experimental Tm data are available, we demonstrated that the consensus Tm is a robust and accurate measure. It is expected that the results of this work would be constituted as a useful set of guidelines to be followed for the successful experimental implementation of various molecular biology techniques, such as quantitative PCR, multiplex PCR and the design of optimal DNA microarrays.