PONDEROSA-C/S: client–server based software package for automated protein 3D structure determination

Peak-picking Of Noe Data Enabled by Restriction Of Shift Assignments-Client Server (PONDEROSA-C/S) builds on the original PONDEROSA software (Lee et al. in Bioinformatics 27:1727–1728. doi:10.1093/bioinformatics/btr200, 2011) and includes improved features for structure calculation and refinement. PONDEROSA-C/S consists of three programs: Ponderosa Server, Ponderosa Client, and Ponderosa Analyzer. PONDEROSA-C/S takes as input the protein sequence, a list of assigned chemical shifts, and nuclear Overhauser data sets (¹³C- and/or ¹⁵N-NOESY). The output is a set of assigned NOEs and 3D structural models for the protein. Ponderosa Analyzer supports the visualization, validation, and refinement of the results from Ponderosa Server. These tools enable semi-automated NMR-based structure determination of proteins in a rapid and robust fashion. We present examples showing the use of PONDEROSA-C/S in solving structures of four proteins: two that enable comparison with the original PONDEROSA package, and two from the Critical Assessment of automated Structure Determination by NMR (Rosato et al. in Nat Methods 6:625–626. doi:10.1038/nmeth0909-625, 2009) competition. The software package can be downloaded freely in binary format from http://pine.nmrfam.wisc.edu/download_packages.html. Registered users of the National Magnetic Resonance Facility at Madison can submit jobs to the PONDEROSA-C/S server at http://ponderosa.nmrfam.wisc.edu, where instructions, tutorials, and instructions can be found. Structures are normally returned within 1–2 days.     

PACSY, a relational database management system for protein structure and chemical shift analysis

PACSY (Protein structure And Chemical Shift NMR spectroscopY) is a relational database management system that integrates information from the Protein Data Bank, the Biological Magnetic Resonance Data Bank, and the Structural Classification of Proteins database. PACSY provides three-dimensional coordinates and chemical shifts of atoms along with derived information such as torsion angles, solvent accessible surface areas, and hydrophobicity scales. PACSY consists of six relational table types linked to one another for coherence by key identification numbers. Database queries are enabled by advanced search functions supported by an RDBMS server such as MySQL or PostgreSQL. PACSY enables users to search for combinations of information from different database sources in support of their research. Two software packages, PACSY Maker for database creation and PACSY Analyzer for database analysis, are available from http://pacsy.nmrfam.wisc.edu.     

EZ-ASSIGN, a program for exhaustive NMR chemical shift assignments of large proteins from complete or incomplete triple-resonance data

For several of the proteins in the BioMagResBank larger than 200 residues, 60 % or fewer of the backbone resonances were assigned. But how reliable are those assignments? In contrast to complete assignments, where it is possible to check whether every triple-resonance Generalized Spin System (GSS) is assigned once and only once, with incomplete data one should compare all possible assignments and pick the best one. But that is not feasible: For example, for 200 residues and an incomplete set of 100 GSS, there are 1.6 × 10²⁶⁰ possible assignments. In “EZ-ASSIGN”, the protein sequence is divided in smaller unique fragments. Combined with intelligent search approaches, an exhaustive comparison of all possible assignments is now feasible using a laptop computer. The program was tested with experimental data of a 388-residue domain of the Hsp70 chaperone protein DnaK and for a 351-residue domain of a type III secretion ATPase. EZ-ASSIGN reproduced the hand assignments. It did slightly better than the computer program PINE (Bahrami et al. in PLoS Comput Biol 5(3):e1000307, 2009) and significantly outperformed SAGA (Crippen et al. in J Biomol NMR 46:281–298, 2010), AUTOASSIGN (Zimmerman et al. in J Mol Biol 269:592–610, 1997), and IBIS (Hyberts and Wagner in J Biomol NMR 26:335–344, 2003). Next, EZ-ASSIGN was used to investigate how well NMR data of decreasing completeness can be assigned. We found that the program could confidently assign fragments in very incomplete data. Here, EZ-ASSIGN dramatically outperformed all the other assignment programs tested.     

progressiveMauve: Multiple Genome Alignment with Gene Gain,Loss and Rearrangement

Background

Multiple genome alignment remains a challenging problem. Effects of recombination including rearrangement, segmental duplication, gain, and loss can create a mosaic pattern of homology even among closely related organisms.

Methodology/Principal Findings

We describe a new method to align two or more genomes that have undergone rearrangements due to recombination and substantial amounts of segmental gain and loss (flux). We demonstrate that the new method can accurately align regions conserved in some, but not all, of the genomes, an important case not handled by our previous work. The method uses a novel alignment objective score called a sum-of-pairs breakpoint score, which facilitates accurate detection of rearrangement breakpoints when genomes have unequal gene content. We also apply a probabilistic alignment filtering method to remove erroneous alignments of unrelated sequences, which are commonly observed in other genome alignment methods. We describe new metrics for quantifying genome alignment accuracy which measure the quality of rearrangement breakpoint predictions and indel predictions. The new genome alignment algorithm demonstrates high accuracy in situations where genomes have undergone biologically feasible amounts of genome rearrangement, segmental gain and loss. We apply the new algorithm to a set of 23 genomes from the genera Escherichia, Shigella, and Salmonella. Analysis of whole-genome multiple alignments allows us to extend the previously defined concepts of core- and pan-genomes to include not only annotated genes, but also non-coding regions with potential regulatory roles. The 23 enterobacteria have an estimated core-genome of 2.46Mbp conserved among all taxa and a pan-genome of 15.2Mbp. We document substantial population-level variability among these organisms driven by segmental gain and loss. Interestingly, much variability lies in intergenic regions, suggesting that the Enterobacteriacae may exhibit regulatory divergence.

Conclusions

The multiple genome alignments generated by our software provide a platform for comparative genomic and population genomic studies. Free, open-source software implementing the described genome alignment approach is available from http://gel.ahabs.wisc.edu/mauve.     

Inferring Host Gene Subnetworks Involved in Viral Replication

Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at http://www.biostat.wisc.edu/~craven/chasman_host_virus/.     

Protein side-chain resonance assignment and NOE assignment using RDC-defined backbones without TOCSY data

One bottleneck in NMR structure determination lies in the laborious and time-consuming process of side-chain resonance and NOE assignments. Compared to the well-studied backbone resonance assignment problem, automated side-chain resonance and NOE assignments are relatively less explored. Most NOE assignment algorithms require nearly complete side-chain resonance assignments from a series of through-bond experiments such as HCCH-TOCSY or HCCCONH. Unfortunately, these TOCSY experiments perform poorly on large proteins. To overcome this deficiency, we present a novel algorithm, called Nasca (NOE Assignment and Side-Chain Assignment), to automate both side-chain resonance and NOE assignments and to perform high-resolution protein structure determination in the absence of any explicit through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY. After casting the assignment problem into a Markov Random Field (MRF), Nasca extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The MRF captures the contact map information of the protein derived from NOESY spectra, exploits the backbone structural information determined by RDCs, and considers all possible side-chain rotamers. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity and compute high-resolution protein structures. Tests on five proteins show that Nasca assigns resonances for more than 90% of side-chain protons, and achieves about 80% correct assignments. The final structures computed using the NOE distance restraints assigned by Nasca have backbone RMSD 0.8–1.5 Å from the reference structures determined by traditional NMR approaches.     

CRF-based models of protein surfaces improve protein-protein interaction site predictions

Background

The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved.

Results

We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers.

Conclusions

Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.     

Fast and Accurate Resonance Assignment of Small-to-Large Proteins by Combining Automated and Manual Approaches

The process of resonance assignment is fundamental to most NMR studies of protein structure and dynamics. Unfortunately, the manual assignment of residues is tedious and time-consuming, and can represent a significant bottleneck for further characterization. Furthermore, while automated approaches have been developed, they are often limited in their accuracy, particularly for larger proteins. Here, we address this by introducing the software COMPASS, which, by combining automated resonance assignment with manual intervention, is able to achieve accuracy approaching that from manual assignments at greatly accelerated speeds. Moreover, by including the option to compensate for isotope shift effects in deuterated proteins, COMPASS is far more accurate for larger proteins than existing automated methods. COMPASS is an open-source project licensed under GNU General Public License and is available for download from http://www.liu.se/forskning/foass/tidigare-foass/patrik-lundstrom/software?l=en. Source code and binaries for Linux, Mac OS X and Microsoft Windows are available.

This is a PLOS Computational Biology Software article.

     

Automated Design of Probes for rRNA-Targeted Fluorescence In Situ Hybridization Reveals the Advantages of Using Dual Probes for Accurate Identification

Fluorescence in situ hybridization (FISH) is a common technique for identifying cells in their natural environment and is often used to complement next-generation sequencing approaches as an integral part of the full-cycle rRNA approach. A major challenge in FISH is the design of oligonucleotide probes with high sensitivity and specificity to their target group. The rapidly expanding number of rRNA sequences has increased awareness of the number of potential nontargets for every FISH probe, making the design of new FISH probes challenging using traditional methods. In this study, we conducted a systematic analysis of published probes that revealed that many have insufficient coverage or specificity for their intended target group. Therefore, we developed an improved thermodynamic model of FISH that can be applied at any taxonomic level, used the model to systematically design probes for all recognized genera of bacteria and archaea, and identified potential cross-hybridizations for the selected probes. This analysis resulted in high-specificity probes for 35.6% of the genera when a single probe was used in the absence of competitor probes and for 60.9% when up to two competitor probes were used. Requiring the hybridization of two independent probes for positive identification further increased specificity. In this case, we could design highly specific probe sets for up to 68.5% of the genera without the use of competitor probes and 87.7% when up to two competitor probes were used. The probes designed in this study, as well as tools for designing new probes, are available online (http://DECIPHER.cee.wisc.edu).     

Directional Trans-Synaptic Labeling of Specific Neuronal Connections in Live Animals

Understanding animal behavior and development requires visualization and analysis of their synaptic connectivity, but existing methods are laborious or may not depend on trans-synaptic interactions. Here we describe a transgenic approach for in vivo labeling of specific connections in Caenorhabditis elegans, which we term iBLINC. The method is based on BLINC (Biotin Labeling of INtercellular Contacts) and involves trans-synaptic enzymatic transfer of biotin by the Escherichia coli biotin ligase BirA onto an acceptor peptide. A BirA fusion with the presynaptic cell adhesion molecule NRX-1/neurexin is expressed presynaptically, whereas a fusion between the acceptor peptide and the postsynaptic protein NLG-1/neuroligin is expressed postsynaptically. The biotinylated acceptor peptide::NLG-1/neuroligin fusion is detected by a monomeric streptavidin::fluorescent protein fusion transgenically secreted into the extracellular space. Physical contact between neurons is insufficient to create a fluorescent signal, suggesting that synapse formation is required. The labeling approach appears to capture the directionality of synaptic connections, and quantitative analyses of synapse patterns display excellent concordance with electron micrograph reconstructions. Experiments using photoconvertible fluorescent proteins suggest that the method can be utilized for studies of protein dynamics at the synapse. Applying this technique, we find connectivity patterns of defined connections to vary across a population of wild-type animals. In aging animals, specific segments of synaptic connections are more susceptible to decline than others, consistent with dedicated mechanisms of synaptic maintenance. Collectively, we have developed an enzyme-based, trans-synaptic labeling method that allows high-resolution analyses of synaptic connectivity as well as protein dynamics at specific synapses of live animals.     

Presaging Critical Residues in Protein interfaces-Web Server (PCRPi-W): A Web Server to Chart Hot Spots in Protein Interfaces

Background

It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called hot spot, contributes the most to the total binding energy, and thus its identification is an important and relevant question that has clear applications in drug discovery and protein design. The experimental identification of hot spots is however a lengthy and costly process, and thus there is an interest in computational tools that can complement and guide experimental efforts.

Principal Findings

Here, we present Presaging Critical Residues in Protein interfaces-Web server (http://www.bioinsilico.org/PCRPi), a web server that implements a recently described and highly accurate computational tool designed to predict critical residues in protein interfaces: PCRPi. PRCPi depends on the integration of structural, energetic, and evolutionary-based measures by using Bayesian Networks (BNs).

Conclusions

PCRPi-W has been designed to provide an easy and convenient access to the broad scientific community. Predictions are readily available for download or presented in a web page that includes among other information links to relevant files, sequence information, and a Jmol applet to visualize and analyze the predictions in the context of the protein structure.     

NASCENT: An automatic protein interaction network generation tool for non-model organisms

Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software.

Availability

http://nascent.pitgroup.org.     

Automated Protein Turnover Calculations from 15N Partial Metabolic Labeling LC/MS Shotgun Proteomics Data

Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS ¹⁵N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (¹⁵N) to the intensity sum of all light (¹⁴N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.     

PONDEROSA, an automated 3D-NOESY peak picking program, enables automated protein structure determination

SUMMARY: PONDEROSA (Peak-picking Of Noe Data Enabled by Restriction of Shift Assignments) accepts input information consisting of a protein sequence, backbone and sidechain NMR resonance assignments, and 3D-NOESY ((13)C-edited and/or (15)N-edited) spectra, and returns assignments of NOESY crosspeaks, distance and angle constraints, and a reliable NMR structure represented by a family of conformers. PONDEROSA incorporates and integrates external software packages (TALOS+, STRIDE and CYANA) to carry out different steps in the structure determination. PONDEROSA implements internal functions that identify and validate NOESY peak assignments and assess the quality of the calculated three-dimensional structure of the protein. The robustness of the analysis results from PONDEROSA's hierarchical processing steps that involve iterative interaction among the internal and external modules. PONDEROSA supports a variety of input formats: SPARKY assignment table (.shifts) and spectrum file formats (.ucsf), XEASY proton file format (.prot), and NMR-STAR format (.star). To demonstrate the utility of PONDEROSA, we used the package to determine 3D structures of two proteins: human ubiquitin and Escherichia coli iron-sulfur scaffold protein variant IscU(D39A). The automatically generated structural constraints and ensembles of conformers were as good as or better than those determined previously by much less automated means. AVAILABILITY: The program, in the form of binary code along with tutorials and reference manuals, is available at http://ponderosa.nmrfam.wisc.edu/.     

Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.     

Timber! Felling the loblolly pine genome

Conventional short read sequences derived from haploid DNA were extended into long super-reads enabling assembly of the massive 22 Gbp loblolly pine, Pinus taeda, genome.See related research http://genomebiology.com/2014/15/3/R59