NHLBI-AbDesigner: an online tool for design of peptide-directed antibodies

Investigation of physiological mechanisms at a cellular level often requires production of high-quality antibodies, frequently using synthetic peptides as immunogens. Here we describe a new, web-based software tool called NHLBI-AbDesigner that allows the user to visualize the information needed to choose optimal peptide sequences for peptide-directed antibody production (http://helixweb.nih.gov/AbDesigner/). The choice of an immunizing peptide is generally based on a need to optimize immunogenicity, antibody specificity, multispecies conservation, and robustness in the face of posttranslational modifications (PTMs). AbDesigner displays information relevant to these criteria as follows: 1) "Immunogenicity Score," based on hydropathy and secondary structure prediction; 2) "Uniqueness Score," a predictor of specificity of an antibody against all proteins expressed in the same species; 3) "Conservation Score," a predictor of ability of the antibody to recognize orthologs in other animal species; and 4) "Protein Features" that show structural domains, variable regions, and annotated PTMs that may affect antibody performance. AbDesigner displays the information online in an interactive graphical user interface, which allows the user to recognize the trade-offs that exist for alternative synthetic peptide choices and to choose the one that is best for a proposed application. Several examples of the use of AbDesigner for the display of such trade-offs are presented, including production of a new antibody to Slc9a3. We also used the program in large-scale mode to create a database listing the 15-amino acid peptides with the highest Immunogenicity Scores for all known proteins in five animal species, one plant species (Arabidopsis thaliana), and Saccharomyces cerevisiae.     

siDRM: an effective and generally applicable online siRNA design tool

Small interfering RNAs (siRNAs) have become an indispensable tool for the investigation of gene functions. Most existing siRNA design tools were trained on datasets assembled from confined origins, incompatible with the diverse siRNA laboratory practice to which these tools will ultimately be applied. We have performed an updated analysis using the disjunctive rule merging (DRM) approach on a large and diverse dataset compiled from siRecords, and implemented the resulting rule sets in siDRM, a new online siRNA design tool. siDRM also implements a few high-sensitivity rule sets and fast rule sets, links to siRecords, and uses several filters to check unwanted detrimental effects, including innate immune responses, cell toxic effects and off-target activities in selecting siRNAs. A performance comparison using an independent dataset indicated that siDRM outperforms 19 existing siRNA design tools in identifying effective siRNAs.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

PIQET: the design and development of an online ‘streamlined’ LCA tool for sustainable packaging design decision support

Background, aim and scope  

‘Streamlined’ life cycle assessment (LCA) tools hold out the possibility of providing LCA information quickly and easily in order to support a variety of decision-making environments and situations. The utility of such tools is closely related to the accuracy needs and possibilities, and the particular decisions to be supported. In order to facilitate the provision and application of LCA information in decision making during packaging design, development and utilisation, there is a prima facia case for a ‘streamlined’ LCA tool, provided it meets a set of requirements, including functionality, accuracy, validity, reliability and usability.     

Optimizing metabolic pathways by screening for feasible synthetic reactions

Background

Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze.

Results

Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis.

Conclusions

While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.     

PathwayBooster: a tool to support the curation of metabolic pathways

Background

Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task.

Results

We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism’s metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms.

Conclusions

By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0447-2) contains supplementary material, which is available to authorized users.     

FOCAL: an experimental design tool for systematizing metabolic discoveries and model development

Current computational tools can generate and improve genome-scale models based on existing data; however, for many organisms, the data needed to test and refine such models are not available. To facilitate model development, we created the forced coupling algorithm, FOCAL, to identify genetic and environmental conditions such that a reaction becomes essential for an experimentally measurable phenotype. This reaction''s conditional essentiality can then be tested experimentally to evaluate whether network connections occur or to create strains with desirable phenotypes. FOCAL allows network connections to be queried, which improves our understanding of metabolism and accuracy of developed models.     

Quantitative analysis of glycine related metabolic pathways for one-carbon synthetic biology

1. Download : Download high-res image (107KB)
2. Download : Download full-size image

     

MMG: a probabilistic tool to identify submodules of metabolic pathways

Motivation: A fundamental task in systems biology is the identificationof groups of genes that are involved in the cellular responseto particular signals. At its simplest level, this often reducesto identifying biological quantities (mRNA abundance, enzymeconcentrations, etc.) which are differentially expressed intwo different conditions. Popular approaches involve using t-teststatistics, based on modelling the data as arising from a mixturedistribution. A common assumption of these approaches is thatthe data are independent and identically distributed; however,biological quantities are usually related through a complex(weighted) network of interactions, and often the more pertinentquestion is which subnetworks are differentially expressed,rather than which genes. Furthermore, in many interesting cases(such as high-throughput proteomics and metabolomics), onlyvery partial observations are available, resulting in the needfor efficient imputation techniques. Results: We introduce Mixture Model on Graphs (MMG), a novelprobabilistic model to identify differentially expressed submodulesof biological networks and pathways. The method can easily incorporateinformation about weights in the network, is robust againstmissing data and can be easily generalized to directed networks.We propose an efficient sampling strategy to infer posteriorprobabilities of differential expression, as well as posteriorprobabilities over the model parameters. We assess our methodon artificial data demonstrating significant improvements overstandard mixture model clustering. Analysis of our model resultson quantitative high-throughput proteomic data leads to theidentification of biologically significant subnetworks, as wellas the prediction of the expression level of a number of enzymes,some of which are then verified experimentally. Availability: MATLAB code is available from http://www.dcs.shef.ac.uk/~guido/software.html Contact: guido{at}dcs.shef.ac.uk Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren     

CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design

Background

The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates.Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes.

Results

After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids.

Conclusions

The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.

     

SCEPTRANS: an online tool for analyzing periodic transcription in yeast

SUMMARY: SCEPTRANS is designed for analysis of microarray timecourse data related to periodic phenomena in the budding yeast. The server allows for easy viewing of temporal profiles of multiple genes in a number of datasets. Additional functionality includes searching for coexpressed genes, periodicity and correlation analysis, integrating functional annotation and localization data as well as advanced operations on sets of genes. AVAILABILITY: Available online at http://sceptrans.org/     

GarlicESTdb: an online database and mining tool for garlic EST sequences

Background  

Allium sativum., commonly known as garlic, is a species in the onion genus (Allium), which is a large and diverse one containing over 1,250 species. Its close relatives include chives, onion, leek and shallot. Garlic has been used throughout recorded history for culinary, medicinal use and health benefits. Currently, the interest in garlic is highly increasing due to nutritional and pharmaceutical value including high blood pressure and cholesterol, atherosclerosis and cancer. For all that, there are no comprehensive databases available for Expressed Sequence Tags(EST) of garlic for gene discovery and future efforts of genome annotation. That is why we developed a new garlic database and applications to enable comprehensive analysis of garlic gene expression.     

VisANT: an online visualization and analysis tool for biological interaction data

Background  

New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems.