De novo assembly and validation of planaria transcriptome by massive parallel sequencing and shotgun proteomics.

Freshwater planaria are a very attractive model system for stem cell biology, tissue homeostasis, and regeneration. The genome of the planarian Schmidtea mediterranea has recently been sequenced and is estimated to contain >20,000 protein-encoding genes. However, the characterization of its transcriptome is far from complete. Furthermore, not a single proteome of the entire phylum has been assayed on a genome-wide level. We devised an efficient sequencing strategy that allowed us to de novo assemble a major fraction of the S. mediterranea transcriptome. We then used independent assays and massive shotgun proteomics to validate the authenticity of transcripts. In total, our de novo assembly yielded 18,619 candidate transcripts with a mean length of 1118 nt after filtering. A total of 17,564 candidate transcripts could be mapped to 15,284 distinct loci on the current genome reference sequence. RACE confirmed complete or almost complete 5' and 3' ends for 22/24 transcripts. The frequencies of frame shifts, fusion, and fission events in the assembled transcripts were computationally estimated to be 4.2%-13%, 0%-3.7%, and 2.6%, respectively. Our shotgun proteomics produced 16,135 distinct peptides that validated 4200 transcripts (FDR ≤1%). The catalog of transcripts assembled in this study, together with the identified peptides, dramatically expands and refines planarian gene annotation, demonstrated by validation of several previously unknown transcripts with stem cell-dependent expression patterns. In addition, our robust transcriptome characterization pipeline could be applied to other organisms without genome assembly. All of our data, including homology annotation, are freely available at SmedGD, the S. mediterranea genome database.

Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes.

BACKGROUND: Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes. RESULTS: Meredys allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time. CONCLUSIONS: Meredys provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. Meredys is a Free Software and the source code is available at http://meredys.sourceforge.net/.

Brownian diffusion of AMPA receptors is sufficient to explain fast onset of LTP.

BACKGROUND: Long-Term Potentiation (LTP) of synapses is thought to be due in part to a change in AMPA Receptor trafficking leading to an increase in the number of AMPA Receptors at the synapse. LTP onset occurs within seconds after the induction signal. A particle-based stochastic simulation software is used to investigate the effect of Brownian diffusion of glutamate receptors on receptor incorporation into the synaptic specialisation and the time-course of LTP expression. The model of the dendritic spine includes receptors diffusing within the membrane, scaffold molecules within the synaptic specialisation capable of binding receptors and a molecular picket-fence surrounding the synaptic membrane area, all features found within the biological system. RESULTS: During simulations, receptors accumulate rapidly at the post-synaptic density (PSD) from the extra-synaptic membrane under a number of biologically observed conditions. The time of half-saturation, t1/2, defined as the time-point at which half the available scaffold proteins are occupied with receptors, is found to be 710 ms. Different scaffold distributions are shown to have little effect on this time-course. Decreasing the probability of escape of receptors from the PSD domain, thus localising receptors closer to the scaffold proteins, substantially decreases t1/2. A decrease of escape probability from 1 to 0 brings about a non-linear decrease in t1/2 from 710 ms to 390 ms. Release-location of receptors within the spine is found to affect the initial rate of receptor incorporation. We simulate three possible sources of receptors: (i) receptors distributed within the spine extra-synaptic membrane; (ii) receptors from exocytotic vesicles released to the synaptic spine; and (iii) receptors entering the spine from the dendritic shaft through the spine neck. Receptors released from exocytotic vesicles initially accumulate faster than receptors released from the other two sources. A model of glutamate release and glutamate-receptor interaction shows that newly inserted receptors make a substantial contribution to a glutamate evoked response within the observed time-frame. CONCLUSIONS: Fast accumulation of AMPA Receptors is consistent with experimentally observed fast onset of LTP expression.

Evolutionary families of peptidase inhibitors.

The proteins that inhibit peptidases are of great importance in medicine and biotechnology, but there has never been a comprehensive system of classification for them. Some of the terminology currently in use is potentially confusing. In the hope of facilitating the exchange, storage and retrieval of information about this important group of proteins, we now describe a system wherein the inhibitor units of the peptidase inhibitors are assigned to 48 families on the basis of similarities detectable at the level of amino acid sequence. Then, on the basis of three-dimensional structures, 31 of the families are assigned to 26 clans. A simple system of nomenclature is introduced for reference to each clan, family and inhibitor. We briefly discuss the specificities and mechanisms of the interactions of the inhibitors in the various families with their target enzymes. The system of families and clans of inhibitors described has been implemented in the MEROPS peptidase database (http://merops.sanger.ac.uk/), and this will provide a mechanism for updating it as new information becomes available.

MEROPS: the peptidase database.

Peptidases (proteolytic enzymes) are of great relevance to biology, medicine and biotechnology. This practical importance creates a need for an integrated source of information about them, and also about their natural inhibitors. The MEROPS database (http://merops.sanger.ac.uk) aims to fill this need. The organizational principle of the database is a hierarchical classification in which homologous sets of the proteins of interest are grouped in families and the homologous families are grouped in clans. Each peptidase, family and clan has a unique identifier. The database has recently been expanded to include the protein inhibitors of peptidases, and these are classified in much the same way as the peptidases. Forms of information recently added include new links to other databases, summary alignments for peptidase clans, displays to show the distribution of peptidases and inhibitors among organisms, substrate cleavage sites and indexes for expressed sequence tag libraries containing peptidases. A new way of making hyperlinks to the database has been devised and a BlastP search of our library of peptidase and inhibitor sequences has been added.

Managing peptidases in the genomic era.

The enzymes that hydrolyse peptide bonds, called peptidases or proteases, are very important to mankind and are also very numerous. The many scientists working on these enzymes are rapidly acquiring new data, and they need good methods to store it and retrieve it. The storage and retrieval require effective systems of classification and nomenclature, and it is the design and implementation of these that we mean by 'managing' peptidases. Ten years ago Rawlings and Barrett proposed the first comprehensive system for the classification of peptidases, which included a set of simple names for the families. In the present article we describe how the system has developed since then. The peptidase classification has now been adopted for use by many other databases, and provides the structure around which the MEROPS protease database (http://merops.sanger.ac.uk) is built.