Drugster is a de novo Drug Design platform, which through the versatility of the elite software that it incorporates can efficiently exploit single or multiple processor workstations and achieve high performance through novel and faster custom-made routines. Drugster is a freeware platform aimed to assist scientists in the field of Computer Aided Drug Design (CADD). It facilitates the use of other freeware applications (PDB2PQR, Gromacs, Ligbuilder, Dock) in order to create a pipeline for producing high quality results.
Citation: Vlachakis D, Tsagkrasoulis D, Megalooikonomou V, Kossida S. (2012) Introducing Drugster: a comprehensive drug design, lead and structure optimization toolkit. Bioinformatics, 2013, 29(1):126-128. [doi: 10.1093/bioinformatics/bts637]
During the past few years, pharmacophore modeling has become one of the key components in computer-aided drug design and in modern drug discovery. DrugOn is a fully interactive pipeline designed to exploit the advantages of modern programming and overcome the command line barrier with two friendly environments for the user (either novice or experienced in the field of Computer Aided Drug Design) to perform pharmacophore modeling through an efficient combination of the PharmACOphore, Gromacs, Ligbuilder and PDB2PQR suites. Our platform features a novel workflow that guides the user through each logical step of the iterative 3D structural optimization setup and drug design process. For the pharmacophore modeling we are focusing on either the characteristics of the receptor or the full molecular system, including a set of selected ligands.
Citation: Vlachakis D, Fakourelis P, Megalooikonomou V, Makris C, Kossida S. (2015) DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit. PeerJ, 2015; 13;3:e725. . [doi: 10.7717/peerj.725]
With the extensive use of microarray technology as a potential prognostic and diagnostic tool, the comparison and reproducibility of results obtained from the use of different platforms is of interest. The integration of those datasets can yield more informative results corresponding to numerous datasets and microarray platforms. We developed a novel integration technique for microarray gene-expression data derived by different studies for the purpose of a two-way Bayesian partition modelling which estimates co-expression profiles under subsets of genes and between biological samples or experimental conditions. The suggested methodology transforms disparate gene-expression data on a common probability scale to obtain inter-study-validated gene signatures. We evaluated the performance of our model using artificial data. Finally, we applied our model to six publicly available cancer gene-expression datasets and compared our results with well-known integrative microarray data methods. Our study shows that the suggested framework can relieve the limited sample size problem while reporting high accuracies by integrating multi-experiment data.
Citation: Tsiliki G, Vlachakis D, Kossida S. (2014) On integrating multi-experiment microarray data. Phil. Trans. R. Soc. A 2014 372 20130136[doi:10.1098/rsta.2013.0136]
Protein structure is more conserved than sequence in nature. In this direction we developed a novel methodology that significantly improves conventional homology modelling when sequence identity is low, by taking into consideration 3D structural features of the template, such as size and shape. Herein, our new homology modelling approach was applied to the homology modelling of the RNA-dependent RNA polymerase (RdRp) of dengue (type II) virus. The RdRp of dengue was chosen due to the low sequence similarity shared between the dengue virus polymerase and the available templates, while purposely avoiding to use the actual X-ray structure that is available for the dengue RdRp. The novel approach takes advantage of 3D space corresponding to protein shape and size by creating a 3D scaffold of the template structure. The dengue polymerase model built by the novel approach exhibited all features of RNA-dependent RNA polymerases and was almost identical to the X-ray structure of the dengue RdRp, as opposed to the model built by conventional homology modelling. Therefore, we propose that the space-aided homology modelling approach can be of a more general use to homology modelling of enzymes sharing low sequence similarity with the template structures.
Citation: Vlachakis D, Kontopoulos DG, Kossida S. (2013) Space Constrained Homology Modelling: The Paradigm of the RNA-Dependent RNA Polymerase of Dengue (Type II) Virus. Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 108910, 9 pages. [doi: 10.1155/2013/108910]
The term 'molecular cartography' encompasses a family of computational methods for two-dimensional transformation of protein structures and analysis of their physicochemical properties. The underlying algorithms comprise multiple manual steps, whereas the few existing implementations typically restrict the user to a very limited set of molecular descriptors.
RESULTS: We present Structuprint, a free standalone software that fully automates the rendering of protein surface maps, given - at the very least - a directory with a PDB file and an amino acid property. The tool comes with a default database of 328 descriptors, which can be extended or substituted by user-provided ones. The core algorithm comprises the generation of a mould of the protein surface, which is subsequently converted to a sphere and mapped to two dimensions, using the Miller cylindrical projection. Structuprint is partly optimized for multicore computers, making the rendering of animations of entire molecular dynamics simulations feasible.
CONCLUSIONS: Structuprint is an efficient application, implementing a molecular cartography algorithm for protein surfaces. According to the results of a benchmark, its memory requirements and execution time are reasonable, allowing it to run even on low-end personal computers. We believe that it will be of use - primarily but not exclusively - to structural biologists and computational biochemists.
Citation: Vlachakis D, Tsagkrasoulis D, Megalooikonomou V, Kossida S. (2016) Structuprint: a scalable and extensible tool for two-dimensional representation of protein surfaces. BMC Struct Biol. 2016;16:4. [doi: 10.1186/s12900-016-0055-7]
An extensive effort is made by The Gene Ontology Consortium in order to gather all the protein – function pairs in a standard format and produce a well structured vocabulary that would present all the known biological functions in a hierarchical, controlled structure.
Taggo takes advantage of the Gene Ontology (GO) to extract the proteins’ main attributes. It combines the potential of discarding annotations that are supported by not so reliable Ecs, it is an extremely fast process, it offers the convenience of searching the ten most general categories of each term, and it allows usage of one of the most reliable Biological Ontologies (Gene Ontology) for the results’ extraction (well structured ontologies based on biological evidence, widely accepted nomenclature)
Liquid Chromatography-Mass Spectrometry (LC-MS) is a commonly used method to detect protein-protein interactions and elucidate complex protein mixtures. Visualization of large data sets produced from LC-MS, specifically the chromatograph and the mass spectra that correspond to its peaks is the focus of this work. The Brukin2d software, developed with Matlab 7.4, uses the compound data that are exported from Bruker 'DataAnalysis' program, and depicts the mean mass spectra of all the chromatograph compounds from one LC-MS run, in one 2D contour plot. Each spot in the plot represents one peptide mass. The spot y-axis position is determined by the time of its compound in the chromatograph, while the x-axis position is the mass to charge ratio (m/z). The darkness of the spots is proportional to the intensity of the mass peaks. Deconvolution data are also supported; to visualize uniquely the main singled-charged isotopes. Two contour plots from different chromatograph runs can then be viewed in the same window and automatically compared, in order to find the similarities and the differences between them. The results of the comparison can be examined through detailed mass quantization tables, while chromatograph compound statistics are also calculated during the procedure.
GIBA is an effective and easy to use tool for the detection of protein complexes through clustering on protein - protein interaction networks. It was proved with extensive experiments that GIBA produces more quality approximations of protein complexes than other methods.
The algorithm is a hierarchical one and performs successive min –cuts until it identifies dense subgraphs. The stopping criterion of the AHC depends on the initial graph density and it is adjusted to each case accordingly. That means that when the input data forms a dense protein interaction network, the stopping criterion of the AHC is stricter and leads to the selection of more dense subgraphs as protein complexes candidates. Otherwise, if the input data forms a relatively sparse protein interaction network, its stopping criterion is resilient and allows the selection of less dense subgraphs as protein complexes candidates.
GAppi performs clustering in protein-protein interaction networks to identify protein complexes.
The algorithm has been tested exhaustively with experimental datasets coming from online protein interaction databases and individual experiments and has been compared with five other effective techniques in order to demonstrate its efficiency and superior performance.
Results showed that GAppi produces feasible and very efficient solutions compared to other techniques. Except from that, due to its adaptive behavior, each time it is used it can satisfy different constraints, thus meeting the different needs of each user. Furthermore, a user friendly interface has been implemented that hosts the proposed algorithmic strategy.
GOmir (by using up to four different databases) introduces, for the first time, miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted.
GOmir module, JTarget, integrates microRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools and also providing a full gene description and functional analysis for each target gene. In addition, GOmir incorporates TAGGO application, which has been designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins.
FED & SAFE Suites
The tools that perform this analysis are:
1. Fusion Events Database (FED), a database for the maintenance and retrieval of fusion data both in prokaryotic and eukaryotic organisms and
2. Software for the Analysis of Fusion Events (SAFE), a computational platform implemented for the automated detection, filtering and visualization of fusion events. Fusion analysis has been used to identify putative protein-protein interactions in completely sequenced genomes of various prokaryotes, and eukaryotes.