Efficient protein alignment algorithm for protein search

© 2010 Lu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution Licens

SuperMimic – Fitting peptide mimetics into protein structures

BACKGROUND: Various experimental techniques yield peptides that are biologically active but have unfavourable pharmacological properties. The design of structurally similar organic compounds, i.e. peptide mimetics, is a challenging field in medicinal chemistry. RESULTS: SuperMimic identifies compounds that mimic parts of a protein, or positions in proteins that are suitable for inserting mimetics. The application provides libraries that contain peptidomimetic building blocks on the one hand and protein structures on the other. The search for promising peptidomimetic linkers for a given peptide is based on the superposition of the peptide with several conformers of the mimetic. New synthetic elements or proteins can be imported and used for searching. CONCLUSION: We present a graphical user interface for finding peptide mimetics that can be inserted into a protein or for fitting small molecules into a protein. Using SuperMimic, promising locations in proteins for the insertion of mimetics can be found quickly and conveniently

In silico modelling of hormone response elements

BACKGROUND: An important step in understanding the conditions that specify gene expression is the recognition of gene regulatory elements. Due to high diversity of different types of transcription factors and their DNA binding preferences, it is a challenging problem to establish an accurate model for recognition of functional regulatory elements in promoters of eukaryotic genes. RESULTS: We present a method for precise prediction of a large group of transcription factor binding sites – steroid hormone response elements. We use a large training set of experimentally confirmed steroid hormone response elements, and adapt a sequence-based statistic method of position weight matrix, for identification of the binding sites in the query sequences. To estimate the accuracy level, a table of correspondence of sensitivity vs. specificity values is constructed from a number of independent tests. Furthermore, feed-forward neural network is used for cross-verification of the predicted response elements on genomic sequences. CONCLUSION: The proposed method demonstrates high accuracy level, and therefore can be used for prediction of hormone response elements de novo. Experimental results support our analysis by showing significant improvement of the proposed method over previous HRE recognition methods

Gene function prediction using labeled and unlabeled data

Research articl

SODa: An Mn/Fe superoxide dismutase prediction and design server

Background: Superoxide dismutases (SODs) are ubiquitous metalloenzymes that play an important role in the defense of aerobic organisms against oxidative stress, by converting reactive oxygen species into nontoxic molecules. We focus here on the SOD family that uses Fe or Mn as cofactor. Results: The SODa webtool http://babylone.ulb.ac.be/soda predicts if a target sequence corresponds to an Fe/Mn SOD. If so, it predicts the metal ion specificity (Fe, Mn or cambialistic) and the oligomerization mode (dimer or tetramer) of the target. In addition, SODa proposes a list of residue substitutions likely to improve the predicted preferences for the metal cofactor and oligomerization mode. The method is based on residue fingerprints, consisting of residues conserved in SOD sequences or typical of SOD subgroups, and of interaction fingerprints, containing residue pairs that are in contact in SOD structures. Conclusion: SODa is shown to outperform and to be more discriminative than traditional techniques based on pairwise sequence alignments. Moreover, the fact that it proposes selected mutations makes it a valuable tool for rational protein design. © 2008 Kwasigroch et al; licensee BioMed Central Ltd.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

In silico segmentations of lentivirus envelope sequences

BACKGROUND: The gene encoding the envelope of lentiviruses exhibits a considerable plasticity, particularly the region which encodes the surface (SU) glycoprotein. Interestingly, mutations do not appear uniformly along the sequence of SU, but they are clustered in restricted areas, called variable (V) regions, which are interspersed with relatively more stable regions, called constant (C) regions. We look for specific signatures of C/V regions, using hidden Markov models constructed with SU sequences of the equine, human, small ruminant and simian lentiviruses. RESULTS: Our models yield clear and accurate delimitations of the C/V regions, when the test set and the training set were made up of sequences of the same lentivirus, but also when they were made up of sequences of different lentiviruses. Interestingly, the models predicted the different regions of lentiviruses such as the bovine and feline lentiviruses, not used in the training set. Models based on composite training sets produce accurate segmentations of sequences of all these lentiviruses. CONCLUSION: Our results suggest that each C/V region has a specific statistical oligonucleotide composition, and that the C (respectively V) regions of one of these lentiviruses are statistically more similar to the C (respectively V) regions of the other lentiviruses, than to the V (respectively C) regions of the same lentivirus

In silico panning for a non-competitive peptide inhibitor

BACKGROUND: Peptide ligands have tremendous therapeutic potential as efficacious drugs. Currently, more than 40 peptides are available in the market for a drug. However, since costly and time-consuming synthesis procedures represent a problem for high-throughput screening, novel procedures to reduce the time and labor involved in screening peptide ligands are required. We propose the novel approach of 'in silico panning' which consists of a two-stage screening, involving affinity selection by docking simulation and evolution of the peptide ligand using genetic algorithms (GAs). In silico panning was successfully applied to the selection of peptide inhibitor for water-soluble quinoprotein glucose dehydrogenase (PQQGDH). RESULTS: The evolution of peptide ligands for a target enzyme was achieved by combining a docking simulation with evolution of the peptide ligand using genetic algorithms (GAs), which mimic Darwinian evolution. Designation of the target area as next to the substrate-binding site of the enzyme in the docking simulation enabled the selection of a non-competitive inhibitor. In all, four rounds of selection were carried out on the computer; the distribution of the docking energy decreased gradually for each generation and improvements in the docking energy were observed over the four rounds of selection. One of the top three selected peptides with the lowest docking energy, 'SERG' showed an inhibitory effect with K(i )value of 20 μM. PQQGDH activity, in terms of the V(max )value, was 3-fold lower than that of the wild-type enzyme in the presence of this peptide. The mechanism of the SERG blockage of the enzyme was identified as non-competitive inhibition. We confirmed the specific binding of the peptide, and its equilibrium dissociation constant (K(D)) value was calculated as 60 μM by surface plasmon resonance (SPR) analysis. CONCLUSION: We demonstrate an effective methodology of in silico panning for the selection of a non-competitive peptide inhibitor from small virtual peptide library. This study is the first to demonstrate the usefulness of in silico evolution using experimental data. Our study highlights the usefulness of this strategy for structure-based screening of enzyme inhibitors