One of the most important and challenging tasks in protein modelling is the prediction of loops, as can be seen in the large variety of existing approaches. Loops In Proteins (LIP) is a database that includes all protein segments of a length up to 15 residues contained in the Protein Data Bank (PDB). In this study, the applicability of LIP to loop prediction in the framework of homology modelling is investigated. Searching the database for loop candidates takes less than 1 s on a desktop PC, and ranking them takes a few minutes. This is an order of magnitude faster than most existing procedures. The measure of accuracy is the root mean square deviation (RMSD) with respect to the main-chain atoms after local superposition of target loop and predicted loop. Loops of up to nine residues length were modelled with a local RMSD <1 A and those of length up to 14 residues with an accuracy better than 2 A. The results were compared in detail with a thoroughly evaluated and tested ab initio method published recently and additionally with two further methods for a small loop test set. The LIP method produced very good predictions. In particular for longer loops it outperformed other methods.

SuperLooper provides the first online interface for the automatic, quick and interactive search and placement of loops in proteins (LIP). A database containing half a billion segments of water-soluble proteins with lengths up to 35 residues can be screened for candidate loops. A specified database containing 180 000 membrane loops in proteins (LIMP) can be searched, alternatively. Loop candidates are scored based on sequence criteria and the root mean square deviation (RMSD) of the stem atoms. Searching LIP, the average global RMSD of the respective top-ranked loops to the original loops is benchmarked to be <2 Å, for loops up to six residues or <3 Å for loops shorter than 10 residues. Other suitable conformations may be selected and directly visualized on the web server from a top-50 list. For user guidance, the sequence homology between the template and the original sequence, proline or glycine exchanges or close contacts between a loop candidate and the remainder of the protein are denoted. For membrane proteins, the expansions of the lipid bilayer are automatically modeled using the TMDET algorithm. This allows the user to select the optimal membrane protein loop concerning its relative orientation to the lipid bilayer. The server is online since October 2007 and can be freely accessed at URL: http://bioinformatics.charite.de/superlooper/

BackgroundThe increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.ResultsIntegration of two public databases allowed superposition of conformers for 193 approved drugs with 5507 crystallised target-bound counterparts. The generation of 9600 drug conformers using an atomic force field was carried out to obtain an optimal coverage of the conformational space.Bioactive conformations are best described by a conformational ensemble: half of all drugs exhibit multiple active states, distributed over the entire range of the reachable energy and conformational space.A number of up to 100 conformers per drug enabled us to reproduce the bound states within a similarity threshold of 1.0 Å in 70% of all cases. This fraction rises to about 90% for smaller or average sized drugs.ConclusionSingle drugs adopt multiple bioactive conformations if they interact with different target proteins. Due to the structural diversity of binding sites they adopt conformations that are distributed over a broad conformational space and wide energy range. Since the majority of drugs is well represented by a predefined low number of conformers (up to 100) this procedure is a valuable method to compare compounds by three-dimensional features or for fast similarity searches starting with pharmacophores.The underlying 9600 generated drug conformers are downloadable from the Super Drug Web site [1]. All superpositions are visualised at the same source. Additional conformers (110,000) of 2400 classified WHO-drugs are also available.

Disialoganglioside GD2 is an established target for immunotherapy in neuroblastoma. We tested the hypothesis that active immunization against the glycolipid GD2 using DNA vaccines encoding for cyclic GD2-mimicking decapeptides (i.e., GD2 mimotopes) is effective against neuroblastoma. For this purpose, two GD2 peptide mimotopes (MA and MD) were selected based on docking experiments to anti-GD2 antibody ch14.18 (binding free energy: À41.23 kJ/mol for MA and À48.06 kJ/mol for MD) and Biacore analysis (mol/L for MA and 5.3 Â 10 À5 mol/L for MD), showing a higher affinity of MD over MA. These sequences were selected for DNA vaccine design based on pSecTag2-A (pSA) also including a T-cell helper epitope. GD2 mimicry was shown following transfection of CHO-1 cells with pSA-MA and pSA-MD DNA vaccines, with twice-higher signal intensity for cells expressing MD over MA. Finally, these DNA vaccines were tested for induction of tumor protective immunity in a syngeneic neuroblastoma model following oral DNA vaccine delivery with attenuated Salmonella typhimurium (SL 7207). Only mice receiving the DNA vaccines revealed a reduction of spontaneous liver metastases. The highest anti-GD2 humoral immune response and natural killer cell activation was observed in mice immunized with the pSA-MD, a finding consistent with superior calculated binding free energy, dissociation constant, and GD2 mimicry potential for GD2 mimotope MD over MA. In summary, we show that DNA immunization with pSA-MD may provide a useful strategy for active immunization against neuroblastoma. (Cancer Res 2006; 66(21): 10567-75)