IEDB-3D: structural data within the immune epitope database.

IEDB-3D is the 3D structural component of the Immune Epitope Database (IEDB) available via the 'Browse by 3D Structure' page at http://www.iedb.org. IEDB-3D catalogs B- and T-cell epitopes and Major Histocompatibility Complex (MHC) ligands for which 3D structures of complexes with antibodies, T-cell receptors or MHC molecules are available in the Protein Data Bank (PDB). Journal articles that are primary citations of PDB structures and that define immune epitopes are curated within IEDB as any other reference along with accompanying functional assays and immunologically relevant information. For each curated structure, IEDB-3D provides calculated data on intermolecular contacts and interface areas and includes an application, EpitopeViewer, to visualize the structures. IEDB-3D is fully embedded within IEDB, thus allowing structural data, both curated and calculated, and all accompanying information to be queried using multiple search interfaces. These include queries for epitopes recognized in different pathogens, eliciting different functional immune responses, and recognized by different components of the immune system. The query results can be downloaded in Microsoft Excel format, or the entire database, together with structural data both curated and calculated, can be downloaded in either XML or MySQL formats.

EchoLOCATION: an in silico analysis of the subcellular locations of Escherichia coli proteins and comparison with experimentally derived locations.

SUMMARY: EchoLOCATION is a database that provides a comprehensive analysis of the subcellular locations of Escherichia coli K-12 proteins. Locations are predicted by integrating data from a range of publicly available algorithms combined with extensive curation of experimental literature. The data can be searched in a variety of ways and can generate lists of subcellular proteomes for analysis. Experimental evidence supports the locations of over 500 envelope proteins (periplasm, inner and outer membrane). From analysis of disagreements between in silico predictions and experimental data, we provide an analysis of protein types where subcellular prediction algorithms are currently not accurate.

Current state-of-the-art molecular dynamics methods and applications.

Molecular dynamics simulations are used to describe the patterns, strength, and properties of protein behavior, drug-receptor interactions, the solvation of molecules, the conformational changes that a protein or molecule may undergo under various conditions, and other events that require the systematic evaluation of molecular properties in dynamic molecular systems. Only few years ago proteins were considered to be rigid body structures with very limited conformational flexibility. However, it is now clear that proteins are highly dynamic structures, the internal organization of which is the key to their 3D spatial arrangement and hence biological function. The study of protein dynamics in the lab is a very complicated, expensive, and time-consuming process. Therefore, a lot of effort and hope lies with the computers and the in silico study of protein structure and molecular dynamics. Herein, an effort has been made to describe the ever-evolving field of molecular dynamics, the different algorithms, and force fields that are being used as well as to provide some insight on what the near future holds for this auspicious field of computational structural biology.

Limitations of Ab initio predictions of peptide binding to MHC class II molecules.

Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.