On dimension reduction of clustering results in structural bioinformatics.

OPTICS is a density-based clustering algorithm that performs well in a wide variety of applications. For a set of input objects, the algorithm creates a reachability plot that can either be used to produce cluster membership assignments, or interpreted itself as an expressive two-dimensional representation of the clustering structure of the input set, even if the input set is embedded in higher dimensions. The focus of this work is a visualization method that can be applied for comparing two, independent hierarchical clusterings by assigning colors to all entries of the input database.

Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets.

Cysteine and tryptophan anomalies found when scanning all the binding sites in the Protein Data Bank.

The Protein Data Bank (PDB) is one of the richest sources of structural biological information in the World. It started to exist as the computer-readable depository of crystallographic data complementing printed papers. The proper interpretation of the content of the individual files in the PDB still needs the detailed information found in the citing publication.

When the Web meets the cell: using personalized PageRank for analyzing protein interaction networks.

Motivation: Enormous and constantly increasing quantity of biological information is represented in metabolic and in protein interaction network databases. Most of these data are freely accessible through large public depositories. The robust analysis of these resources needs novel technologies, being developed today.

A hybrid clustering of protein binding sites.

The Protein Data Bank contains the description of approximately 27 000 protein-ligand binding sites. Most of the ligands at these sites are biologically active small molecules, affecting the biological function of the protein. The classification of their binding sites may lead to relevant results in drug discovery and design.

On the asymmetry of the residue compositions of the binding sites on protein surfaces.

By screening all the ligand binding sites in the Protein Data Bank, we have found that while it is geometrically possible that a loop, formed from a protein chain with residues ZYX, would "impersonate" another chain-loop with residues XYZ by a simple twisting of either the loop or the bound ligand, it almost never happens. This fact is rather surprising, and implies a notable asymmetry, since (i) loops in the folded proteins sometimes can be flexible enough to be twisted, but (ii) ligands are almost always extremely mobile before binding to the protein, therefore they can turn around and bind to residue-sequence ZYX as well. Data availability: The supplementary Table 3 lists the appearances of the residue-sequences and their inverses in the binding sites of the whole PDB, and is available at http://www.

Four spatial points that define enzyme families.

The catalytic properties of enzymes, containing the Asp-His-Ser triads are deeply investigated for a long time. Serine endopeptidases, cutinases, acetylcholinesterases, cellulases, among other enzymes, contain these triads. We found that solely the geometric properties of just four points in the spatial structure of these enzymes are characteristic to their family.

Being a binding site: characterizing residue composition of binding sites on proteins.

The Protein Data Bank contains the description of more than 45,000 three-dimensional protein and nucleic-acid structures today. Started to exist as the computer-readable depository of crystallographic data complementing printed articles, the proper interpretation of the content of the individual files in the PDB still frequently needs the detailed information found in the citing publication. This fact implies that the fully automatic processing of the whole PDB is a very hard task.