MolLoc: a web tool for the local structural alignment of molecular surfaces.

MolLoc stands for Molecular Local surface comparison, and is a web server for the structural comparison of molecular surfaces. Given two structures in PDB format, the user can compare their binding sites, cavities or any arbitrary residue selection. Moreover, the web server allows the comparison of a query structure with a list of structures. Each comparison produces a structural alignment that maximizes the extension of the superimposition of the surfaces, and returns the pairs of atoms with similar physicochemical properties that are close in space after the superimposition. Based on this subset of atoms sharing similar physicochemical properties a new rototranslation is derived that best superimposes them. MolLoc approach is both local and surface-oriented, and therefore it can be particularly useful when testing if molecules with different sequences and folds share any local surface similarity. The MolLoc web server is available at http://bcb.dei.unipd.it/MolLoc.

Discovery of similar regions on protein surfaces.

Discovery of a similar region on two protein surfaces can lead to important inference about the functional role or molecular interaction of this region for one of the proteins if such information is available for the other. We propose a new characterization of protein surfaces based on a spin-image representation of the surfaces that facilitates the simultaneous search of the entire surface of each of two proteins for a matching region. For a surface point, we introduce spin-image profiles that are related to the degree of exposure of the point to identify structurally equivalent surface regions in two proteins. Unlike some related methods, we do not assume that a known fixed region of one of the protein surfaces is to be matched on the other protein surface. Rather, we search for the largest similar regions on each of the two surfaces. In spite of the fact that this approach is entirely geometric and no use is made of physicochemical properties of the protein surfaces or fold information, it is effective in identifying similar regions on both surfaces even when the region corresponds to a binding site on one of the proteins. The discovery of similar regions on two or more proteins also has implications for drug design and pharmacophore identification. We present experimental results from datasets of more than 50 protein surfaces.

Monotony of surprise and large-scale quest for unusual words.

The problem of characterizing and detecting recurrent sequence patterns such as substrings or motifs and related associations or rules is variously pursued in order to compress data, unveil structure, infer succinct descriptions, extract and classify features, etc. In molecular biology, exceptionally frequent or rare words in bio-sequences have been implicated in various facets of biological function and structure. The discovery, particularly on a massive scale, of such patterns poses interesting methodological and algorithmic problems and often exposes scenarios in which tables and synopses grow faster and bigger than the raw sequences they are meant to encapsulate. In previous study, the ability to succinctly compute, store, and display unusual substrings has been linked to a subtle interplay between the combinatorics of the subword of a word and local monotonicities of some scores used to measure the departure from expectation. In this paper, we carry out an extensive analysis of such monotonicities for a broader variety of scores. This supports the construction of data structures and algorithms capable of performing global detection of unusual substrings in time and space linear in the subject sequences, under various probabilistic models.

On the functional and structural characterization of hubs in protein-protein interaction networks.

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in Protein-Protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: Do hub proteins have special biological properties? Do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? Do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins?

Effective labeling of molecular surface points for cavity detection and location of putative binding sites.

We present a method for detecting and comparing cavities on protein surfaces that is useful for protein binding site recognition. The method is based on a representation of the protein structures by a collection of spin-images and their associated spin-image profiles. Results of the cavity detection procedure are presented for a large set of non-redundant proteins and compared with SURFNET-ConSurf. Our comparison method is used to find a surface region in one cavity of a protein that is geometrically similar to a surface region in the cavity of another protein. Such a finding would be an indication that the two regions likely bind to the same ligand. Our overall approach for cavity detection and comparison is benchmarked on several pairs of known complexes, obtaining a good coverage of the atoms of the binding sites.