GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.

In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods.

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored.

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase.

To calculate similarities between molecular structures, measures based on the maximum common subgraph are frequently applied. For the comparison of protein binding sites, these measures are not fully appropriate since graphs representing binding sites on a detailed atomic level tend to get very large. In combination with an NP-hard problem, a large graph leads to a computationally demanding task.

Extraction of protein binding pockets in close neighborhood of bound ligands makes comparisons simple due to inherent shape similarity.

Methods for comparing protein binding sites are frequently validated on data sets of pockets that were obtained simply by extracting the protein area next to the bound ligands. With this strategy, any unoccupied pocket will remain unconsidered. Furthermore, a large amount of ligand-biased intrinsic shape information is predefined, inclining the subsequent comparisons as rather trivial even in data sets that hardly contain redundancies in sequence information.

Fingerprint Kernels for Protein Structure Comparison.

A key task in structural biology is to define a meaningful similarity measure for the comparison of protein structures. Recently, the use of graphs as modeling tools for molecular data has gained increasing importance. In this context, kernel functions have attracted a lot of attention, especially since they allow for the application of a rich repertoire of methods from the field of kernel-based machine learning.

Superposition and alignment of labeled point clouds.

Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property.

GARLig: a fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm.

In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required.

Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules.

The concept of multiple graph alignment (MGA) has recently been introduced as a novel method for the structural analysis of biomolecules. Using approximate graph matching techniques, this method enables the robust identification of approximately conserved patterns in biologically related structures. In particular, MGA enables the characterization of functional protein families independent of sequence or fold homology.