CavBase is a database containing information about the three-dimensional geometry and the physicochemical properties of putative protein binding sites. Analyzing CavBase data typically involves computing the similarity of pairs of binding sites. In contrast to sequence alignment, however, a structural comparison of protein binding sites is a computationally challenging problem, making large scale studies difficult or even infeasible. One possibility to overcome this obstacle is to precompute pairwise similarities in an all-against-all comparison, and to make these similarities subsequently accessible to data analysis methods. Pairwise similarities, once being computed, can also be used to equip CavBase with a neighborhood structure. Taking advantage of this structure, methods for problems such as similarity retrieval can be implemented efficiently. In this paper, we tackle the problem of performing an all-against-all comparison using CavBase, consisting of more than 200,000 protein cavities, by means of parallel computation and cloud computing techniques. We present the conceptual design and technical realization of a large-scale study to create a similarity database called CavSimBase. We illustrate how CavSimBase is constructed, is accessed, and is used to answer biological questions by data analysis and similarity retrieval.

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. Therefore, for the comparison of binding sites, a less detailed coarse graph model is used building upon so-called pseudocenters. Consistently, a loss of structural data is caused since many atoms are discarded and no information about the shape of the binding site is considered. This is usually resolved by performing subsequent calculations based on additional information. These steps are usually quite expensive, making the whole approach very slow. The main drawback of a graph-based model solely based on pseudocenters, however, is the loss of information about the shape of the protein surface. In this study, we propose a novel and efficient modeling formalism that does not increase the size of the graph model compared to the original approach, but leads to graphs containing considerably more information assigned to the nodes. More specifically, additional descriptors considering surface characteristics are extracted from the local surface and attributed to the pseudocenters stored in Cavbase. These properties are evaluated as additional node labels, which lead to a gain of information and allow for much faster but still very accurate comparisons between different structures.

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. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.

UNLABELLED: fconv is a program intended for parsing and manipulating multiple aspects and properties of molecular data. Up to now, it has been developed and extensively tested for 3 years. It has become a very robust and comprehensive tool involved in a broad range of computational workflows that are currently applied in our drug design environment. Typical tasks are as follows: conversion and error correction of formats such as PDB(QT), MOL2, SDF, DLG and CIF; extracting ligands from PDB as MOL2; automatic or ligand-based cavity detection; rmsd calculation and clustering; substructure searches; alignment and structural superposition; building of crystal packings; adding hydrogens; calculation of various properties like the number of rotatable bonds; molecular weights or vdW volumes. The atom type classification is based on a consistent assignment of internal atom types, which are by far more differentiated compared with e.g. Sybyl atom types. Apart from the predefined mapping of these types onto Sybyl types, the user is able to assign own mappings by providing modified template files, thus allowing for tailor-made atom type sets. AVAILABILITY: fconv is free software available under GNU General Public License. C++ sources and precompiled executables for LINUX/UNIX, Mac OS and Windows, as well as tutorials are available on http://www.agklebe.de.

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. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites.

Comparative analysis is a topic of utmost importance in structural bioinformatics. Recently, a structural counterpart to sequence alignment, called multiple graph alignment, was introduced as a tool for the comparison of protein structures in general and protein binding sites in particular. Using approximate graph matching techniques, this method enables the identification of approximately conserved patterns in functionally related structures. In this paper, we introduce a new method for computing graph alignments motivated by two problems of the original approach, a conceptual and a computational one. First, the existing approach is of limited usefulness for structures that only share common substructures. Second, the goal to find a globally optimal alignment leads to an optimization problem that is computationally intractable. To overcome these disadvantages, we propose a semiglobal approach to graph alignment in analogy to semiglobal sequence alignment that combines the advantages of local and global graph matching.

We proceed from a method for protein structure comparison in which information about the geometry and physico-chemical properties of such structures are represented in the form of labeled point clouds, that is, a set of labeled points in three-dimensional Euclidean space. Two point clouds are then compared by computing an optimal spatial superposition. This approach has recently been introduced in the literature and was shown to produce very good similarity scores. It does not, however, establish an alignment in the sense of a one-to-one correspondence between the basic units of two or more protein structures. From a biological point of view, alignments of this kind are of great interest, as they offer important information about evolution, heredity, and the mutual correspondence between molecular constituents. In this paper, we therefore developed a method for computing pairwise or multiple alignments of protein structures on the basis of labeled point cloud superpositions.

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. This article first recalls the concept of MGA and then addresses the problem of computing optimal alignments from an algorithmic point of view. In this regard, a method from the field of evolutionary algorithms is proposed and empirically compared with a hitherto existing heuristic approach. Empirically, it is shown that the former yields significantly better results than the latter, albeit at the cost of an increased runtime.

Graphs are frequently used to describe the geometry and also the physicochemical composition of protein active sites. Here, the concept of graph alignment as a novel method for the structural analysis of protein binding pockets is presented. Using inexact, approximate graph-matching techniques, our method enables the robust identification of fuzzily conserved areas in binding pockets. Thus, using multiple graph alignments, it is possible to characterize functional protein families independent of sequence or fold homology. This paper first introduces the problem of graph alignment in a formal way and discusses algorithmic solutions for this problem. Then, it is shown how the calculated graph alignments can be analyzed to identify structural features that are characteristic for a given protein family. In this connection, the related concept of a fuzzy consensus graph is introduced. The methods are applied to a substantial high-quality subset of the PDB database and their ability to successfully characterize and classify 10 highly populated functional protein families is shown.

Graphs are frequently used to describe the geometry and also the physicochemical composition of protein active sites. Here, the concept of graph alignment as a novel method for the structural analysis of protein binding pockets is presented. Using inexact graph-matching techniques, one is able to identify both conserved areas and regions of difference among different binding pockets. Thus, using multiple graph alignments, it is possible to characterize functional protein families and to examine differences among related protein families independent of sequence or fold homology. Optimized algorithms are described for the efficient calculation of multiple graph alignments for the analysis of physicochemical descriptors representing protein binding pockets. Additionally, it is shown how the calculated graph alignments can be analyzed to identify structural features that are characteristic for a given protein family and also features that are discriminative among related families. The methods are applied to a substantial high-quality subset of the PDB database and their ability to successfully characterize and classify 10 highly populated functional protein families is shown. Additionally, two related protein families from the group of serine proteases are examined and important structural differences are detected automatically and efficiently

MOTIVATION: Graph-based clique-detection techniques are widely used for the recognition of common substructures in proteins. They permit the detection of resemblances that are independent of sequence or fold homologies and are also able to handle conformational flexibility. Their high computational complexity is often a limiting factor and prevents a detailed and fine-grained modeling of the protein structure. RESULTS: We present an efficient two-step method that significantly speeds up the detection of common substructures, especially when used to screen larger databases. It combines the advantages from both clique-detection and geometric hashing. The method is applied to an established approach for the comparison of protein binding-pockets, and some empirical results are presented. AVAILABILITY: Upon request from the authors.