92C40 Biochemistry, molecular biology
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One crucial step in virtual drug design is the identification of new lead structures with respect to a pharmacological target molecule. The search for new lead structures is often done with the help of a pharmacophore, which carries the essential structural as well as physico-chemical properties that a molecule needs to have in order to bind to the target molecule. In the absence of the target molecule, such a pharmacophore can be established by comparison of a set of active compounds. In order to identify their common features,a multiple alignment of all or most of the active compounds is necessary. Moreover, since the “outer shape” of the molecules plays a major role in the interaction between drug and target, an alignment algorithm aiming at the identification of common binding properties needs to consider the molecule’s “outer shape”, which can be approximated by the solvent excluded surface. In this thesis, we present a new approach to molecular surface alignment based on a discrete representation of shape as well as physico-chemical properties by points distributed on the solvent excluded surface. We propose a new method to distribute points regularly on a surface w.r.t. a smoothly varying point density given on that surface. Since the point distribution algorithm is not restricted to molecular surfaces, it might also be of interest for other applications. For the computation of pairwise surface alignments, we extend an existing point matching scheme to surface points, and we develop an efficient data structure speeding up the computation by a factor of three. Moreover, we present an approach to compute multiple alignments from pairwise alignments, which is able to handle a large number of surface points. All algorithms are evaluated on two sets of molecules: eight thermolysin inhibitors and seven HIV-1 protease inhibitors. Finally, we compare the results obtained from surface alignment with the results obtained by applying an atom alignment approach.
Biochemical interactions are determined by the 3D-structure of the involved components - thus the identification of conformations is a key for many applications in rational drug design. {\sf ConFlow} is a new multilevel approach to conformational analysis with main focus on completeness in investigation of conformational space. In contrast to known conformational analysis, the starting point for design is a space-based description of conformational areas. A tight integration of sampling and analysis leads to an identification of conformational areas simultaneously during sampling. An incremental decomposition of high-dimensional conformational space is used to guide the analysis. A new concept for the description of conformations and their path connected components based on convex hulls and {\em Hypercubes}is developed. The first results of the {\sf ConFlow} application constitute a 'proof of concept' and are further more highly encouraging. In comparison to conventional industrial applications, {\sf ConFlow} achieves higher accuracy and a specified degree of completeness with comparable effort.
A new and time efficient model to evaluate the free energy of solvation has been developed. The solvation free energy is separated into an electrostatic term, a hydrogen bond term, and a rest-term, combining both entropic and van der Waals effects. The electrostatic contribution is evaluated with a simplified boundary element method using the partial charges of the MMFF94 force field. The number of hydrogen bonds and the solvent excluded surface area over the surface atoms are used in a linear model to estimate the non-electrostatic contribution. This model is applied to a set of 213 small and mostly organic molecules, yielding an rmsd of 0.87kcal/mol and a correlation with experimental data of r=0.951. The model is applied as a supplementary component of the free energy of binding to estimate binding constants of protein ligand complexes. The intermolecular interaction energy is evaluated by using the MMFF94 force field.
Many real world problems can be mapped onto graphs and solved with well-established efficient algorithms studied in graph theory. One such problem is to find large sets of points satisfying some mutual relationship. This problem can be transformed to the problem of finding all cliques of an undirected graph by mapping each point onto a vertex of the graph and connecting any two vertices by an edge whose corresponding points satisfy our desired relationship. Clique detection has been widely studied and there exist efficient algorithms. In this paper we study a related problem, where all points have a set of binary attributes, each of which is either 0 or 1. This is only a small limitation, since all discrete properties can be mapped onto binary attributes. In our case, we want to find large sets of points not only satisfying some mutual relationship; but, in addition, all points of a set also need to have at least one common attribute with value 1. The problem we described can be mapped onto a set of induced subgraphs, where each subgraph represents a single attribute. For attribute $i$, its associated subgraph contains those vertices corresponding to the points with attribute $i$ set to 1. We introduce the notion of a maximal clique of a family, $\mathcal{G}$, of induced subgraphs of an undirected graph, and show that determining all maximal cliques of $\mathcal{G}$ solves our problem. Furthermore, we present an efficient algorithm to compute all maximal cliques of $\mathcal{G}$. The algorithm we propose is an extension of the widely used Bron-Kerbosch algorithm.
A recently developed algorithm allows Rigid Body Docking of ligands to proteins, regardless of the accessibility and location of the binding site. The Docking procedure is divided into three subsequent optimization phases, two of which utilize rigid body dynamics. The last one is applied with the ligand already positioned inside the binding pocket and accounts for full flexibility. Initially, a combination of geometrical and force-field based methods is used as a Coarse Docking strategy, considering only Lennard-Jones interactions between the target and pharmaceutically relevant atoms or functional groups. The protein is subjected to a Hot Spot Analysis, which reveals points of high affinity in the protein environment towards these groups. The hot spots are distributed into different subsets according to their group affiliation. The ligand is described as a complementary point set, consisting of the same subsets. Both sets are matched in $\mathrm{I\!R}^{3}$, by superimposing members of the same subsets. In the first instance, steric inhibition is nearly neglected, preventing the system's trajectory from trapping in local minima and thus from finding false positive solutions. Hence the exact location of the binding site can be determined fast and reliably without any additional information. Subsequently, errors resulting from approximations are minimized via finetuning, this time considering both Lennard-Jones and Coulomb forces. Finally, the potential energy of the whole complex is minimized. In a first evaluation, results are rated by a reduced scoring function considering only noncovalent interaction energies. Exemplary Screening results will be given for specific ligands.
RNA 3D-Modeling
(2003)
This article presents a new computational approach to the three-dimensional (3D) modeling of ribonucleic acid (RNA) sequences with unknown spatial structure. The main concept is a mapping of the query sequence onto the 3D structures of a suitable template RNA molecule. This technique called \textit{threading} has originally been developed for the modeling of protein 3D structures. The application to RNA systems bridges the information gap between the growing mass of RNA sequence data and the relatively limited number of available 3D structures. The new RNA threading method is demonstrated on a tRNA model system because sufficient representative 3D structures have experimentally been elucidated and deposited in the public databases. Nevertheless, the method is in principle transferable on all other RNA species. Algorithms are developed that decompose these template structures into their secondary structure elements and gather this information in a specific template database. The best template is chosen with public alignment and secondary structure prediction tools which are integrated in the RNA modeling module. The structural information gathered from the template and the best alignment is combined to establish a comprehensive 3D model of the query sequence. A range of complete tRNA structures has successfully been modeled with the RNA threading method. The prototype module visualizes the models and provides convenient access to the proposed 3D structures. Therefore, the method could give new insight into a variety of RNA systems which in the recent years have become increasingly important as potential new pharmaceutical agents.
Pyridinochelin, a novel catecholate type siderophore, has been designed on the basis of the active analog enterobactin. Growth promotion tests indicate that this synthetic siderophore feeds various pathogenic bacteria effectively with iron even though it lacks one catecholate group compared to enterobactin. The superposition of the siderophore structures suggests that the structure of the skeleton connecting the catecholate groups might be an important factor for the iron transport.
The Monte Carlo simulation of the dynamics of complex molecules produces trajectories with a large number of different configurations to sample configuration space. It is expected that these configurations can be classified into a small number of conformations representing essential changes in the shape of the molecule. We present a method to visualize these conformations by point sets in the plane based on a geometrical distance measure between individual configurations. It turns out that different conformations appear as well-separated point sets. The method is further improved by performing a cluster analysis of the data set. The point-cluster representation is used to control a three-dimensional molecule viewer application to show individual configurations and conformational changes. The extraction of essential coordinates and visualization of molecular shape is discussed.