Gerhard Klebe

PDB2PQR: Expanding and upgrading automated preparation of biomolecular structures for molecular simulations

Real-world observable physical and chemical characteristics are increasingly being calculated from the 3D structures of biomolecules. Methods for calculating pKa values, binding constants of ligands, and changes in protein stability are readily available, but often the limiting step in computational biology is the conversion of PDB structures into formats ready for use with biomolecular simulation software. The continued sophistication and integration of biomolecular simulation methods for systems- and genome-wide studies requires a fast, robust, physically realistic and standardized protocol for preparing macromolecular structures for biophysical algorithms. As described previously, the PDB2PQR web server addresses this need for electrostatic field calculations (Dolinsky et al., Nucleic Acids Research, 32, W665–W667, 2004). Here we report the significantly expanded PDB2PQR that includes the following features: robust standalone command line support, improved pKa estimation via the PROPKA framework, ligand parameterization via PEOE_PB charge methodology, expanded set of force fields and easily incorporated user-defined parameters via XML input files, and improvement of atom addition and optimization code. These features are available through a new web interface (http://pdb2pqr.sourceforge.net/), which offers users a wide range of options for PDB file conversion, modification and parameterization.

Virtual ligand screening: strategies, perspectives and limitations.

In contrast to high-throughput screening, in virtual ligand screening (VS), compounds are selected using computer programs to predict their binding to a target receptor. A key prerequisite is knowledge about the spatial and energetic criteria responsible for protein–ligand binding. The concepts and prerequisites to perform VS are summarized here, and explanations are sought for the enduring limitations of the technology. Target selection, analysis and preparation are discussed, as well as considerations about the compilation of candidate ligand libraries. The tools and strategies of a VS campaign, and the accuracy of scoring and ranking of the results, are also considered.

Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures

Several criteria were defined to select a dataset of high-quality X-ray structures from the Cambridge file resulting in 639 molecules. Six currently available programs for automatic 3D structure generation were compared by converting the connectivity tables including appropriate stereodescriptors from this dataset of 639 molecular structures into 3D geometries: CONCORD, ALCOGEN, Chem-X, MOLGEO, COBRA, and CORINA. The geometries produced by the different programs were evaluated in terms of several quality criteria and are discussed in detail. These criteria measure how well the different programs reproduce the X-ray geometries of the 639 input structures. Accordingly, the major strengths and weaknesses of the programs are indicated.

A New Method to Detect Related Function Among Proteins Independent of Sequence and Fold Homology

A new method has been developed to detect functional relationships among proteins independent of a given sequence or fold homology. It is based on the idea that protein function is intimately related to the recognition and subsequent response to the binding of a substrate or an endogenous ligand in a well-characterized binding pocket. Thus, recognition of similar ligands, supposedly linked to similar function, requires conserved recognition features exposed in terms of common physicochemical interaction properties via the functional groups of the residues flanking a particular binding cavity. Following a technique commonly used in the comparison of small molecule ligands, generic pseudocenters coding for possible interaction properties were assigned for a large sample set of cavities extracted from the entire PDB and stored in the database Cavbase. Using a particular query cavity a series of related cavities of decreasing similarity is detected based on a clique detection algorithm. The detected similarity is ranked according to property-based surface patches shared in common by the different clique solutions. The approach either retrieves protein cavities accommodating the same (e.g. co-factors) or closely related ligands or it extracts proteins exhibiting similar function in terms of a related catalytic mechanism. Finally the new method has strong potential to suggest alternative molecular skeletons in de novo design. The retrieval of molecular building blocks accommodated in a particular sub-pocket that shares similarity with the pocket in a protein studied by drug design can inspire the discovery of novel ligands.

Adding calorimetric data to decision making in lead discovery: a hot tip

Recognition of the limitations of high-throughput screening approaches in the discovery of candidate drugs has reawakened interest in structure-based and other rational design methods. Here, we describe how isothermal titration calorimetry can be used to obtain thermodynamic data on the binding of compounds to protein targets. We propose that these data — particularly the change in enthalpy — could provide a valuable, complementary addition to established tools for selecting compounds in lead discovery and for aiding lead optimization.

Relibase: Design and Development of a Database for Comprehensive Analysis of Protein-Ligand Interactions

Knowledge discovery from the exponentially growing body of structurally characterised protein-ligand complexes as a source of information in structure-based drug design is a major challenge in contemporary drug research. Given the need for powerful data retrieval, integration and analysis tools, Relibase was developed as a database system particularly designed to handle protein-ligand related problems and tasks. Here, we describe the design and functionality of the Relibase core database system. Features of Relibase include, e.g. the detailed analysis of superimposed ligand binding sites, ligand similarity and substructure searches, and 3D searches for protein-ligand and protein-protein interaction patterns. The broad range of functions provided in Relibase and its high level of data integration, along with its flexible and intuitive interface, makes Relibase an invaluable data mining tool which can significantly enhance the drug development process. An example, illustrating a 3D query for quarternary ligand nitrogen atoms interacting with aromatic ring systems in proteins, a pattern found in pharmaceutically relevant target proteins such as, e.g. acetylcholine-esterase, is discussed.

Comparative Molecular Similarity Index Analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries

Comparative molecular field analysis has been applied to a data set of thermolysin inhibitors. Fields expressed in terms of molecular similarity indices (CoMSIA) have been used instead of the usually applied Lennard-Jones- and Coulomb-type potentials (CoMFA). Five different properties, assumed to cover the major contributions responsible for ligand binding, have been considered: steric, electrostatic, hydrophobic, and hydrogen-bond donor or acceptor properties. The statistical evaluation of the field properties by PLS analysis reveals a similar predictive potential to CoMFA. However, significantly improved and easily interpretable contour maps are obtained. The features in these maps intuitively suggest where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity. They can also be interpreted with respect to the known structural protein environment of thermolysin. Most of the highlighted regions in the maps are mirrored by features in the surrounding environment required for binding. Using the derived correlation model, different members of a combinatorial library designed for thermolysin inhibition have been scored for affinity. The results obtained demonstrate the prediction power of the CoMSIA method.

Recent developments in structure-based drug design

Abstract. Structure-based design has emerged as a new tool in medicinal chemistry. A prerequisite for this new approach is an understanding of the principles of molecular recognition in protein-ligand complexes. If the three-dimensional structure of a given protein is known, this information can be directly exploited for the retrieval and design of new ligands. Structure-based ligand design is an iterative approach. First of all, it requires the crystal structure or a model derived from the crystal structure of a closely related homolog of the target protein, preferentially complexed with a ligand. This complex unravels the binding mode and conformation of a ligand under investigation and indicates the essential aspects determining its binding affinity. It is then used to generate new ideas about ways of improving an existing ligand or of developing new alternative bonding skeletons. Computational methods supplemented by molecular graphics are applied to assist this step of hypothesis generation. The features of the protein binding pocket can be translated into queries used for virtual computer screening of large compound libraries or to design novel ligands de novo. These initial proposals must be confirmed experimentally. Subsequently they are optimized toward higher affinity and better selectivity. The latter aspect is of utmost importance in defining and controlling the pharmacological profile of a ligand. A prerequisite to tailoring selectivity by rational design is a detailed understanding of molecular parameters determining selectivity. Taking examples from current drug development programs (HIV proteinase, t-RNA transglycosylase, thymidylate synthase, thrombin and, related serine proteinases), we describe recent advances in lead discovery via computer screening, iterative design, and understanding of selectivity discrimination.

Statistical potentials and scoring functions applied to protein-ligand binding.

In virtual screening, small-molecule ligands are docked into protein binding sites and their binding affinity is predicted. Knowledge-based, regression-based and first-principle-based methods have been developed to rank computer-generated binding modes. As a result of still existing deficiencies, a best compromise might be the combination of several scoring schemes into a consensus scoring approach.

A fast and efficient method to generate biologically relevant conformations.

SummaryMutual binding between a ligand of low molecular weight and its macromolecular receptor demands structural complementarity of both species at the recognition site. To predict binding properties of new molecules before synthesis, information about possible conformations of drug molecules at the active site is required, especially if the 3D structure of the receptor is not known. The statistical analysis of small-molecule crystal data allows one to elucidate conformational preferences of molecular fragments and accordingly to compile libraries of putative ligand conformations. A comparison of geometries adopted by corresponding fragments in ligands bound to proteins shows similar distributions in conformation space. We have developed an automatic procedure that generates different conformers of a given ligand. The entire molecule is decomposed into its individual ring and open-chain torsional fragments, each used in a variety of favorable conformations. The latter ones are produced according to the library information about conformational preferences. During this building process, an extensive energy ranking is applied. Conformers ranked as energetically favorable are subjected to an optimization in torsion angle space. During minimization, unfavorable van der Waals interactions are removed while keeping the open-chain torsion angles as close as possible to the experimentally most frequently observed values. In order to assess how well the generated conformers map conformation space, a comparison with experimental data has been performed. This comparison gives some confidence in the efficiency and completeness of this approach. For some ligands that had been structurally characterized by protein crystallography, the program was used to generate sets of some 10 to 100 conformers. Among these, geometries are found that fall convincingly close to the conformations actually adopted by these ligands at the binding site.