The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol.

Improved protein-ligand docking using GOLD.

Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.

/pdf/molecular-docking-a-powerful-approach-for-structure-based-3scyz9zmai.pdf

Molecular docking: a powerful approach for structure-based drug discovery.

Protein–protein interactions have a key role in most biological processes, and offer attractive opportunities for therapeutic intervention. Developing small molecules that modulate protein–protein interactions is difficult, owing to issues such as the lack of well-defined binding pockets. Nevertheless, there has been important progress in this endeavour in recent years. Here, we use illustrative examples to discuss general strategies for addressing the challenges inherent in the discovery and characterization of small-molecule inhibitors of protein–protein interactions.

Small-molecule inhibitors of protein–protein interactions: progressing towards the dream

An overview is given on the diverse uses of computational chemistry in drug discovery. Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-likeness, and advanced methods for determining protein-ligand binding.

http://chekhov.cs.vt.edu/PAPERS/Jorgensen.DrugDiscovery.pdf

The Many Roles of Computation in Drug Discovery

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community.

/pdf/swissdock-a-protein-small-molecule-docking-web-service-based-4572p3qe0t.pdf

SwissDock, a protein-small molecule docking web service based on EADock DSS

Cyclothialidine and its congeners: A new class of DNA gyrase inhibitors

Substituted 4-quinolone- (1, A = CH) and 1,8-naphthyrid-4-one- (1, A = N) 3-carboxylic acids are currently the only classes of clinically useful antibacterial agents exerting their activity by inhibiting the subunit A of DNA gyrase. Pyrimido[1,6-alpha]benzimidazoles 11 have been found to be a new class of inhibitors of this enzyme. The design, synthesis, and biological activity of these compounds are reported.

Pyrimido(1,6‐a)benzimidazoles: A New Class of DNA Gyrase Inhibitors.

Cyclothialidine (Ro 09-1437) is a potent DNA gyrase inhibitor that was isolated from S. filipinensis NR0484. In this account we describe the identification of potent antibacterials structurally derived from this virtuallyneat enzyme inhibitor.

From the DNA gyrase inhibitor Cyclothialidine to a new class of antibacterial agents

An oligonucleotide array of more than 250,000 specific probes representing two complete bacterial genomes including the 1,743 open reading frames of Haemophilus influenzae, and the 1,969 open reading frames of Streptococcus pneumoniae has been designed. When hybridised against labelled genomic DNA, all genes were detected and more than 96% of the signal intensity values were within a factor of three of the mean. For transcript imaging, microarrays were hybridised against total RNA populations quantitatively represented by labelled cDNAs. The measurements of transcript abundance for all genes were shown to be sensitive, specific, quantitative and reproducible. Nearly 85% of all S. pneumoniae mRNAs were found expressed during in vitro exponential growth. Identification of regulons important for S. pneumoniae entry into the stationary phase has been used to validate the approach. We will show, for the first time, how a genome-wide transcriptional analysis is applied to bacterial genomes and is being used to identify signal transduction regulatory networks, and to profile antibiotic compounds.

/pdf/genome-wide-transcriptional-analysis-of-bacterial-genomes-3y28ebtm7q.pdf

Hans Gmuender

Papers

Cyclothialidine and its congeners: A new class of DNA gyrase inhibitors

Pyrimido(1,6‐a)benzimidazoles: A New Class of DNA Gyrase Inhibitors.

From the DNA gyrase inhibitor Cyclothialidine to a new class of antibacterial agents

Genome-wide transcriptional analysis of bacterial genomes: applications in antibacterial drug discovery

Design, Synthesis, and Structure—Activity Relationship Studies of ATP Analogues as DNA Gyrase Inhibitors.