Fast automated placement of polar hydrogen atoms in protein-ligand complexes

<p>Abstract</p> <p>Background</p> <p>Hydrogen bonds play a major role in the stabilization of protein-ligand complexes. The ability of a functional group to form them depends on the position of its hydrogen atoms. An accurate knowledge of the positions of hydrogen atoms in proteins is therefore important to correctly identify hydrogen bonds and their properties. The high mobility of hydrogen atoms introduces several degrees of freedom: Tautomeric states, where a hydrogen atom alters its binding partner, torsional changes where the position of the hydrogen atom is rotated around the last heavy-atom bond in a residue, and protonation states, where the number of hydrogen atoms at a functional group may change. Also, side-chain flips in glutamine and asparagine and histidine residues, which are common crystallographic ambiguities must be identified before structure-based calculations can be conducted.</p> <p>Results</p> <p>We have implemented a method to determine the most probable hydrogen atom positions in a given protein-ligand complex. Optimality of hydrogen bond geometries is determined by an empirical scoring function which is used in molecular docking. This allows to evaluate protein-ligand interactions with an established model. Also, our method allows to resolve common crystallographic ambiguities such as as flipped amide groups and histidine residues. To ensure high speed, we make use of a dynamic programming approach.</p> <p>Conclusion</p> <p>Our results were checked against selected high-resolution structures from an external dataset, for which the positions of the hydrogen atoms have been validated manually. The quality of our results is comparable to that of other programs, with the advantage of being fast enough to be applied on-the-fly for interactive usage or during score evaluation.</p

Food Policy Development in the Australian State of Victoria: A Case Study of the Food Alliance

This article explores the development of a food policy body called the Food Alliance and the role of the organization in encouraging the development of food policy that integrates health and ecological issues. The Food Alliance is located within the Australian state of Victoria. A policy triangle is used as a framework to describe and analyse the work of the Food Alliance. Lessons are drawn about effective strategies for influencing integrated food policy. This occurs in a context where food policy typically favours powerful industry and agricultural interests and where relationships between the health and environmental sectors are in their infancy. The implications for planning and organizing a state-wide food policy are explored from the perspective of policy and the ways in which this can be influenced through working with key stakeholders

Discovery of a new class of inhibitors for the protein arginine deiminase type 4 (PAD4) by structure-based virtual screening

<p>Abstract</p> <p>Background</p> <p>Rheumatoid arthritis (RA) is an autoimmune disease with unknown etiology. Anticitrullinated protein autoantibody has been documented as a highly specific autoantibody associated with RA. Protein arginine deiminase type 4 (PAD4) is the enzyme responsible for catalyzing the conversion of peptidylarginine into peptidylcitrulline. PAD4 is a new therapeutic target for RA treatment. In order to search for inhibitors of PAD4, structure-based virtual screening was performed using LIDAEUS (Ligand discovery at Edinburgh university). Potential inhibitors were screened experimentally by inhibition assays.</p> <p>Results</p> <p>Twenty two of the top-ranked water-soluble compounds were selected for inhibitory screening against PAD4. Three compounds showed significant inhibition of PAD4 and their IC₅₀values were investigated. The structures of the three compounds show no resemblance with previously discovered PAD4 inhibitors, nor with existing drugs for RA treatment.</p> <p>Conclusion</p> <p>Three compounds were discovered as potential inhibitors of PAD4 by virtual screening. The compounds are commercially available and can be used as scaffolds to design more potent inhibitors against PAD4.</p

Insights into Ligand–Protein Binding from Local Mechanical Response

Computational studies of ligand–protein binding are crucial for properly designing novel compounds of potential pharmacological interest. In this respect, researchers are increasingly interested in steered molecular dynamics for ligand–protein binding and unbinding studies. In particular, it has been suggested that analyzing the work profiles along the ligand–protein undocking paths could be fruitful. Here, we propose that small portions of work profiles, termed “local mechanical responses” of the system to a steering force, could serve as a universal measure for capturing relevant information about the system under investigation. Specifically, we first collected a high number of steering trajectories using two biological systems of increasing complexity (i.e., alanine dipeptide and (R)-roscovitine/CDK5 complex). Then, we devised a novel postprocessing tool to be applied to the local mechanical responses, to extract structural information related to the biological processes under investigation. Despite the out-of-equilibrium character of the trajectories, the analysis carried out on the work profiles provided pivotal information about the investigated biological processes. This could eventually be applied to drug design

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government

A virtual screening approach for identifying plants with anti H5N1 neuraminidase activity

Recent outbreaks of highly pathogenic and occasional drug-resistant influenza strains have highlighted the need to develop novel anti-influenza therapeutics. Here, we report computational and experimental efforts to identify influenza neuraminidase inhibitors from among the 3000 natural compounds in the Malaysian-Plants Natural-Product (NADI) database. These 3000 compounds were first docked into the neuraminidase active site. The five plants with the largest number of top predicted ligands were selected for experimental evaluation. Twelve specific compounds isolated from these five plants were shown to inhibit neuraminidase, including two compounds with IC50 values less than 92 μM. Furthermore, four of the 12 isolated compounds had also been identified in the top 100 compounds from the virtual screen. Together, these results suggest an effective new approach for identifying bioactive plant species that will further the identification of new pharmacologically active compounds from diverse natural-product resources