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      Solid-Phase Extraction of Pesticides by Using Bioinspired Peptide Receptors

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          Abstract

          A virtual development of hexapeptide receptors bioinspired by the acetylcholinesterase enzyme active site is proposed. A semicombinatorial approach was applied to generate a virtual hexapeptides library with different affinity properties towards organophosphate and carbamate pesticides. The virtual screening process was addressed to obtain peptides able to separate pesticide subclasses in the experimental work. Three hexapeptides, two generated by molecular modeling and one having a scrambled sequence, were used as selective sorbent materials for pesticides in preanalytical solid-phase extraction (SPE) method. Selective adsorption and cross-reactivity were tested directly on a mix of four pesticides (carbaryl, chlorpyrifos-ethyl, malathion, and thiabendazole) having different structures and physico-chemical properties, at a total concentration of 120 ppb (each pesticide at concentration of 30 ppb). The results were compared to traditional sorbent material such as C-18 and strata-X. Data showed that only one of the hexapeptides virtually designed had significant differences in competitive absorption between aliphatic pesticide malathion, fungicide thiabendazole chosen as negative control, and aromatic pesticides. These results partially supported the simulated strategy.

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          Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

          Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.
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            Latest developments in molecular docking: 2010-2011 in review.

            The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine-learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Where appropriate, we refer readers to exemplar case studies. Copyright © 2013 John Wiley & Sons, Ltd.
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              Docking challenge: protein sampling and molecular docking performance.

              Computational tools are essential in the drug design process, especially in order to take advantage of the increasing numbers of solved X-ray and NMR protein-ligand structures. Nowadays, molecular docking methods are routinely used for prediction of protein-ligand interactions and to aid in selecting potent molecules as a part of virtual screening of large databases. The improvements and advances in computational capacity in the past decade have allowed for further developments in molecular docking algorithms to address more complicated aspects such as protein flexibility. The effects of incorporation of active site water molecules and implicit or explicit solvation of the binding site are other relevant issues to be addressed in the docking procedures. Using the right docking algorithm at the right stage of virtual screening is most important. We report a staged study to address the effects of various aspects of protein flexibility and inclusion of active site water molecules on docking effectiveness to retrieve (and to be able to predict) correct ligand poses and to rank docked ligands in relation to their biological activity for CHK1, ERK2, LpxC, and UPA. We generated multiple conformers for the ligand and compared different docking algorithms that use a variety of approaches to protein flexibility, including rigid receptor, soft receptor, flexible side chains, induced fit, and multiple structure algorithms. Docking accuracy varied from 1% to 84%, demonstrating that the choice of method is important.
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                Author and article information

                Journal
                Journal of Chemistry
                Journal of Chemistry
                Hindawi Limited
                2090-9063
                2090-9071
                2015
                2015
                : 2015
                :
                : 1-7
                Article
                10.1155/2015/905701
                0961023c-2e7c-4719-98b1-536d38b06e3f
                © 2015

                http://creativecommons.org/licenses/by/3.0/

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