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      Covalent Docking of Large Libraries for the Discovery of Chemical Probes

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          Chemical probes that form a covalent bond with a protein target often show enhanced selectivity, potency, and utility for biological studies. Despite these advantages, protein-reactive compounds are usually avoided in high-throughput screening campaigns. Here we describe a general method (DOCKovalent) for screening large virtual libraries of electrophilic small molecules. We apply this method prospectively to discover reversible covalent fragments that target distinct protein nucleophiles, including the catalytic serine of AmpC β-lactamase and noncatalytic cysteines in RSK2, MSK1, and JAK3 kinases. We identify submicromolar to low-nanomolar hits with high ligand efficiency, cellular activity and selectivity, including the first reported reversible covalent inhibitors of JAK3. Crystal structures of inhibitor complexes with AmpC and RSK2 confirm the docking predictions and guide further optimization. As covalent virtual screening may have broad utility for the rapid discovery of chemical probes, we have made the method freely available through an automated web server ( http://covalent.docking.org).

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          Most cited references 44

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          Automatic processing of rotation diffraction data from crystals of initially unknown symmetry and cell constants

           W Kabsch (1993)
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            Placebo-controlled trial of tofacitinib monotherapy in rheumatoid arthritis.

            Tofacitinib (CP-690,550) is a novel oral Janus kinase inhibitor that is being investigated as a targeted immunomodulator and disease-modifying therapy for rheumatoid arthritis. In this phase 3, double-blind, placebo-controlled, parallel-group, 6-month study, 611 patients were randomly assigned, in a 4:4:1:1 ratio, to 5 mg of tofacitinib twice daily, 10 mg of tofacitinib twice daily, placebo for 3 months followed by 5 mg of tofacitinib twice daily, or placebo for 3 months followed by 10 mg of tofacitinib twice daily. The primary end points, assessed at month 3, were the percentage of patients with at least a 20% improvement in the American College of Rheumatology scale (ACR 20), the change from baseline in Health Assessment Questionnaire-Disability Index (HAQ-DI) scores (which range from 0 to 3, with higher scores indicating greater disability), and the percentage of patients with a Disease Activity Score for 28-joint counts based on the erythrocyte sedimentation rate (DAS28-4[ESR]) of less than 2.6 (with scores ranging from 0 to 9.4 and higher scores indicating more disease activity). At month 3, a higher percentage of patients in the tofacitinib groups than in the placebo groups met the criteria for an ACR 20 response (59.8% in the 5-mg tofacitinib group and 65.7% in the 10-mg tofacitinib group vs. 26.7% in the combined placebo groups, P<0.001 for both comparisons). The reductions from baseline in HAQ-DI scores were greater in the 5-mg and 10-mg tofacitinib groups than in the placebo groups (-0.50 and -0.57 points, respectively, vs. -0.19 points; P<0.001). The percentage of patients with a DAS28-4(ESR) of less than 2.6 was not significantly higher with tofacitinib than with placebo (5.6% and 8.7% in the 5-mg and 10-mg tofacitinib groups, respectively, and 4.4% with placebo; P=0.62 and P=0.10 for the two comparisons). Serious infections developed in six patients who were receiving tofacitinib. Common adverse events were headache and upper respiratory tract infection. Tofacitinib treatment was associated with elevations in low-density lipoprotein cholesterol levels and reductions in neutrophil counts. In patients with active rheumatoid arthritis, tofacitinib monotherapy was associated with reductions in signs and symptoms of rheumatoid arthritis and improvement in physical function. (Funded by Pfizer; ORAL Solo ClinicalTrials.gov number, NCT00814307.).
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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.

                Author and article information

                Nat Chem Biol
                Nat. Chem. Biol.
                Nature chemical biology
                20 September 2014
                26 October 2014
                December 2014
                01 June 2015
                : 10
                : 12
                : 1066-1072
                [1 ]Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
                [2 ]Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, San Francisco, CA 94158, USA
                [3 ]Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA
                [4 ]Faculty of Pharmacy & Ontario Institute for Cancer Research, University of Toronto, Toronto, Canada
                [5 ]Clermont Université, UMR 1071 Inserm/Université d’Auvergne, 63000 Clermont-Ferrand, France
                [6 ]INRA, USC 2018, 63000 Clermont-Ferrand, France
                [7 ]Service de Bactériologie, Centre Hospitalier Universitaire, 63000 Clermont-Ferrand, France
                Author notes
                [§ ]Corresponding authors: bshoichet@ 123456gmail.com (for correspondence relating to the docking method and to covalent inhibition of β-lactamase); jack.taunton@ 123456ucsf.edu (for correspondence relating to covalent inhibition of kinases)

                Equal contribution



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