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      The structural basis for cancer drug interactions with the catalytic and allosteric sites of SAMHD1

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          REFMAC5 dictionary: organization of prior chemical knowledge and guidelines for its use.

          One of the most important aspects of macromolecular structure refinement is the use of prior chemical knowledge. Bond lengths, bond angles and other chemical properties are used in restrained refinement as subsidiary conditions. This contribution describes the organization and some aspects of the use of the flexible and human/machine-readable dictionary of prior chemical knowledge used by the maximum-likelihood macromolecular-refinement program REFMAC5. The dictionary stores information about monomers which represent the constitutive building blocks of biological macromolecules (amino acids, nucleic acids and saccharides) and about numerous organic/inorganic compounds commonly found in macromolecular crystallography. It also describes the modifications the building blocks undergo as a result of chemical reactions and the links required for polymer formation. More than 2000 monomer entries, 100 modification entries and 200 link entries are currently available. Algorithms and tools for updating and adding new entries to the dictionary have also been developed and are presented here. In many cases, the REFMAC5 dictionary allows entirely automatic generation of restraints within REFMAC5 refinement runs.
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            Advances in the development of nucleoside and nucleotide analogues for cancer and viral diseases.

            Nucleoside analogues have been in clinical use for almost 50 years and have become cornerstones of treatment for patients with cancer or viral infections. The approval of several additional drugs over the past decade demonstrates that this family still possesses strong potential. Here, we review new nucleoside analogues and associated compounds that are currently in preclinical or clinical development for the treatment of cancer and viral infections, and that aim to provide increased response rates and reduced side effects. We also highlight the different approaches used in the development of these drugs and the potential of personalized therapy.
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              Macromolecular size-and-shape distributions by sedimentation velocity analytical ultracentrifugation.

              Sedimentation velocity analytical ultracentrifugation is an important tool in the characterization of macromolecules and nanoparticles in solution. The sedimentation coefficient distribution c(s) of Lamm equation solutions is based on the approximation of a single, weight-average frictional coefficient of all particles, determined from the experimental data, which scales the diffusion coefficient to the sedimentation coefficient consistent with the traditional s approximately M(2/3) power law. It provides a high hydrodynamic resolution, where diffusional broadening of the sedimentation boundaries is deconvoluted from the sedimentation coefficient distribution. The approximation of a single weight-average frictional ratio is favored by several experimental factors, and usually gives good results for chemically not too dissimilar macromolecules, such as mixtures of folded proteins. In this communication, we examine an extension to a two-dimensional distribution of sedimentation coefficient and frictional ratio, c(s,f(r)), which is representative of a more general set of size-and-shape distributions, including mass-Stokes radius distributions, c(M,R(S)), and sedimentation coefficient-molar mass distributions c(s,M). We show that this can be used to determine average molar masses of macromolecules and characterize macromolecular distributions, without the approximation of any scaling relationship between hydrodynamic and thermodynamic parameters.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                October 23 2018
                October 23 2018
                October 23 2018
                October 10 2018
                : 115
                : 43
                : E10022-E10031
                Article
                10.1073/pnas.1805593115
                6205433
                30305425
                85fc1c42-d3c0-48a3-b69b-e5edd923bb75
                © 2018
                History

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