Institution
Scripps Research Institute
Nonprofit•San Diego, California, United States•
About: Scripps Research Institute is a nonprofit organization based out in San Diego, California, United States. It is known for research contribution in the topics: Receptor & Antigen. The organization has 16250 authors who have published 32843 publications receiving 2906516 citations.
Topics: Receptor, Antigen, Signal transduction, Virus, Immune system
Papers published on a yearly basis
Papers
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TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.
Abstract: AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.
20,059 citations
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TL;DR: AutoDock4 incorporates limited flexibility in the receptor and its utility in analysis of covalently bound ligands is reported, using both a grid‐based docking method and a modification of the flexible sidechain technique.
Abstract: We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.
15,616 citations
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Harvard University1, Brigham and Women's Hospital2, University of Wisconsin-Madison3, University of California, Berkeley4, Technical University of Denmark5, Icahn School of Medicine at Mount Sinai6, Vienna University of Technology7, University of Erlangen-Nuremberg8, German Cancer Research Center9, University of Milan10, Johns Hopkins University11, University of Washington12, Scripps Research Institute13, Walter and Eliza Hall Institute of Medical Research14, University of Iowa15
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Abstract: The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
12,142 citations
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10,370 citations
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TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
Abstract: A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual's phenotype are reverse transcribed into its genotype and become . heritable traits sic . We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein)ligand test systems having known three-dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein)ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard y1 y1 .
9,322 citations
Authors
Showing all 16336 results
Name | H-index | Papers | Citations |
---|---|---|---|
Richard A. Flavell | 231 | 1328 | 205119 |
Irving L. Weissman | 201 | 1141 | 172504 |
Ronald M. Evans | 199 | 708 | 166722 |
Eric J. Topol | 193 | 1373 | 151025 |
Douglas R. Green | 182 | 661 | 145944 |
John R. Yates | 177 | 1036 | 129029 |
Tony Hunter | 175 | 593 | 124726 |
George F. Koob | 171 | 935 | 112521 |
Eliezer Masliah | 170 | 982 | 127818 |
Mark Gerstein | 168 | 751 | 149578 |
Michel C. Nussenzweig | 165 | 516 | 87665 |
Donald E. Ingber | 164 | 610 | 100682 |
Dennis R. Burton | 164 | 683 | 90959 |
Chad A. Mirkin | 164 | 1078 | 134254 |
Ian A. Wilson | 158 | 971 | 98221 |