doi:10.1126/science.aad7701 PMC: PMID:
Single-molecule decoding of combinatorially modified nucleosomes
Efrat Shema1,2, Daniel Jones3, Noam Shoresh2, Laura Donohue1,2, Oren Ram1,2, Bradley E. Bernstein1,2,*
Different combinations of histone modifications have been proposed to signal distinct gene regulatory functions, but this area is poorly addressed by existing technologies. We applied high-throughput single-molecule imaging to decode combinatorial modifications on millions of individual nucleosomes from pluripotent stem cells and lineage-committed cells. We identified definitively bivalent nucleosomes with concomitant repressive and activating marks, as well as other combinatorial modification states whose prevalence varies with developmental potency. We showed that genetic and chemical perturbations of chromatin enzymes preferentially affect nucleosomes harboring specific modification states. Last, we combined this proteomic platform with single-molecule DNA sequencing technology to simultaneously determine the modification states and genomic positions of individual nucleosomes. This single-molecule technology has the potential to address fundamental questions in chromatin biology and epigenetic regulation.
doi:10.1126/science.aad8036 PMC: PMID:
Design of structurally distinct proteins using strategies inspired by evolution
T. M. Jacobs1, B. Williams2, T. Williams2, X. Xu3,4,*, A. Eletsky3,4, J. F. Federizon3, T. Szyperski3, B. Kuhlman2,5,†
Natural recombination combines pieces of preexisting proteins to create new tertiary structures and functions. We describe a computational protocol, called SEWING, which is inspired by this process and builds new proteins from connected or disconnected pieces of existing structures. Helical proteins designed with SEWING contain structural features absent from other de novo designed proteins and, in some cases, remain folded at more than 100°C. High-resolution structures of the designed proteins CA01 and DA05R1 were solved by x-ray crystallography (2.2 angstrom resolution) and nuclear magnetic resonance, respectively, and there was excellent agreement with the design models. This method provides a new strategy to rapidly create large numbers of diverse and designable protein scaffolds.
Nature 533, 73–76 (05 May 2016) doi:10.1038/nature17439
Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia, Katherine C. Elbert, Philip D. F. Adler, Casey Falk, Malia B. Wenny, Aurelio Mollo, Matthias Zeller, Sorelle A. Friedler, Joshua Schrier & Alexander J. Norquist
Inorganic–organic hybrid materials1, 2, 3 such as organically templated metal oxides1, metal–organic frameworks (MOFs)2 and organohalide perovskites4 have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table5, 6, 7, 8, 9. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative10) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility11, photovoltaic properties12, gas adsorption capacity13 or lithium-ion intercalation14) to identify promising target candidates for synthetic efforts11, 15; determination of the structure–property relationship from large bodies of experimental data16, 17, enabled by integration with high-throughput synthesis and measurement tools18; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification19, 20 or gas adsorption properties21). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions—failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.