Plant Prime Editors Enable Precise Gene Editing in Rice Cells

Genome editing is revolutionizing plant research and crop breeding.Sequence-specific nucleases(SSNs)such as zinc finger nuclease(ZFN)and TAL effector nuclease(TALEN)have been used to create site-specific DNA double-strand breaks and to achieve precise DNA modifications by promoting homology-directed repair(HDR)(Steinert et al.,2016;Voytas,2013).Later,RNA-guided SSNs such as CRISPR-Cas9,Cas12a,Cas12b,and their variants were applied for genome editing in plants(Li et al.,2013;Nekrasov et alM 2013;Tang et al.,2017;Zhong et al.,2019;Ming et al.,2020;Tang et al.,2019).However,HDR relies on simultaneous delivery of SSNs and DNA donors,which has been challenging in plants(Steinert et al.,2016;Zhang et aL,2019).Another challenge for realizing efficient HDR in plants is that DNA repair favors nonhomologous end joining(NHEJ)pathways over HDR in most cell types(Puchta,2005;Qi et al.,2013).

Neural network learns physical rules for copolymer translocation through amphiphilic barriers

Recent developments in computer processing power lead to new paradigms of how problems in many-body physics and especially polymer physics can be addressed.Parallel processors can be exploited to generate millions of molecular configurations in complex environments at a second,and concomitant free-energy landscapes can be estimated.Databases that are complete in terms of polymer sequences and architecture form a powerful training basis for cross-checking and verifying machine learning-based models.We employ an exhaustive enumeration of polymer sequence space to benchmark the prediction made by a neural network.In our example,we consider the translocation time of a copolymer through a lipid membrane as a function of its sequence of hydrophilic and hydrophobic units.First,we demonstrate that massively parallel Rosenbluth sampling for all possible sequences of a polymer allows for meaningful dynamic interpretation in terms of the mean first escape times through the membrane.Second,we train a multi-layer neural network on logarithmic translocation times and show by the reduction of the training set to a narrow window of translocation times that the neural network develops an internal representation of the physical rules for sequencecontrolled diffusion barriers.Based on the narrow training set,the network result approximates the order of magnitude of translocation times in a window that is several orders of magnitude wider than the training window.We investigate how prediction accuracy depends on the distance of unexplored sequences from the training window.