The elongation cycle of protein synthesis involves the delivery of aminoacyl-tRNAs to the A-site of the ribosome, followed by peptide-bond formation and translocation of the tRNAs through the ribosome to reopen the A-site1,2. The translocation reaction is catalyzed by elongation factor G (EF-G) in a GTP-dependent fashion3. Despite the availability of structures of various EF-G-ribosome complexes, the precise mechanism by which tRNAs move through the ribosome still remains unclear. Here we use multiparticle cryo-EM analysis to resolve two previously unseen subpopulations within EF-G-ribosome complexes at sub-nanometer resolution, one of them with a partially translocated tRNA. Comparison of these sub-states reveals that translocation of tRNA on the 30S subunit parallels the swiveling of the 30S-head and is coupled to un-ratcheting of the 30S-body. Since the tRNA maintains contact with the P-site on the 30S-head and simultaneously establishes interaction with the E-site on the 30S-platform, a novel intra-subunit pe/E hybrid state is formed. This state is stabilized by domain IV of EF-G, which interacts with the swiveled 30S-head conformation. These findings provide direct structural and mechanistic insight into the “missing link” in terms of tRNA intermediates involved in the universally conserved translocation process.

The dynamic nature of biomolecules leads to significant challenges when characterizing the structural properties associated with function. While X-ray crystallography and imaging techniques (such as cryo-electron microscopy) can reveal the structural details of stable molecular complexes, strategies must be developed to characterize configurations that exhibit only marginal stability (such as intermediates) or configurations that do not correspond to minima on the energy landscape (such as transition-state ensembles). Here, we present a methodology (MDfit) that utilizes molecular dynamics simulations to generate configurations of excited states that are consistent with available biophysical and biochemical measurements. To demonstrate the approach, we present a sequence of configurations that are suggested to be associated with transfer RNA (tRNA) movement through the ribosome (translocation). The models were constructed by combining information from X-ray crystallography, cryo-electron microscopy, and biochemical data. These models provide a structural framework for translocation that may be further investigated experimentally and theoretically to determine the precise energetic character of each configuration and the transition dynamics between them. free-energy landscape | modeling transient configurations | molecular machine | tRNA hybrid | translation S tructural biology techniques, including X-ray crystallography and cryo-electron microscopy (cryo-EM), have provided extraordinary insights into the details of the functional configurations of biomolecular machines, such as the ribosome (1-11). These successes have relied on the ability to isolate structurally homogeneous populations of the molecular complexes. Molecular systems undergo continual fluctuations (12). However, when the energetic minimum associated with a particular configuration is sufficiently deep, the structural fluctuations only lead to minor deviations from the average. The structural models provided by these approaches describe the average coordinates inside each basin, and occasionally the structural distribution within a basin may also be characterized (13-16). When the energy landscape contains multiple basins of comparable free energy, one must "trap" the system (5) in a particular configuration, which results in a modified energy landscape (17). This is often true for X-ray crystallographic models, because scattering patterns may only be obtained if the molecules in the crystal are of sufficiently similar configuration and orientation. In this respect, cryo-EM is more flexible because heterogeneities (i.e., molecules that are not trapped in the desired basin) may be computationally removed after data collection (8, 11). These sorting methods allow one to access information about less populated basins of attraction. The smaller number of images in these basins may result in limited resolution; however, several recent studies with ≈10 6 images have produced reconstructions of subpopulations with subnanometer resolution. In principle, it...

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The existing iris recognition methods offer excellent recognition performance for known classes, but they do not consider the rejection of unknown classes. It is important to reject an unknown object class for a reliable iris recognition system. This study proposes open‐set iris recognition based on deep learning. In the method, by training the deep network, the extracted iris features are clustered near the feature centre of each kind of iris image. Then, the authors build an open‐class features outlier network (OCFON) containing distance features, which maps the features extracted by the deep network to a new feature space and classifies them. Finally, the unknown class samples are determined by a SoftMax probability threshold. The authors conducted experiments on the open iris dataset constructed using the iris datasets CASIA‐Iris‐Twins and CASIA‐Iris‐Lamp. The experiment shows that the proposed method has good open‐set iris recognition performance, can effectively distinguish iris samples of unknown classes, and has little impact on the recognition ability of known classes of iris samples.

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