Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder–decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available athttps://github.com/alncat/opusDSD.

Leiomodin (Lmod) is a class of potent tandem-G-actin–binding nucleators in muscle cells. Lmod mutations, deletion, or instability are linked to lethal nemaline myopathy. However, the lack of high-resolution structures of Lmod nucleators in action severely hampered our understanding of their essential cellular functions. Here we report the crystal structure of the actin–Lmod2_162–495 nucleus. The structure contains two actin subunits connected by one Lmod2_162–495 molecule in a non–filament-like conformation. Complementary functional studies suggest that the binding of Lmod2 stimulates ATP hydrolysis and accelerates actin nucleation and polymerization. The high level of conservation among Lmod proteins in sequence and functions suggests that the mechanistic insights of human Lmod2 uncovered here may aid in a molecular understanding of other Lmod proteins. Furthermore, our structural and mechanistic studies unraveled a previously unrecognized level of regulation in mammalian signal transduction mediated by certain tandem-G-actin–binding nucleators.

In June 2013, the first human infection by avian influenza A(H6N1) virus was reported in Taiwan. This incident raised the concern for possible human epidemics and pandemics from H6 viruses. In this study, we performed structural and functional investigation on the hemagglutinin (HA) proteins of the human-infecting A/Taiwan/2/2013(H6N1) (TW H6) virus and an avian A/chicken/Guangdong/S1311/2010(H6N6) (GD H6) virus that transmitted efficiently in guinea pigs. Our results revealed that in the presence of HA₁ Q226, the triad of HA₁ S137, E190 and G228 in GD H6 HA allows the binding to both avian- and human-like receptors with a slight preference for avian receptors. Its conservation among the majority of H6 HAs provides an explanation for the broader host range of this subtype. Furthermore, the triad of N137, V190 and S228 in TW H6 HA may alleviate the requirement for a hydrophobic residue at HA₁ 226 of H2 and H3 HAs when binding to human-like receptors. Consequently, TW H6 HA has a slight preference for human receptors, thus may represent an intermediate towards a complete human adaptation. Importantly, the triad observed in TW H6 HA is detected in 74% H6 viruses isolated from Taiwan in the past 14 years, suggesting an elevated threat of H6 viruses from this region to human health. The novel roles of the triad at HA₁ 137, 190 and 228 of H6 HA in binding to receptors revealed here may also be used by other HA subtypes to achieve human adaptation, which needs to be further tested in laboratory and closely monitored in field surveillance.

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).