In the present investigation, we have identified the functional significance of the highly conserved miR-140 binding site on the Hepatitis E Virus (HEV) genome. Multiple sequence alignment of the viral genome sequences along with RNA folding prediction indicated that the putative miR-140 binding site has significant conservation for sequence and secondary RNA structure among HEV genotypes. Site-directed mutagenesis and reporter assays indicated that an intact sequence of the miR-140 binding site is essential for HEV translation. Provision of mutant miR-140 oligos carrying same mutation as on mutant HEV successfully rescued mutant HEV replication. In vitro cell-based assays with modified oligos proved that host factor-miR-140 is a critical requirement for HEV replication. Biotinylated RNA pulldown and RNA immunoprecipitation assays proved that the predicted secondary RNA structure of the miR-140 binding site allows the recruitment of hnRNP K, which is a key protein of the HEV replication complex. We predicted the model from the obtained results that the miR-140 binding site can serve as a platform for recruitment of hnRNP K and other proteins of HEV replication complex only in the presence of miR-140.

The neuronal SNARE protein SNAP25a (isoform 2) forms part of the SNARE complex eliciting synaptic vesicle fusion during neuronal exocytosis. While the post-fusion cis-SNARE complex has been studied extensively, little is known about the pre-fusion conformation of SNAP25a. Here we analyze monomeric SNAP25a by NMR spectroscopy, further supported by small-angle X-ray scattering (SAXS) experiments. SAXS data indicate that monomeric SNAP25 is more compact than a Gaussian chain but still a random coil. NMR shows that for monomeric SNAP25a, before SNAP25a interacts with its SNARE partners to drive membrane fusion, only the N-terminal part (region A5 to V36) of the first SNARE motif, SN1 (L11 - L81), is helical, comprising two α-helices (ranging from A5 to Q20 and S25 toV36). From E37 onwards, SNAP25a is mostly disordered and displays high internal flexibility, including the C-terminal part of SN1, almost the entire second SNARE motif (SN2, N144-A199), and the connecting loop region. Apart from the N-terminal helices, only the C-termini of both SN1 (E73 - K79) and SN2 (region T190 - A199), as well as two short regions in the connecting loop (D99 - K102 and E123 - M127) show a weak α-helical propensity (α-helical population < 25%). We speculate that the N-terminal helices (A5 to Q20 and S25 to V36) which constitute the N-terminus of SN1 act as a nucleation site for initiating SNARE zippering.

DNA trinucleotide repeat (TRs) expansion beyond a threshold often results in human neurodegenerative diseases. The mechanisms causing expansions remain unknown, although the tendency of TR ssDNA to self-associate into hairpins that slip along their length is widely presumed related. Here we apply single molecule FRET (smFRET) experiments and molecular dynamics simulations to determine conformational stabilities and slipping dynamics for CAG, CTG, GAC and GTC hairpins. Tetraloops are favored in CAG (89%), CTG (89%) and GTC (69%) while GAC favors triloops. We also determined that TTG interrupts near the loop in the CTG hairpin stabilize the hairpin against slipping. The different loop stabilities have implications for intermediate structures that may form when TR-containing duplex DNA opens. Opposing hairpins in the (CAG) ∙ (CTG) duplex would have matched stability whereas opposing hairpins in a (GAC) ∙ (GTC) duplex would have unmatched stability, introducing frustration in the (GAC) ∙ (GTC) opposing hairpins that could encourage their resolution to duplex DNA more rapidly than in (CAG) ∙ (CTG) structures. Given that the CAG and CTG TR can undergo large, disease-related expansion whereas the GAC and GTC sequences do not, these stability differences can inform and constrain models of expansion mechanisms of TR regions.

Monoclonal antibody (mAb)-based biologics are well established treatments of cancer. Antibody discovery campaigns are typically directed at a single target of interest, which inherently limits the possibility of uncovering novel antibody specificities or functionalities. Here, we present a target-unbiased approach for antibody discovery that relies on generating mAbs against native target cell surfaces via phage display. This method combines a previously reported method for improved whole-cell phage display selections with next-generation sequencing analysis to efficiently identify mAbs with the desired target cell reactivity. Applying this method to multiple myeloma cells yielded a panel of >50 mAbs with unique sequences and diverse reactivities. To uncover the identities of the cognate antigens recognized by this panel, representative mAbs from each unique reactivity cluster were used in a multi-omic target deconvolution approach. From this, we identified and validated three cell surface antigens: PTPRG, ICAM1, and CADM1. PTPRG and CADM1 remain largely unstudied in the context of multiple myeloma, which could warrant further investigation into their potential as therapeutic targets. These results highlight the utility of optimized whole-cell phage display selection methods and could motivate further interest in target-unbiased antibody discovery workflows.

Mirogabalin is a novel gabapentinoid drug with a hydrophobic bicyclo substituent on the γ-aminobutyric acid moiety that targets the voltage-gated calcium channel subunit α2δ1. Here, to reveal the mirogabalin recognition mechanisms of α2δ1, we present structures of recombinant human α2δ1 with and without mirogabalin analyzed by cryo-electron microscopy. These structures show the binding of mirogabalin to the previously reported gabapentinoid binding site, which is the extracellular dCache_1 domain containing a conserved amino acid binding motif. A slight conformational change occurs around the residues positioned close to the hydrophobic group of mirogabalin. Mutagenesis binding assays identified that residues in the hydrophobic interaction region, in addition to several amino acid binding motif residues around the amino and carboxyl groups of mirogabalin, are critical for mirogabalin binding. The A215L mutation introduced to decrease the hydrophobic pocket volume predictably suppressed mirogabalin binding and promoted the binding of another ligand, L-Leu, with a smaller hydrophobic substituent than mirogabalin. Alterations of residues in the hydrophobic interaction region of α2δ1 to those of the α2δ2, α2δ3, and α2δ4 isoforms, of which α2δ3 and α2δ4 are gabapentin-insensitive, suppressed the binding of mirogabalin. These results support the importance of hydrophobic interactions in α2δ1 ligand recognition.

Knr4/Smi1 proteins are specific to the fungal kingdom and their deletion in the model yeast Saccharomyces cerevisiae and the human pathogen Candida albicans results in hypersensitivity to specific antifungal agents and a wide range of parietal stresses. In S. cerevisiae, Knr4 is located at the crossroads of several signalling pathways, including the conserved cell wall integrity and calcineurin pathways. Knr4 interacts genetically and physically with several protein members of those pathways. Its sequence suggests that it contains large intrinsically disordered regions. Here, a combination of small-angle X-ray scattering (SAXS) and crystallographic analysis led to a comprehensive structural view of Knr4. This experimental work unambiguously showed that Knr4 comprises two large intrinsically disordered regions flanking a central globular domain whose structure has been established. The structured domain is itself interrupted by a disordered loop. Using the CRISPR/Cas9 genome editing technique, strains expressing KNR4 genes deleted from different domains were constructed. The N-terminal domain and the loop are essential for optimal resistance to cell wall-binding stressors. The C-terminal disordered domain, on the other hand, acts as a negative regulator of this function of Knr4. The identification of molecular recognition features, the possible presence of secondary structure in these disordered domains and the functional importance of the disordered domains revealed here designate these domains as putative interacting spots with partners in either pathway. Targeting these interacting regions is a promising route to the discovery of inhibitory molecules that could increase the susceptibility of pathogens to the antifungals currently in clinical use.

The nuclear pore complex (NPC) is a giant protein assembly that penetrates the double layers of the nuclear membrane. The overall structure of the NPC has approximately eightfold symmetry and is formed by approximately 30 nucleoporins. The great size and complexity of the NPC have hindered the study of its structure for many years until recent breakthroughs were achieved by integrating the latest high-resolution cryo-electron microscopy (cryo-EM), the emerging artificial intelligence-based modeling and all other available structural information from crystallography and mass spectrometry. Here, we review our latest knowledge of the NPC architecture and the history of its structural study from in vitro to in situ with progressively improved resolutions by cryo-EM, with a particular focus on the latest subnanometer-resolution structural studies. The future directions for structural studies of NPCs are also discussed.

Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already allow the recovery of a few distinct conformations from heterogeneous single-particle samples, but the treatment of complex forms of heterogeneity such as the continuum of possible transitory states and flexible regions remains largely an open problem. In recent years there has been a surge of new approaches for treating the more general problem of continuous heterogeneity. This paper surveys the current state of the art in this area.

Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel complexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discovery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessibility are also presented.

Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in different regions of a map. Currently available methods for local resolution estimation require manual adjustment of parameters and in some cases necessitate acquisition or de novo generation of so-called "half maps". Here, we developed CryoRes, a deep-learning algorithm to estimate local resolution directly from a single final cryo-EM density map, specifically by learning resolution-aware patterns of density map voxels through supervised training on a large dataset comprising 1,174 experimental cryo-EM density maps. CryoRes significantly outperforms all of the state-of-the-art competing resolution estimation methods, achieving an average RMSE of 2.26 Å for local resolution estimation relative to the currently most reliable FSC-based method blocres, yet requiring only the single final map as input. Further, CryoRes is able to generate a molecular mask for each map, with accuracy 12.12% higher than the masks generated by ResMap. CryoRes is ultra-fast, fully automatic, parameter-free, applicable to cryo-EM subtomogram data, and freely available at https://cryores.zhanglab.net.

Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å-1, and present the results in a more interpretable form.

One of the main purposes of CryoEM Single Particle Analysis is to reconstruct the three-dimensional structure of a macromolecule thanks to the acquisition of many particle images representing different poses of the sample. By estimating the orientation of each projected particle, it is possible to recover the underlying 3D volume by multiple 3D reconstruction methods, usually working either in Fourier or in real space. However, the reconstruction from the projected images works under the assumption that all particles in the dataset correspond to the same conformation of the macromolecule. Although this requisite holds for some macromolecules, it is not true for flexible specimens, leading to motion-induced artefacts in the reconstructed CryoEM maps. In this work, we introduce a new Algebraic Reconstruction Technique called ZART, which is able to include continuous flexibility information during the reconstruction process to improve local resolution and reduce motion blurring. The conformational changes are modelled through Zernike3D polynomials. Our implementation allows for a multiresolution description of the macromolecule adapting itself to the local resolution of the reconstructed map. In addition, ZART has also proven to be a useful algorithm in cases where flexibility is not so dominant, as it improves the overall aspect of the reconstructed maps by improving their local and global resolution.