N[6]-threonylcarbamoyladenosine (t[6]A) at tRNA position 37 is essential for translational fidelity and cellular homeostasis. The multi-subunit KEOPS complex catalyzes t[6]A formation in Archaea and Eukarya. Here we present cryo-EM structures of C. elegans KEOPS in its apo and tRNA-bound states. tRNA binding induces concerted conformational rearrangements, distorting the anticodon loop to project A37 into the Kae1 active site. Kae1 recognizes the conserved G10-C25 pair and 36-UAA-38 motif. Bud32 directly contacts the anticodon and acceptor arms, coupling ATP hydrolysis to t[6]A catalysis in the distant Kae1 active site through long-range conformational changes. Cgi121 enhances catalytic efficiency through cooperative binding with Bud32 and the tRNA 3' CCA. Pcc1 stabilizes the anticodon loop and mediates KEOPS dimerization, enhancing tRNA binding and t[6]A activity. GAMOS-associated mutations cluster at functional hotspots within the KEOPS-tRNA complex. This study provides a structural framework for understanding KEOPS mechanism and its cellular roles.

Orthobunyaviruses are segmented negative-sense RNA viruses that encompass several life-threatening human and animal pathogens. However, no licensed antivirals are currently available. The viral RNA-dependent RNA polymerase (RdRp) is a multi-domain enzyme critical for genome replication and transcription, representing a promising target for antiviral drug development. Here, we establish robust in vitro enzymatic activity assays for the Ebinur Lake virus (EBIV) polymerase and identify suramin, a century-old drug, as an inhibitor of EBIV polymerase. We further determine cryo-EM structures of the EBIV polymerase in apo, elongation, and suramin-bound states. These structures reveal conformational rearrangements of the polymerase during RNA synthesis, including conformational transitions of the prime-and-realign (PR) loop and a unique β-hairpin that bridges the zinc-binding domain (ZBD) to the RdRp core. The structural observations are correlated with in vitro enzymatic activity and cell-based minireplicon assays. The suramin-bound structure reveals two distinct inhibitory binding sites. One site sterically clashes with the vRNA promoter. The other site directly blocks RNA template strand binding, thereby inhibiting polymerase activity. These findings advance our understanding of orthobunyavirus RNA synthesis mechanisms and offer a structural framework for the rational design and optimization of antiviral drugs.

Vesicular stomatitis virus (VSV) is a promising anticancer agent but rapidly loses its infectivity in human serum. Here, we demonstrate that low-density and very low-density lipoproteins (LDLs and VLDLs) compete with VSV-G-displaying vectors for occupancy of cellular LDL receptors (LDLRs). Infectivity of VSV-G-displaying vectors was reduced two- to three-logs in the presence of heat-inactivated human serum but not if the serum was depleted of lipoproteins. The inhibitory effect was replicated in serum-free media supplemented with physiological concentrations of purified lipoprotein particles that contain the LDLR-binding protein Apo B-100, and the degree of inhibition correlated with the concentration of LDL or VLDL. When VSV-G was retargeted to the epidermal growth factor receptor, competitive inhibition by human serum or by LDL/VLDL was no longer observed. Similar results were obtained when VSV-G was retargeted to other receptors. Our findings demonstrate that serum LDL and VLDL competitively block the entry of VSV-G-displaying viral particles, but this barrier for in vivo delivery can be circumvented through a display of a receptor-targeting ligand on VSV-G.

Nucleus-encoded proteins are involved in intron splicing in plant chloroplasts. Although many splicing factors were discovered, the mechanisms for chloroplast intron splicing remain unknown. Here, we identified a P-class pentatricopeptide repeat (PPR) protein, PHOTOSYSTEM ONE BIOGENESIS FACTOR6 (PBF6), that is essential for the accumulation of photosystem I complex. PBF6 bound to ycf3 intron 1 and clpP1 intron 2, and the introns of petB, ndhA, and ndhB transcripts, and was required for their splicing. Truncated PBF6 containing the 10 N-terminal PPR motifs bound to specific sequences to ycf3 intron 1, clpP1 intron 2, and to the introns of petB, ndhA, and ndhB. PBF6 formed two multi-subunit splicing complexes with other known splicing factors from the CRM, peptidyl-tRNA hydrolase, RNase III, PORR, APO, DEAD-box RNA helicase, and mTERF families. PBF6-containing complex I was about 600 kDa, comprising nine known splicing factors: CAF1, CAF2, CRS2, CFM3a, RNC1, WTF1, APO2, RH3, and mTERF2. PBF6-containing complex II was about 300 kDa and contained three known splicing factors: CAF1, CAF2, and CFM2. Furthermore, two known PPR-type splicing factors, PBF2 and ECD2, both of which are required for splicing the ycf3 intron 1, also formed two multi-subunit splicing complexes with other known splicing factors. Importantly, these three PPR-type splicing factors formed their splicing complexes independently. Our data suggest that a PPR-type splicing factor forms splicing complexes with other known splicing factors under transient expression conditions to facilitate intron splicing and that several PPR-type splicing factors work together to promote the splicing of the same intron through forming respective splicing complexes.

Structure-based virtual screening (SBVS) is a widely used approach in in silico drug discovery, requiring a receptor structure or binding site to predict a ligand's binding pose and affinity. Consequently, the performance of SBVS depends on the protein's conformation. The most common SBVS method is protein-ligand docking, which relies on physics-based, empirical, or knowledge-based scoring functions to estimate binding affinity. However, these methods are highly sensitive to structural variations and conformational changes of the receptor structure. The performance of SBVS often drops substantially when the apo form of the receptor used for screening is different from its holo form. To address this issue, we previously introduced an SBVS method called PL-PatchSurfer. This method uses a surface representation to describe a binding site of receptors and drug molecules, which is more robust to structural changes, offering more stable screening performance. The surface of the molecules is segmented into patches, and their shapes and physicochemical properties, such as the electrostatic potential, are represented by the 3D Zernike descriptor, a compact and rotationally invariant mathematical representation. Here, we introduce PL-PatchSurfer3, an improved version of PL-PatchSurfer, which incorporates two key enhancements: a refined definition of hydrogen bond complementarity and the integration of visibility, which captures curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while maintaining its robustness against receptor structure variations.Scientific contributionThis study presents PL-PatchSurfer3, an improved surface patch-based virtual screening method that incorporates molecular surface shape, physicochemical properties, hydrogen-bonding compatibility, and visibility, capturing local curvature using rotationally invariant 3D Zernike descriptors. PL-PatchSurfer3 demonstrates robust performance across holo, apo, modeled, and AlphaFold-predicted receptor structures and is superior to or competitive with conventional approaches and recent deep learning-based methods.

Parkinson's disease (PD), the second most common age-related neurodegenerative disorder worldwide, is associated with mutations in several genes, one of which is LRRK2 (leucine-rich repeat kinase 2). Despite cryo-EM structures elucidating the active and inactive architectures of the wild type (WT), the regulatory mechanisms and intermediate conformational states (INTs) that facilitate the transition between these forms, as well as the distinctions in the G2019S mutant, remain unclear. Here, we elucidated how mutations reshape the conformational dynamics through molecular dynamics simulations, highlighting the salt bridge contributions of the E1920-R2026 and K1906-D2017 residue pairs and fluctuations of the activation loop (A-loop) lid in stabilizing specific states. Furthermore, the reconstructed two-dimensional Well-Tempered Metadynamics (2D WT-MetaD) free-energy landscape along A-loop and K1906-E1920 coordinates revealed two distinct activation/inactivation pathways. Interestingly, the inactivation pathway in WT revealed a metastable "semi-open" state, while the G2019S mutation favored activation by decreasing the inactive-to-active energy barrier; however, once the active state was formed, it became comparatively stable and required more energy for the active-to-inactive transition than the WT. Thus, the G2019S mutation facilitated activation and remained in its active form, providing a mechanistic basis for its uncontrolled kinase hyperactivity. To reinforce the MetaD-derived stability of the G2019S active conformation over WT, we assessed the binding of the type-I inhibitor LRRK2-IN-1 for active state conformational rearrangements in the ligand-bound state. The absolute binding free-energy consistently showed that the ligand preferentially stabilizes the mutant active state, highlighting its binding affinity and modestly increased inhibitor potency, relative to WT. Finally, two-dimensional Umbrella Sampling (2D-US) showed apo-state transitions arising from DYGI (D2017-Y2018-G2019-I2020) rearrangements and αC-helix coupling via COM distances of K1906-E1920 and E1920-R2026 residue pairs. The results revealed that the mutant (G2019S) follows two routes with four INTs.

This chapter aims to provide the reader with an overview of how to leverage computational algorithms toward improving the affinity of an antibody for a particular target. In most cases, structural information is required; however, the prerequisite information is not always available. Here, we take a bottom-up approach that will guide readers through scenarios where data such as antibody apo or complex structures are unknown. The focus of the chapter is on how computational tools and workflows can be used to address these limitations when attempting to identify an affinity-matured antibody variant with desirable properties. We begin with an overview of antibody structure prediction, followed by predictions of antibody-antigen complexes, antibody affinity, stability, and developability. Finally, we conclude this chapter with guidance notes and best practices for antibody computational design.

The cystic fibrosis transmembrane conductance regulator (CFTR) is an ATP-gated anion channel whose function is tightly linked to its conformational dynamics and is influenced by the composition of its membrane lipid environment. Despite high-resolution three-dimensional (3D) structures, the molecular determinants that stabilize specific CFTR conformations and enable ion conduction remain incompletely understood. Here, we performed all-atom molecular dynamics (MD) simulations of the human CFTR 3D structure in both the apo and VX-770 (ivacaftor)-bound states, embedded in a heterogeneous lipid bilayer, in order to systematically analyze electrostatic interactions, linking amino acids to each other as well as to anions and membrane lipids. We identified 557 electrostatic interactions between charged and polar amino acid side chains, which we systematically mapped across the CFTR 3D structure. They are organized into specific regions, with a subset showing high frequency and conservation across simulations, suggesting a structural role in stabilizing CFTR architecture. In contrast, more transient electrostatic interactions were detected in dynamic regions potentially linked to conformational transitions or other functional roles. Irregularities in transmembrane (TM) helices often incorporate amino acids involved in electrostatic interactions. Many basic and polar residues involved in electrostatic interactions also engaged in anion coordination, underscoring their contribution to ion conduction. In addition, some showed selective interactions with cholesterol and phosphatidylserine, revealing spatially organized lipid binding, particularly at the level of the lasso and in the vicinity of the VX-770 binding site, which may mark regions important for allosteric communication. VX-770 binding preserved the global architecture of the electrostatic interaction networks but induced subtle shifts, acting on specific salt bridges. Regardless of whether VX-770 is present or not, a secondary portal displayed between TM10/TM12 emerged from these MD simulations, in addition to the main TM4/TM6 portal, whose morphology and diameter are controlled by a fluctuating salt bridge. Two exit routes also appeared for the exit of anions toward the extracellular milieu. Altogether, our integrative analysis highlights how dynamic electrostatic networks, together with ion and lipid interactions, support CFTR's structural plasticity and functional modulation, offering molecular insights into potentiation mechanisms and into the specific evolution of CFTR in the ABC transporter superfamily.

Allostery plays a critical role in protein dynamics and is essential for many biological functions. Over the past decade, various computational approaches have been proposed for predicting allosteric sites. However, the strengths and weaknesses of each method are not well understood. In this study, we created two independent datasets that had not been used in selected computational protocols: a CAPASP-General subset comprising holo state allosteric proteins and a CAPASP-Unbound subset comprising apo state allosteric proteins. We then systematically evaluated the accuracy of five allosteric site prediction tools across five dimensions: sensitivity, specificity, F1-score, MCC value and ranking capability. The results indicated that the machine learning models PASSer and APOP, which are based on protein physicochemical properties, not only achieved the highest success rate in sensitivity prediction but also lead in average F1-score and MCC value. However, these models performed better with the CAPASP-General subset than with the CAPASP-Unbound subset, suggesting that the prediction models require further improvement. These findings could facilitate the selection of appropriate prediction models for different allosteric proteins and enhance our understanding of protein function and regulatory mechanisms.