Structural basis for human mitochondrial tRNA maturation.

The human mitochondrial genome is transcribed into two RNAs, containing mRNAs, rRNAs and tRNAs, all dedicated to produce essential proteins of the respiratory chain. The precise excision of tRNAs by the mitochondrial endoribonucleases (mt-RNase), P and Z, releases all RNA species from the two RNA transcripts. The tRNAs then undergo 3'-CCA addition. In metazoan mitochondria, RNase P is a multi-enzyme assembly that comprises the endoribonuclease PRORP and a tRNA methyltransferase subcomplex. The requirement for this tRNA methyltransferase subcomplex for mt-RNase P cleavage activity, as well as the mechanisms of pre-tRNA 3'-cleavage and 3'-CCA addition, are still poorly understood. Here, we report cryo-EM structures that visualise four steps of mitochondrial tRNA maturation: 5' and 3' tRNA-end processing, methylation and 3'-CCA addition, and explain the defined sequential order of the tRNA processing steps. The methyltransferase subcomplex recognises the pre-tRNA in a distinct mode that can support tRNA-end processing and 3'-CCA addition, likely resulting from an evolutionary adaptation of mitochondrial tRNA maturation complexes to the structurally-fragile mitochondrial tRNAs. This subcomplex can also ensure a tRNA-folding quality-control checkpoint before the sequential docking of the maturation enzymes. Altogether, our study provides detailed molecular insight into RNA-transcript processing and tRNA maturation in human mitochondria.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling.

BACKGROUND: Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance. RESULTS: In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set. CONCLUSIONS: GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

Molecular modeling of some commercially available antiviral drugs and their derivatives against SARS-CoV-2 infection.

Numerous prior studies have identified therapeutic targets that could effectively combat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, including the angiotensin-converting enzyme 2 (ACE2) receptor, RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro). In parallel, antiviral compounds like abacavir, acyclovir, adefovir, amantadine, amprenavir, darunavir, didanosine, oseltamivir, penciclovir, and tenofovir are under investigation for their potential in drug repurposing to address this infection. The aim of the study was to determine the effect of modifying the functional groups of the aforementioned antivirals in silico. Using the genetic optimization for ligand docking algorithm on software Maestro (version 11.1), the modified antivirals were docked onto ACE2 receptor, RdRp, and Mpro. Using QuickProp (Maestro v11.1), PASS (prediction of activity spectra for the substances), and altogether with SwissADME, the ADMET (absorption, distribution, metabolism, excretion, and toxicity) of the modified antivirals, as well as their bioavailability and the predicted activity spectra, were determined. Discovery studio software was used to undertake post-docking analysis. Among the 10 antivirals, N(CH3)2 derivative of darunavir, N(CH3)2 derivative of amprenavir and NCH3 derivative of darunavir exhibited best binding affinities with ACE2 receptor (docking scores: -10.333, -9.527 and -9.695 kJ/mol, respectively). Moreover, NCH3 derivative of abacavir (-6.506 kJ/mol), NO2 derivative of didanosine (-6.877 kJ/mol), NCH3 derivative of darunavir (-7.618 kJ/mol) exerted promising affinity to Mpro. In conclusion, the results of the in silico screenings can serve as a useful information for future experimental works.

Apo and Aß46-bound γ-secretase structures provide insights into amyloid-ß processing by the APH-1B isoform.

Deposition of amyloid-ß (Aß) peptides in the brain is a hallmark of Alzheimer's disease. Aßs are generated through sequential proteolysis of the amyloid precursor protein by the γ-secretase complexes (GSECs). Aß peptide length, modulated by the Presenilin (PSEN) and APH-1 subunits of GSEC, is critical for Alzheimer's pathogenesis. Despite high relevance, mechanistic understanding of the proteolysis of Aß, and its modulation by APH-1, remain incomplete. Here, we report cryo-EM structures of human GSEC (PSEN1/APH-1B) reconstituted into lipid nanodiscs in apo form and in complex with the intermediate Aß46 substrate without cross-linking. We find that three non-conserved and structurally divergent APH-1 regions establish contacts with PSEN1, and that substrate-binding induces concerted rearrangements in one of the identified PSEN1/APH-1 interfaces, providing structural basis for APH-1 allosteric-like effects. In addition, the GSEC-Aß46 structure reveals an interaction between Aß46 and loop 1PSEN1, and identifies three other H-bonding interactions that, according to functional validation, are required for substrate recognition and efficient sequential catalysis.

Proton-coupled transport mechanism of the efflux pump NorA.

Efflux pump antiporters confer drug resistance to bacteria by coupling proton import with the expulsion of antibiotics from the cytoplasm. Despite efforts there remains a lack of understanding as to how acid/base chemistry drives drug efflux. Here, we uncover the proton-coupling mechanism of the Staphylococcus aureus efflux pump NorA by elucidating structures in various protonation states of two essential acidic residues using cryo-EM. Protonation of Glu222 and Asp307 within the C-terminal domain stabilized the inward-occluded conformation by forming hydrogen bonds between the acidic residues and a single helix within the N-terminal domain responsible for occluding the substrate binding pocket. Remarkably, deprotonation of both Glu222 and Asp307 is needed to release interdomain tethering interactions, leading to opening of the pocket for antibiotic entry. Hence, the two acidic residues serve as a "belt and suspenders" protection mechanism to prevent simultaneous binding of protons and drug that enforce NorA coupling stoichiometry and confer antibiotic resistance.

Structural insights into the DNA topoisomerase II of the African swine fever virus.

Type II topoisomerases are ubiquitous enzymes that play a pivotal role in modulating the topological configuration of double-stranded DNA. These topoisomerases are required for DNA metabolism and have been extensively studied in both prokaryotic and eukaryotic organisms. However, our understanding of virus-encoded type II topoisomerases remains limited. One intriguing example is the African swine fever virus, which stands as the sole mammalian-infecting virus encoding a type II topoisomerase. In this work, we use several approaches including cryo-EM, X-ray crystallography, and biochemical assays to investigate the structure and function of the African swine fever virus type II topoisomerase, pP1192R. We determine the structures of pP1192R in different conformational states and confirm its enzymatic activity in vitro. Collectively, our results illustrate the basic mechanisms of viral type II topoisomerases, increasing our understanding of these enzymes and presenting a potential avenue for intervention strategies to mitigate the impact of the African swine fever virus.

Structures of co-transcriptional RNA capping enzymes on paused transcription complex.

The 5'-end capping of nascent pre-mRNA represents the initial step in RNA processing, with evidence demonstrating that guanosine addition and 2'-O-ribose methylation occur in tandem with early steps of transcription by RNA polymerase II, especially at the pausing stage. Here, we determine the cryo-EM structures of the paused elongation complex in complex with RNGTT, as well as the paused elongation complex in complex with RNGTT and CMTR1. Our findings show the simultaneous presence of RNGTT and the NELF complex bound to RNA polymerase II. The NELF complex exhibits two conformations, one of which shows a notable rearrangement of NELF-A/D compared to that of the paused elongation complex. Moreover, CMTR1 aligns adjacent to RNGTT on the RNA polymerase II stalk. Our structures indicate that RNGTT and CMTR1 directly bind the paused elongation complex, illuminating the mechanism by which 5'-end capping of pre-mRNA during transcriptional pausing.

Structure and flexibility of the DNA polymerase holoenzyme of vaccinia virus.

The year 2022 was marked by the mpox outbreak caused by the human monkeypox virus (MPXV), which is approximately 98% identical to the vaccinia virus (VACV) at the sequence level with regard to the proteins involved in DNA replication. We present the production in the baculovirus-insect cell system of the VACV DNA polymerase holoenzyme, which consists of the E9 polymerase in combination with its co-factor, the A20-D4 heterodimer. This led to the 3.8 Å cryo-electron microscopy (cryo-EM) structure of the DNA-free form of the holoenzyme. The model of the holoenzyme was constructed from high-resolution structures of the components of the complex and the A20 structure predicted by AlphaFold 2. The structures do not change in the context of the holoenzyme compared to the previously determined crystal and NMR structures, but the E9 thumb domain became disordered. The E9-A20-D4 structure shows the same compact arrangement with D4 folded back on E9 as observed for the recently solved MPXV holoenzyme structures in the presence and the absence of bound DNA. A conserved interface between E9 and D4 is formed by a cluster of hydrophobic residues. Small-angle X-ray scattering data show that other, more open conformations of E9-A20-D4 without the E9-D4 contact exist in solution using the flexibility of two hinge regions in A20. Biolayer interferometry (BLI) showed that the E9-D4 interaction is indeed weak and transient in the absence of DNA although it is very important, as it has not been possible to obtain viable viruses carrying mutations of key residues within the E9-D4 interface.

Molecular mechanisms linking missense ACTG2 mutations to visceral myopathy.

Visceral myopathy is a life-threatening disease characterized by muscle weakness in the bowel, bladder, and uterus. Mutations in smooth muscle γ-actin (ACTG2) are the most common cause of the disease, but the mechanisms by which the mutations alter muscle function are unknown. Here, we examined four prevalent ACTG2 mutations (R40C, R148C, R178C, and R257C) that cause different disease severity and are spread throughout the actin fold. R178C displayed premature degradation, R148C disrupted interactions with actin-binding proteins, R40C inhibited polymerization, and R257C destabilized filaments. Because these mutations are heterozygous, we also analyzed 50/50 mixtures with wild-type (WT) ACTG2. The WT/R40C mixture impaired filament nucleation by leiomodin 1, and WT/R257C produced filaments that were easily fragmented by smooth muscle myosin. Smooth muscle tropomyosin isoform Tpm1.4 partially rescued the defects of R40C and R257C. Cryo-electron microscopy structures of filaments formed by R40C and R257C revealed disrupted intersubunit contacts. The biochemical and structural properties of the mutants correlate with their genotype-specific disease severity.

On the role of native contact cooperativity in protein folding.

The consistency of energy landscape theory predictions with available experimental data, as well as direct evidence from molecular simulations, have shown that protein folding mechanisms are largely determined by the contacts present in the native structure. As expected, native contacts are generally energetically favorable. However, there are usually at least as many energetically favorable nonnative pairs owing to the greater number of possible nonnative interactions. This apparent frustration must therefore be reduced by the greater cooperativity of native interactions. In this work, we analyze the statistics of contacts in the unbiased all-atom folding trajectories obtained by Shaw and coworkers, focusing on the unfolded state. By computing mutual cooperativities between contacts formed in the unfolded state, we show that native contacts form the most cooperative pairs, while cooperativities among nonnative or between native and nonnative contacts are typically much less favorable or even anticooperative. Furthermore, we show that the largest network of cooperative interactions observed in the unfolded state consists mainly of native contacts, suggesting that this set of mutually reinforcing interactions has evolved to stabilize the native state.

RNA Secondary Structure Thermodynamics.

Several different ways to predict RNA secondary structures have been suggested in the literature. Statistical methods, such as those that utilize stochastic context-free grammars (SCFGs), or approaches based on machine learning aim to predict the best representative structure for the underlying ensemble of possible conformations. Their parameters have therefore been trained on larger subsets of well-curated, known secondary structures. Physics-based methods, on the other hand, usually refrain from using optimized parameters. They model secondary structures from loops as individual building blocks which have been assigned a physical property instead: the free energy of the respective loop. Such free energies are either derived from experiments or from mathematical modeling. This rigorous use of physical properties then allows for the application of statistical mechanics to describe the entire state space of RNA secondary structures in terms of equilibrium probabilities. On that basis, and by using efficient algorithms, many more descriptors of the conformational state space of RNA molecules can be derived to investigate and explain the many functions of RNA molecules. Moreover, compared to other methods, physics-based models allow for a much easier extension with other properties that can be measured experimentally. For instance, small molecules or proteins can bind to an RNA and their binding affinity can be assessed experimentally. Under certain conditions, existing RNA secondary structure prediction tools can be used to model this RNA-ligand binding and to eventually shed light on its impact on structure formation and function.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs.

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

RNA Secondary Structure Modeling Following the IPANEMAP Workflow.

The structure of RNA molecules and their complexes are crucial for understanding biology at the molecular level. Resolving these structures holds the key to understanding their manifold structure-mediated functions ranging from regulating gene expression to catalyzing biochemical processes. Predicting RNA secondary structure is a prerequisite and a key step to accurately model their three dimensional structure. Although dedicated modelling software are making fast and significant progresses, predicting an accurate secondary structure from the sequence remains a challenge. Their performance can be significantly improved by the incorporation of experimental RNA structure probing data. Many different chemical and enzymatic probes have been developed; however, only one set of quantitative data can be incorporated as constraints for computer-assisted modelling. IPANEMAP is a recent workflow based on RNAfold that can take into account several quantitative or qualitative data sets to model RNA secondary structure. This chapter details the methods for popular chemical probing (DMS, CMCT, SHAPE-CE, and SHAPE-Map) and the subsequent analysis and structure prediction using IPANEMAP.

Modified Nucleotides and RNA Structure Prediction.

Nucleotide modifications are occurrent in all types of RNA and play an important role in RNA structure formation and stability. Modified bases not only possess the ability to shift the RNA structure ensemble towards desired functional confirmations. By changes in the base pairing partner preference, they may even enlarge or reduce the conformational space, i.e., the number and types of structures the RNA molecule can adopt. However, most methods to predict RNA secondary structure do not provide the means to include the effect of modifications on the result. With the help of a heavily modified transfer RNA (tRNA) molecule, this chapter demonstrates how to include the effect of different base modifications into secondary structure prediction using the ViennaRNA Package. The constructive approach demonstrated here allows for the calculation of minimum free energy structure and suboptimal structures at different levels of modified base support. In particular we, show how to incorporate the isomerization of uridine to pseudouridine ( Ψ ) and the reduction of uridine to dihydrouridine (D).

A Guide to Computational Cotranscriptional Folding Featuring the SRP RNA.

Although RNA molecules are synthesized via transcription, little is known about the general impact of cotranscriptional folding in vivo. We present different computational approaches for the simulation of changing structure ensembles during transcription, including interpretations with respect to experimental data from literature. Specifically, we analyze different mutations of the E. coli SRP RNA, which has been studied comparatively well in previous literature, yet the details of which specific metastable structures form as well as when they form are still under debate. Here, we combine thermodynamic and kinetic, deterministic, and stochastic models with automated and visual inspection of those systems to derive the most likely scenario of which substructures form at which point during transcription. The simulations do not only provide explanations for present experimental observations but also suggest previously unnoticed conformations that may be verified through future experimental studies.

The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline.

Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved. In this chapter, we describe a multiscale approach to predict such structures, incorporating SAXS experimental data into a hierarchical procedure which couples two coarse-grained representations: Ernwin, a helix-based approach, which deals with the global arrangement of secondary structure elements, and SPQR, a nucleotide-centered coarse-grained model, which corrects and refines the structures predicted at the coarser level.We describe the methodology through its application on the Braveheart long noncoding RNA, starting from the SAXS and secondary structure data to propose a refined, all-atom structure.

Installation of LYRM proteins in early eukaryotes to regulate the metabolic capacity of the emerging mitochondrion.

Core mitochondrial processes such as the electron transport chain, protein translation and the formation of Fe-S clusters (ISC) are of prokaryotic origin and were present in the bacterial ancestor of mitochondria. In animal and fungal models, a family of small Leu-Tyr-Arg motif-containing proteins (LYRMs) uniformly regulates the function of mitochondrial complexes involved in these processes. The action of LYRMs is contingent upon their binding to the acylated form of acyl carrier protein (ACP). This study demonstrates that LYRMs are structurally and evolutionarily related proteins characterized by a core triplet of α-helices. Their widespread distribution across eukaryotes suggests that 12 specialized LYRMs were likely present in the last eukaryotic common ancestor to regulate the assembly and folding of the subunits that are conserved in bacteria but that lack LYRM homologues. The secondary reduction of mitochondria to anoxic environments has rendered the function of LYRMs and their interaction with acylated ACP dispensable. Consequently, these findings strongly suggest that early eukaryotes installed LYRMs in aerobic mitochondria as orchestrated switches, essential for regulating core metabolism and ATP production.

Evaluation of DFT methods for predicting geometries and NMR spectra of Bi(III) dithiocarbamate complexes with antitumor properties.

CONTEXT: Bismuth complexes with dithiocarbamate ligands have attracted attention because of their biological applications, such as antimicrobial, antileishmanial, and anticancer properties. These complexes have high cytotoxic activity against cancer cells, being more active than the standard drugs cisplatin, doxorubicin, and tamoxifen. In the present study, we investigated the ability of some DFT methods to reproduce the geometries and NMR spectra of the Bi(III) dithiocarbamate complexes, selected based on their proven antitumor activity. Our investigation revealed that the M06-L/def2-TZVP/ECP/CPCM method presented good accuracy in predicting geometries, while the TPSSh/def2-SVP/ECP/CPCM method proved effective in analyzing the 13C NMR spectra of these molecules. In general, all examined methods exhibited comparable performance in predicting 1H NMR signals. METHODS: Calculations were performed with the Gaussian 09 program using the def2-SVP and def2-TZVP basis sets, employing relativistic effective core potential (ECP) for Bi and using the CPCM solvent model. The exchange-correlation functionals BP86, PBE, OLYP, M06-L, B3LYP, B3LYP-D3, M06-2X, TPSSh, CAM-B3LYP, and ωB97XD were used in the study. Geometry optimizations were started from crystallographic structures available at the Cambridge Structural Database. The theoretical results were compared with experimental data using the mean root-mean-square deviation (RMSD), mean absolute deviations (MAD), and linear correlation coefficient (R2).

Synthesis, Structure and Antibacterial Activity of Two Novel Coordination Polymers Based on N,N'-bis(4-carbozvlbenzvl)-4-aminotoluene and Heterocyclic Ligand against S. aureus.

As the use of antibiotics increases, the increasing resistance of bacteria is the main reason for the reduced efficiency of antibacterial drugs, making the research of new antibacterial materials become new hot spot. In this article, two novel coordination polymers (CPs), namely, [Cd2(L)2(bibp)2]n (1) and [Ni(L)(bib)]n (2), where H2L = N,N'-bis(4-carbozvlbenzvl)-4-aminotoluene, bibp = 4,4'-bis(imidazol-1-yl)biphenyl, and bib = 1,3-bis(1-imidazoly)benzene, have been synthesized under solvothermal and hydrothermal condition. Structural clarification was performed through infrared spectrum and single-crystal X-ray diffraction analysis, while thermal analysis and XRD technology were used for the performance assessment of compounds 1 and 2. In addition, antibacterial performance experiments showed that compounds 1 and 2 have certain selectivity in their antibacterial properties and have good antibacterial properties against S. aureus. As the concentration of the compound increases, the inhibitory effect gradually strengthens, and when the concentration of the compound reaches 500 µg/mL and 400 µg/mL, the concentration of the S. aureus solution no longer increases and has been completely inhibited.

Carbonic Anhydrases: Different Active Sites, Same Metal Selectivity Rules.

Carbonic anhydrases are mononuclear metalloenzymes catalyzing the reversible hydration of carbon dioxide in organisms belonging to all three domains of life. Although the mechanism of the catalytic reaction is similar, different families of carbonic anhydrases do not have a common ancestor nor do they exhibit significant resemblance in the amino acid sequence or the structure and composition of the metal-binding sites. Little is known about the physical principles determining the metal affinity and selectivity of the catalytic centers, and how well the native metal is protected from being dislodged by other metal species from the local environment. Here, we endeavor to shed light on these issues by studying (via a combination of density functional theory calculations and polarizable continuum model computations) the thermodynamic outcome of the competition between the native metal cation and its noncognate competitor in various metal-binding sites. Typical representatives of the competing cations from the cellular environments of the respective classes of carbonic anhydrases are considered. The calculations reveal how the Gibbs energy of the metal competition changes when varying the metal type, structure, composition, and solvent exposure of the active center. Physical principles governing metal competition in different carbonic anhydrase metal-binding sites are delineated.