Three-dimensional Structure Of Complex Research Articles

Bow-shock structure of Sgr-B molecular-cloud complex in the Galactic Centre inferred from 3D CO-line kinematics

ABSTRACT Three-dimensional (3D) bubble structure of the Sgr-B molecular-cloud complex is derived by a kinematical analysis of CO-line archival cube data of the Galactic Centre (GC) observed with the Nobeyama 45-m telescope. The line-of-sight depth is estimated by applying the face-on transformation method of radial velocity to the projected distance on the Galactic plane considering the Galactic rotation of the central molecular zone (CMZ). The 3D complex exhibits a conical-horn structure with the Sgr-B2 cloud located in the farthest end on the line of sight at radial velocity $v_{\rm lsr} \sim 70$ km s$^{-1}$, and the entire complex composes a lopsided bubble opening toward the Sun at $v_{\rm lsr}\sim 50$ to 30 km s$^{-1}$. The line-of-sight extent of the complex is $\sim 100$ pc according to the large velocity extent for several tens of km s$^{-1}$ from Sgr-B2 to the outskirts. The entire complex exhibits a flattened conical bubble with full sizes $\sim 40 \ {\rm pc} \times 20 \ {\rm pc} \times 100 \ {\rm pc}$ in the l, b and line-of-sight directions, respectively. Based on the 3D analysis, we propose a formation scenario of the giant molecular bubble structure due to a galactic bow shock, and suggest that the star formation in Sgr-B2 was enhanced by dual-side compression (DSC) of the B2 cloud by the Galactic shock wave from up-stream and expanding H ii region from the down-stream side of the GC Arm I in Galactic rotation.

Pairing interacting protein sequences using masked language modeling

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.

Structural insights into the cross-exon to cross-intron spliceosome switch

Early spliceosome assembly can occur through an intron-defined pathway, whereby U1 and U2 small nuclear ribonucleoprotein particles (snRNPs) assemble across the intron1. Alternatively, it can occur through an exon-defined pathway2–5, whereby U2 binds the branch site located upstream of the defined exon and U1 snRNP interacts with the 5′ splice site located directly downstream of it. The U4/U6.U5 tri-snRNP subsequently binds to produce a cross-intron (CI) or cross-exon (CE) pre-B complex, which is then converted to the spliceosomal B complex6,7. Exon definition promotes the splicing of upstream introns2,8,9 and plays a key part in alternative splicing regulation10–16. However, the three-dimensional structure of exon-defined spliceosomal complexes and the molecular mechanism of the conversion from a CE-organized to a CI-organized spliceosome, a pre-requisite for splicing catalysis, remain poorly understood. Here cryo-electron microscopy analyses of human CE pre-B complex and B-like complexes reveal extensive structural similarities with their CI counterparts. The results indicate that the CE and CI spliceosome assembly pathways converge already at the pre-B stage. Add-back experiments using purified CE pre-B complexes, coupled with cryo-electron microscopy, elucidate the order of the extensive remodelling events that accompany the formation of B complexes and B-like complexes. The molecular triggers and roles of B-specific proteins in these rearrangements are also identified. We show that CE pre-B complexes can productively bind in trans to a U1 snRNP-bound 5′ splice site. Together, our studies provide new mechanistic insights into the CE to CI switch during spliceosome assembly and its effect on pre-mRNA splice site pairing at this stage.

Native Top-Down Mass Spectrometry for Characterizing Sarcomeric Proteins Directly from Cardiac Tissue Lysate.

Native top-down mass spectrometry (nTDMS) has emerged as a powerful structural biology tool that can localize post-translational modifications (PTMs), explore ligand-binding interactions, and elucidate the three-dimensional structure of proteins and protein complexes in the gas-phase. Fourier-transform ion cyclotron resonance (FTICR) MS offers distinct capabilities for nTDMS, owing to its ultrahigh resolving power, mass accuracy, and robust fragmentation techniques. Previous nTDMS studies using FTICR have mainly been applied to overexpressed recombinant proteins and protein complexes. Here, we report the first nTDMS study that directly analyzes human heart tissue lysate by direct infusion FTICR MS without prior chromatographic separation strategies. We have achieved comprehensive nTDMS characterization of cardiac contractile proteins that play critical roles in heart contraction and relaxation. Specifically, our results reveal structural insights into ventricular myosin light chain 2 (MLC-2v), ventricular myosin light chain 1 (MLC-1v), and alpha-tropomyosin (α-Tpm) in the sarcomere, the basic contractile unit of cardiac muscle. Furthermore, we verified the calcium (Ca2+) binding domain in MLC-2v. In summary, our nTDMS platform extends the application of FTICR MS to directly characterize the structure, PTMs, and metal-binding of endogenous proteins from heart tissue lysate without prior separation methods.

Multi-task bioassay pre-training for protein-ligand binding affinity prediction.

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

Local defocus estimation in single particle analysis in cryo-electron microscopy

Single Particle analysis (SPA) aims to determine the three-dimensional structure of proteins and macromolecular complexes. The current state of the art has allowed us to achieve near-atomic and even atomic resolutions. To obtain high-resolution structures, a set of well-defined image processing steps is required. A critical one is the estimation of the Contrast Transfer Function (CTF), which considers the sample defocus and aberrations of the microscope. Defocus is usually globally estimated; in this case, it is the same for all the particles in each micrograph. But proteins are ice-embedded at different heights, suggesting that defocus should be measured in a local (per particle) manner. There are four state-of-the-art programs to estimate local defocus (Gctf, Relion, CryoSPARC, and Xmipp). In this work, we have compared the results of these software packages to check whether the resolution improves. We have used the Scipion framework and developed a specific program to analyze local defocus. The results produced by different programs do not show a clear consensus using the current test datasets in this study.

Magnetotellurics unveils a hidden caldera complex beneath the Cappadocia Volcanic Province, Central Anatolia, Türkiye

The Neogene-Quaternary volcanism in central Anatolia is characterized by ignimbrite sequences, and associated calderas have been partly dismantled and buried as a result of tectonic, volcanic, erosional and depositional processes, hindering the identification of these structures from surface expressions. To search the location of a concealed caldera complex, one of the probable ignimbrite source vents, magnetotelluric data acquired at 60 stations in the period range from 0.001 s to 1000 s were used to derive an upper crustal three-dimensional electrical resistivity model in the Cappadocian Volcanic Province, central Anatolia, Türkiye. The electrical resistivity model provides constraints on the location, geometry and boundaries of a hidden caldera complex situated in the Çiftlik basin, north of Mt. Melendiz accurately, as well as its morphology at depth, which is characterized by a large depression with low resistivity values beneath the high resistivity layer consisting of pyroclastic flow deposits. This area spatially coinciding with Neogene collapse structures is interpreted as a buried caldera complex with a maximum diameter of ~12 km and depth of −3 km (below sea level) that is formed by partially overlapped nested caldera structures of subsequent volcanic eruptions and attributed to the probable source area for the Miocene-Pliocene ignimbrite emplacements. The collapse and burying process of the caldera complex have been linked with the transtensional tectonics of the Cappadocia region. Besides, the structural margins are inactive and probably bounded by faults buried under younger volcanic products within the Cappadocian Volcanic Province. The results present major implications for a better understanding of the deep three-dimensional structure of hidden caldera complexes, of which little is known, and also for describing the relationship between hidden calderas and regional tectonics.

MDTOMO method for continuous conformational variability analysis in cryo electron subtomograms based on molecular dynamics simulations

Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.

AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation.

We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.

The PANDORA Software for Anchor-Restrained Peptide:MHC Modeling.

Major histocompatibility complexes (MHC) play a key role in the immune surveillance system in all jawed vertebrates. MHC class I molecules randomly sample cytosolic peptides from inside the cell, while MHC class II sample exogenous peptides. Both types of peptide:MHC complex are then presented on the cell surface for recognition by αβ T cells (CD8+ and CD4+, respectively). The three-dimensional structure of such complexes can give crucial insights in the presentation and recognition mechanisms. For this reason, softwares like PANDORA have been developed to rapidly and accurately generate peptide:MHC (pMHC) 3D structures. In this chapter, we describe the protocol of PANDORA. PANDORA exploits the structural knowledge on anchor pockets that MHC molecules use to dock peptides. PANDORA provides anchor positions as restraints to guide the modeling process. This allows PANDORA to generate twenty 3D models in just about 5min. PANDORA is highly customizable, easy to install, supports parallel processing, and is suitable to provide large datasets for deep learning algorithms.

R-SIM: A Database of Binding Affinities for RNA-small Molecule Interactions

Ribonucleic acids (RNAs) are involved in a multitude of crucial cellular functions by acting as a central conduit for information transfer. Due to their essential and versatile functional roles in the cell, RNAs have also been implicated in multiple disease conditions of therapeutic relevance including cancers, bacterial and viral infections and neurodegenerative disorders. Recently, several approaches have emerged to tap into the potentially unexplored regions of the druggable genome, which refers to the genes and gene products that are focused during drug development. For example, considering RNAs as viable alternative therapeutic targets for drug development can potentially expand the range of therapeutic targets. Consequently, the availability of adequate binding affinity measurements for RNA-small molecule interactions is essential to understand target selectivity and design more potent RNA-targeting drug-like molecules. To facilitate this growing need, we have curated a database of experimentally validated RNA-small molecule interactions, called RNA-Small molecule Interaction Miner (R-SIM). Each entry in R-SIM provides comprehensive information on sequence, structure and classification of the RNA target, various physicochemical properties of the small molecule, binding affinity value and corresponding experimental conditions, three-dimensional structure (experimental or modelled) of the RNA-small molecule complex, and the literature source for the data. It also provides a user-friendly web interface with several options for search, display, sorting, visualization, download and upload of the data. R-SIM is freely available at: https://web.iitm.ac.in/bioinfo2/R_SIM/index.html. We envisage that R-SIM has several potential applications in understanding and accelerating the development of novel RNA-targeted small molecule therapeutics.

The three-dimensional structure of galactic molecular cloud complexes out to 2.5 kpc

ABSTRACT Knowledge of the three-dimensional structure of Galactic molecular clouds is important for understanding how clouds are affected by processes such as turbulence and magnetic fields and how this structure affects star formation within them. Great progress has been made in this field with the arrival of the Gaia mission, which provides accurate distances to ∼109 stars. Combining these distances with extinctions inferred from optical–infrared, we recover the three-dimensional structure of 16 Galactic molecular cloud complexes at ∼1 pc resolution using our novel three-dimensional dust mapping algorithm dustribution. Using astrodendro, we derive a catalogue of physical parameters for each complex. We recover structures with aspect ratios between 1 and 11, i.e. everything from near-spherical to very elongated shapes. We find a large variation in cloud environments that is not apparent when studying them in two dimensions. For example, the nearby California and Orion A clouds look similar on sky, but we find California to be more sheet-like, and massive, which could explain their different star formation rates. By calculating the total mass of these individual clouds, we demonstrate that it is necessary to define cloud boundaries in three dimensions in order to obtain an accurate mass; simply integrating the extinction overestimates masses. We find that Larson’s relationship on mass versus radius holds true whether you assume a spherical shape for the cloud or take their true extents.

Two transition complexes based on 1H-benzimidazole-5,6-dicarboxylic acid: Synthesis, structure and photocatalytic degradation of dyes

Metal-organic frameworks [Co(Hbidc)(H2O)2] (1) and [Mn(Hbidc)(H2O)] (2), with multidentate 1H-benzimidazole-5,6-dicarboxylic acid (H3bidc) ligand, have been synthesized under hydro/solvothermal conditions and structurally characterized by elemental analysis, IR spectrum, and single-crystal X-ray diffraction. Single-crystal X-ray diffraction analysis revealed that the center Co atom of complex 1 is six-coordinated with three-dimensional supramolecular structure and center Mn of complex 2 is five-coordinated with exhibiting a 2D layered network. The photodegradation of Crystal violet dye and Methylene blue dye were studied firstly by complexes 1 and 2 as photocatalysts. Research result indicates that the degradation rate for complex 1 can reach 89.85% , 90.6% and that for complex 2 can reach 88.28% , 79.48%. At the same time, corresponding to photocatalytic kinetics was performed.

Structural insights into global mutations in the ligand-binding domain of VAR2CSA and its implications on placental malaria vaccine

Placental malaria is a public health burden particularly in Africa as it causes severe symptoms and results in stillbirths or maternal deaths. Plasmodium falciparum protein VAR2CSA drives placental malaria (PM) in pregnant women by adhering to chondroitin sulfate A (CSA) on the placenta. VAR2CSA is a primary vaccine candidate for PM with two vaccines based on it already under clinical trials. The first cryo-EM three-dimensional structure of Pf CSA-VAR2CSA complex revealed crucial interacting residues considered to be highly conserved across P. falciparum strains. In the current study, we have conducted a global sequence analysis of 1,114 VAR2CSA field isolate sequences from more than nine countries across three continents revealing numerous mutations in CSA-binding residues. Further, structural mapping has revealed significant polymorphisms on the ligand binding surfaces. The variants from this limited set of 1,114 sequences highlight the concerns that are vital in current considerations for development of vaccines based on VAR2CSA for placental malaria.

Profile of Patrick Cramer

Patrick Cramer’s interest in the scientific method may have been sparked by a childhood gift. While growing up, his parents gave him a kit to try little experiments at home that inspired him to become a scientist. Patrick Cramer. Image credit: Patrick Cramer. Cramer studied chemistry, first at the University of Stuttgart and later at Heidelberg University in Germany. “I was in love with the beauty of biological molecules, so I moved into biochemistry and then into structural biology,” says Cramer. This move was facilitated by stints at the University of Bristol and Cambridge University. “At Cambridge I really fell in love with structural biology, and so I chose that as a subject for my PhD studies at the European Molecular Biology Laboratory,” he says. At the European Molecular Biology Laboratory in Grenoble, France, Cramer began to study gene transcription, launching a career unraveling the mysteries of this fundamental biological process. Cramer has determined the three-dimensional (3D) structure of transcription complexes, providing numerous insights into the mechanisms of transcription initiation, elongation, and regulation. He has also contributed to the development of functional genomics and computational biology to study how the genome is transcribed and regulated in living cells and understand how gene activity is controlled. Now director at the Max Planck Institute for Biophysical Chemistry in Gottingen, Germany, Cramer was elected to the National Academy of Sciences in 2020. In his Inaugural Article (1), his team describes the first structures of the highly conserved transcription factor TATA-box binding protein (TBP) in complex with a nucleosome. When Cramer received his doctorate in 1998, a major question about transcription was the structure of RNA polymerase II, the central enzyme that makes messenger RNA. To work on this problem, he joined Roger Kornberg’s laboratory at Stanford University as a postdoctoral researcher, funded by …

Shared Binding Mode of Perrottetinene and Tetrahydrocannabinol Diastereomers inside the CB1 Receptor May Incentivize Novel Medicinal Drug Design: Findings from an in Silico Assay.

In recent years, therapeutic compounds derived from phytocannabinoids have brought renewed attention to the benefits they offer to ameliorate chronic disease symptoms. Among cannabinoids, tetrahydrocannabinol (THC) is a well-known component of the Cannabis plant, whose active principles have been studied through the years. Another psychoactive phytocannabinoid, derived from liverworts Radula, perrottetinene (PET), has created interest, especially as a pharmaceutical product and for its legal recreational use. Unfortunately, so far, the interaction mode of these compounds at the type 1 cannabinoid receptors (CB1R) binding site remains unknown, and no experimental three-dimensional structure in complex with THC or PET is available in the Protein Data Bank. Today, many computational methodologies can assist in this crusade and help unveil how these molecules bind, based on the already known pose of a structurally similar compound. In this work, we aim to elucidate the binding mode of THC and PET molecules in both cis and trans conformers, using a combination of several computational methodologies, including molecular docking, molecular dynamics, free energy calculations, and protein-energy network studies. We found that THC and PET interact similarly with the CB1R, in a different conformation depending on the considered diastereomer. We have observed that cis ligands adopted a half-chair conformation of the cycle ring containing the dimethyl group, assuming an axial or equatorial conformation producing a different induced fitting of the surrounding residues compared with trans ligands, with higher interaction energy than the trans conformer. For PET, we have seen that Trp-279 and Trp-356 have a marked influence on the binding. After binding, Trp-279 accommodates its side chain to better interact with the PET's terminal phenyl group, disturbing CB1R residues communication. The interaction with Trp-356 might impair the activation of CB1R and can influence the binding of PET as a partial agonist. Understanding the PET association with CB1R from a molecular perspective can offer a glimpse of preventing potential toxicological or recreational effects since it is an attractive lead for drug development with fewer side effects than trans-THC.

The synthesis of heteroleptic cyanometallate aminopyridine complexes and an investigation into their structural properties with various spectroscopic methods

In this study, three new transition metal(II) complexes consisting of zinc(II), mercury(II) atoms, cyanide group (CN), 3-aminopyridine (3AP) and 4-aminopyridine (4AP) ligand molecules were successfully synthesized in a single crystal form. The formulas of these newly synthesized heteroleptic cyanometalate aminopyridines complexes are given as [Zn(II)(3AP)2(CN)2], [Zn(II)(4AP)2(CN)2] and [Hg(II)(4AP)2(CN)2]. The crystal structures of these heteroleptic cyanometalate complexes were characterized by spectral analysis including single crystal X-ray diffraction techniques, FT-IR, and thermal analysis. The FT-IR and single crystal XRD spectra of these complexes were recorded in the 3750–250 cm−1 wavenumber range and in the region of about 3–29°, respectively. In addition, the thermal analysis curves of these complexes were recorded in the temperature range of 40–850 ℃ using TG, DTG, and DSC methods. The structural properties of these complexes are explained with reference to the changes in their characteristic peaks belonging to 3AP, 4AP ligand molecules and cyanide group in their FT-IR spectra. The most important feature of these new complexes is the presence of NH···N interactions between the H atom of the NH2 group of the 3AP and 4AP ligand molecules and the nitrogen atom of the CN group. These interactions play the most important role in the realization of the three-dimensional structure of the new heteroleptic cyanometalate complexes in crystal form. According to the spectral data of the newly obtained heteroleptic cyanometalate complexes, the complex with 3AP has the orthorhombic crystal system and the space group Pca21, whereas the other two heteroleptic cyanometalate complexes with 4AP have in the monoclinic crystal system and the C2/c space group. The asymmetric unit of [Zn(II)(3AP)2(CN)2] comprises one Zn(II) ion, two cyano groups, and two 3AP ligand molecules; the asymmetric unit of [Zn(II)(4AP)2(CN)2] comprises one Zn(II) ion, one cyano group and one 4AP ligand molecule. Similarly, the asymmetric unit of [Hg(II)(4AP)2(CN)2] comprises one Hg(II) ion, one cyano group and one 4AP ligand molecule.

Multiplicity spin, structure, and charge of iron-verdohemeoxygenase complex: A comparison study by the DFT method

Recently the three-dimensional structure of verdoheme heme oxygenase complex was revealed. However, many parameters of verdoheme heme oxygenase’s complex structure and their role and function on Heme degradation were unknown. In this work the structure of iron verdoheme in complex with heme oxygenase was compared by the density functional theory (DFT)-based B3LYP method using the 6-31G basis set. Many parameters such as charge of verdoheme and iron as central metal, electron distribution, spin multiplicity of the molecule and proximal substituents effects on verdoheme ring stabilization and their arrangement are discussed and compared for twelve different conformations of the molecules to find the most energetically stable states.

Three-dimensional structure of a string-fluid complex plasma

Three-dimensional structure of complex (dusty) plasmas was investigated under long-term microgravity conditions in the International-Space-Station-based Plasmakristall-4 facility. The microparticle suspensions were confined in a polarity-switched dc discharge. The experimental results were compared to the results of the molecular dynamics simulations with the interparticle interaction potential represented as a superposition of isotropic Yukawa and anisotropic quadrupole terms. Both simulated and experimental data exhibited qualitatively similar structural features indicating the bulk liquid-like order with the inclusion of solid-like strings aligned with the axial electric field. Individual strings were identified and their size spectrum was calculated. The decay rate of the size spectrum was found to decrease with the enhancement of string-like structural features.

Simulation of the Effect of Point Mutations on the Stability of Protein Dimers Using the Bcl-2 Protein Family as an Example

A new method has been developed that enables synthesizing peptides with desired physicochemical properties, for example, with high affinity to one target protein and low affinity to other proteins, and determining the stability of protein complexes with point substitutions of amino acid residues taking into account the three-dimensional structure of the complex by the example of the Bcl2 family, thereby achieving targeted therapeutic selectivity in the treatment of various pathologies.