How 'Protein-Docking' Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Evaluation of the novel endophytic fungus Chaetomium ascotrichoides 1-24-2 from Pinus massoniana as a biocontrol agent against pine wilt disease caused by Bursaphelenchus xylophilus

BACKGROUND

Bursaphelenchus xylophilus, the causative agent of pine wilt disease (PWD), is an ever-increasing threat to Pinus forests worldwide. This study aimed to develop biological control of PWD by the application of endophytic fungi isolated from healthy pine trees.

RESULTS

We successfully isolated a novel endophytic fungal strain 1-24-2 from branches of healthy Pinus massoniana. The culture filtrates (CFs) of strain 1-24-2 exhibited strong nematicidal activity against Bursaphelenchus xylophilus, with a corrected mortality rate of 99.00%. Based on the morphological and molecular characteristics, the isolated strain 1-24-2 was identified as Chaetomium ascotrichoides. In the in-planta assay, pine seedlings (2-years-old) treated with 1-24-2 CFs + pine wood nematode (T2) showed a significant control effect of 80%. A total of 24 toxic compounds were first identified from 1-24-2 CFs through gas chromatography-mass spectrometry (GC-MS) analysis, from which O-methylisourea, 2-chlorobenzothiazole, and 4,5,6-trihydroxy-7-methylphthalide showed robust binding sites at Tyr119 against phosphoethanolamine methyltransferase (PMT) protein of Bursaphelenchus xylophilus by molecular docking approach and could be used as potential compounds for developing effective nematicides. Interestingly, strain 1-24-2 produces toxic volatile organic compounds (VOCs), which disturb the natural development process of B. xylophilus, whose total number decreased by up to 83.32% in the treatment group as compared to control and also reduced Botrytis cinerea growth by up to 71.01%.

CONCLUSION

Our results highlight the potential of C. ascotrichoides 1-24-2 as a promising biocontrol agent with solid nematicidal activity against B. xylophilus. This is the first report of C. ascotrichoides isolated from P. massoniana exhibiting strong biocontrol potential against B. xylophilus in the world. © 2024 Society of Chemical Industry.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Functionalized siRNA-chitosan nanoformulations promote triple-negative breast cancer cell death via blocking the miRNA-21/AKT/ERK signaling axis: in-silico and in vitro studies

Oncogenic microRNA (miRNA), especially miRNA-21 upregulation in triple-negative breast cancer (TNBC), suggests a new class of therapeutic targets. In this study, we aimed to create GE11 peptide-conjugated small interfering RNA-loaded chitosan nanoparticles (GE11-siRNA-CSNPs) for the targeting of EGFR overexpressed TNBC and selectively inhibit miRNA-21 expression. A variety of in-silico and in vitro cellular and molecular studies were conducted to investigate the binding affinities of specific targets used as well as the anticancer efficacies and mechanisms of GE11-siRNA-CSNPs in TNBC cells. An in-silico assessment reveals a distinct binding affinity of miRNA-21 with siRNA as well as between the extracellular domain of EGFR and synthesized peptides. Notably, the in vitro results showed that GE11-siRNA-CSNPs were revealed to have better cytotoxicity against TNBC cells. It significantly inhibits miRNA-21 expression, cell migration, and colony formation. The results also indicated that GE11-siRNA-CSNPs impeded cell cycle progression. It induces cell death by reducing the expression of the antiapoptotic gene Bcl-2 and increasing the expression of the proapoptotic genes Bax, Caspase 3, and Caspase 9. Additionally, the docking analysis and immunoblot investigations verified that GE1-siRNA-CSNPs, which specifically target TNBC cells and suppress miRNA-21, can prevent the effects of miRNA-21 on the proliferation of TNBC cells via controlling EGFR and subsequently inhibiting the PI3K/AKT and ERK1/2 signaling axis. The GE11-siRNA-CSNPs design, which specifically targets TNBC cells, offers a novel approach for the treatment of breast cancer with improved effectiveness. This study suggests that GE11-siRNA-CSNPs could be a promising candidate for further assessment as an additional strategy in the treatment of TNBC.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.

HIV-1 Transcription Inhibition Using Small RNA-Binding Molecules

The HIV-1 transactivator protein Tat interacts with the transactivation response element (TAR) at the three-nucleotide UCU bulge to facilitate the recruitment of transcription elongation factor-b (P-TEFb) and induce the transcription of the integrated proviral genome. Therefore, the Tat-TAR interaction, unique to the virus, is a promising target for developing antiviral therapeutics. Currently, there are no FDA-approved drugs against HIV-1 transcription, suggesting the need to develop novel inhibitors that specifically target HIV-1 transcription. We have identified potential candidates that effectively inhibit viral transcription in myeloid and T cells without apparent toxicity. Among these candidates, two molecules showed inhibition of viral protein expression. A molecular docking and simulation approach was used to determine the binding dynamics of these small molecules on TAR RNA in the presence of the P-TEFb complex, which was further validated by a biotinylated RNA pulldown assay. Furthermore, we examined the effect of these molecules on transcription factors, including the SWI/SNF complex (BAF or PBAF), which plays an important role in chromatin remodeling near the transcription start site and hence regulates virus transcription. The top candidates showed significant viral transcription inhibition in primary cells infected with HIV-1 (98.6). Collectively, our study identified potential transcription inhibitors that can potentially complement existing cART drugs to address the current therapeutic gap in current regimens. Additionally, shifting of the TAR RNA loop towards Cyclin T1 upon molecule binding during molecular simulation studies suggested that targeting the TAR loop and Tat-binding UCU bulge together should be an essential feature of TAR-binding molecules/inhibitors to achieve complete viral transcription inhibition.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.

In silico selection of aptamers for bacterial toxins detection

The most commonly used toxins in biological warfare are staphylococcal enterotoxin B (3SEB), cholera toxin (1XTC), and botulinum toxin (3BTA). Uncovering novel strategies for identifying these toxins is paramount; therefore, aptamers are used for this purpose. Aptamers are single-stranded DNA or RNA oligonucleotides selected via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high binding affinity and specificity against target molecules. However, SELEX in vitro is tedious; hence, adopting alternative in silico molecular docking approaches is necessary. We aimed to conduct molecular docking with accessible tools and obtain RNA aptamers. First, 4,820,095 sequences obtained from an initial library of 9.5 × 109 Python script sequences were used. The GraphClust program was used to create representative groups or clusters, and the DoGSiteScorer (https://proteins.plus/) was used to conduct binding site detection of the proteins: 5DO4 (thrombin), 3SEB, 1XTC, and 3BTA. rDock, HDock, and PatchDock were adopted, combining different docking program results (consensus scoring), to improve receptor-ligand prediction. An analysis of the poses and root mean square deviation (RMSD) was performed, and 468 structurally different aptamers were obtained. The DoGSiteScorer program predicted the binding site of each protein to direct the interaction with the aptamer. Candidate aptamers for 3SEB, 1XTC, and 3BTA were selected according to the pose value considering the closeness of the interaction with a lower mean of 45.923 Å, 45.854 Å, and 72.490 Å, respectively.Communicated by Ramaswamy H. Sarma.

Exploring potential targets of HPV&BC based on network pharmacology and urine proteomics

BACKGROUND

Bladder cancer (BC) caused by Human papillomavirus (HPV) infection remains a complex public health problem in developing countries. Although the HPV vaccine effectively prevents HPV infection, it does not benefit patients with BC who already have HPV.

METHODS

Firstly, the differential genes of HPV-related BC patients were screened by transcriptomics, and then the prognostic and clinical characteristics of the differential genes were analyzed to screen out the valuable protein signatures. Furthermore, the compound components and targets of Astragali Radix (AR) were analyzed by network pharmacology, and the intersection targets of drug components and HPV_BC were screened out for pathway analysis. In addition, the binding ability of the compound to the Astragali-HPV_BC target was verified by molecular docking and virtual simulation. Finally, to identify potential targets in BC patients through urine proteomics and in vitro experiments.

RESULTS

Eleven HPV_BC-related protein signatures were screened out, among which high expression of EGFR, CTNNB1, MYC, GSTM1, MMP9, CXCR4, NOTCH1, JUN, CXCL12, and KRT14 had a poor prognosis, while low expression of CASP3 had a poor prognosis. In the analysis of clinical characteristics, it was found that high-risk scores, EGFR, MMP9, CXCR4, JUN, and CXCL12 tended to have higher T stage, pathological stage, and grade. Pharmacological and molecular docking analysis identified a natural component of AR (Quercetin) and it corresponding core targets (EGFR). The OB of the natural component was 46.43, and the DL was 0.28, respectively. In addition, EGFR-Quercetin has high affinity. Urine proteomics and RT-PCR showed that EGFR was expressed explicitly in BC patients. Mechanism analysis revealed that AR component targets might affect HPV_BC patients through Proteoglycans in the cancer pathway.

CONCLUSION

AR can target EGFR through its active component (Quercetin), and has a therapeutic effect on HPV_BC patients.

Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents

Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal–ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes’ root-mean-square deviation (RMSD) and the program’s internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket’s high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative’s tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.

Mutation pattern of cancer suppressor p53 gene in factory workers exposed to polypropylene and impact on their reproductive hormones

BACKGROUND

Polypropylene is a thermoplastic polymer playing the role of an endocrine disruptor that interferes with the union, emission, transport or elimination of normal hormones. Epidemiological information indicated the relation of endocrine-disturbing chemicals with prostate cancer, testis tumor and diminished fertility. p53 is a key tumor silencer gene. The present study aimed to evaluate luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels and the risk of p53 mutations as a result of exposure to polypropylene in non-tumorous adult male factory workers.

METHODS

In total, 150 (controls = 35, workers = 115) subjects were recruited. Groups were maintained according to the tenure of exposure G1 (1-5 years), G2 (6-10 years), G3 (11-15 years) and G4 (16-20 years). Concentrations of LH and FSH were determined through an enzyme-linked immunosorbent assay. Genotyping analysis was performed by polymerase chain reaction based gel electrophoresis followed by DNA sequencing. The structural and functional impact of the mutation on the p53 structure was evaluated using 50-ns molecular dynamics (MD) simulations and protein-DNA docking.

RESULTS

Mean plasma LH levels were significantly decreased in G1 (p > 0.05) as well as the G2, G3 and G4 (p > 0.001) groups. Similarly, FSH levels were significant decrease in G1 (p > 0.05), G2 (p > 0.01), G3 (p > 0.001) and G4 (p > 0.001) compared to the control group. Sequencing results found three variants i.e. g.13450 T>G, g.13430C>T and g.13737G>A. One of them was predicted to be disease-causing others are polymorphisms. MD simulation of missense mutation R273H showed no structural impact on the protein structure in MD simulation, but it resulted in weaker binding of p53 with the DNA that might lower the gene expression of cell cycle regulatory proteins.

CONCLUSIONS

These findings predict decreased fertility and risk of malignancies in the future. The spectrum of p53 mutations as a result of polypropylene exposure in the Pakistani population has not been investigated before. Further studies and meta-analyses are required to elucidate the role of different plasticizers in reproduction and cancer-causing risk factors in a larger population.

Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases

The incidence rate of nervous system diseases has increased in recent years. Nerve injury or neurodegenerative diseases usually cause neuronal loss and neuronal circuit damage, which seriously affect motor nerve and autonomic nervous function. Therefore, safe and effective treatment is needed. As traditional drug research becomes slower and more expensive, it is vital to enlist the help of cutting- edge technology. Virtual screening (VS) is an attractive option for the identification and development of promising new compounds with high efficiency and low cost. With the assistance of computer- aided drug design (CADD), VS is becoming more and more popular in new drug development and research. In recent years, it has become a reality to transform non-neuronal cells into functional neurons through small molecular compounds, which provides a broader application prospect than transcription factor-mediated neuronal reprogramming. This review mainly summarizes related theory and technology of VS and the drug research and development using VS technology in nervous system diseases in recent years, and focuses more on the potential application of VS technology in neuronal reprogramming, thus facilitating new drug design for both prevention and treatment of nervous system diseases.

Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ1-Riboswitch

Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ₁-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Role and Perspective of Molecular Simulation-Based Investigation of RNA-Ligand Interaction: From Small Molecules and Peptides to Photoswitchable RNA Binding

Aberrant RNA–protein complexes are formed in a variety of diseases. Identifying the ligands that interfere with their formation is a valuable therapeutic strategy. Molecular simulation, validated against experimental data, has recently emerged as a powerful tool to predict both the pose and energetics of such ligands. Thus, the use of molecular simulation may provide insight into aberrant molecular interactions in diseases and, from a drug design perspective, may allow for the employment of less wet lab resources than traditional in vitro compound screening approaches. With regard to basic research questions, molecular simulation can support the understanding of the exact molecular interaction and binding mode. Here, we focus on examples targeting RNA–protein complexes in neurodegenerative diseases and viral infections. These examples illustrate that the strategy is rather general and could be applied to different pharmacologically relevant approaches. We close this study by outlining one of these approaches, namely the light-controllable association of small molecules with RNA, as an emerging approach in RNA-targeting therapy.