RNA-Ligand Interactions Quantified by Surface Plasmon Resonance with Reference Subtraction

Molecular surfaces modeling: Advancements in deep learning for molecular interactions and predictions

Molecular surface analysis can provide a high-dimensional, rich representation of molecular properties and interactions, which is crucial for enabling powerful predictive modeling and rational molecular design across diverse scientific and technological domains. 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 accelerating molecular discovery and innovation. The integration of AI techniques with molecular surface analysis has opened up new frontiers, allowing researchers to uncover hidden patterns, relationships, and design principles that were previously elusive. By leveraging the complementary strengths of molecular surface representations and advanced AI algorithms, scientists can now explore chemical space more efficiently, optimize molecular properties with greater precision, and drive transformative advancements in areas like drug development, materials engineering, and catalysis. In this review, we aim to provide an overview of recent advancements in the field of molecular surface analysis and its integration with AI techniques. These AI-driven approaches have led to significant advancements in various downstream tasks, including interface site prediction, protein-protein interaction prediction, surface-centric molecular generation and design.

A 36-base hairpin within lncRNA DRAIC, which is modulated by alternative splicing, interacts with the IKKα coiled-coil domain and inhibits NF-κB and tumor cell phenotypes

A tumor-suppressive long noncoding RNA (lncRNA) DRAIC (down-regulated RNA in cancers) inhibits NF-κB activity and physically interacts with IKKα, a kinase component of the IKK complex, in several cancer types. Here we explore the precise molecular mechanism involved in this interaction and suppression. Using SHAPE-MaP, we identified a 36-nucleotide hairpin (A+B) within DRAIC that is necessary and sufficient for its anti-oncogenic function. RNA immunoprecipitation (RIP) and Electrophoretic mobility shift assays (EMSA) confirmed this hairpin physically interacts with the coiled coil domain of IKKα. A+B RNA has a high binding affinity (KD ∼1-7 nM) to the coiled-coil domain of IKKα. The binding of A+B disrupts the dimerization of NEMO and IKKα coiled-coil domains, a critical step for IKK action. Consistent with this, A+B inhibits the phosphorylation of the NF-κB inhibitor IκBα and suppresses NF-κB activity. Publicly available tumor RNAseq data revealed that alternative splicing modulates the presence of this critical hairpin: the inclusion of exon 4a (encoding one side of the A+B hairpin) in lung tumors correlates with reduced NF-κB activity. By demonstrating that the A+B hairpin is both necessary and sufficient to inhibit IKK and oncogenic phenotypes, this study underscores the centrality of IKKα interaction and NF-κB inhibition in DRAIC-mediated cancer suppression and indicates that the activity of this lncRNA is regulated by alternative splicing. This study also reveals the first example of a short RNA disrupting coiled-coil dimerization, offering a new approach to disrupt such dimerization in cancer biology.

Advancing target validation with PROTAC technology

INTRODUCTION

Targeted protein degradation (TPD) is a cutting-edge technology that provides new avenues for drug discovery and development. PROteolysis TArgeting Chimeras (PROTACs) are the most established and advanced TPD strategy, enabling the selective degradation of disease-associated and 'undruggable' proteins of interest (POIs) by leveraging the cell's natural protein degradation machinery. To confirm that PROTAC-induced proximity drives protein degradation, target validation and ternary complex formation must be thoroughly assessed.

AREAS COVERED

In this perspective, the authors detail some of the most widely used in silico, structural, in vitro, and in cellulo methods to validate PROTAC target engagement and ternary complex formation. Additionally, they discuss the growing use of PROTACs as chemical probes for novel target identification and validation.

EXPERT OPINION

Target validation is essential in the PROTAC approach, and ongoing studies should prioritize confirming ternary complex formation using assays conducted under physiologically relevant cellular conditions. Proteomics analyses are among the most valuable tools for elucidating PROTAC mechanisms, selectivity, and outcomes. The authors are optimistic about the future of PROTACs in drug development and their use as probes to confirm target engagement. PROTAC technology holds vast opportunities for future exploration, offering significant potential to further both chemical and biological research.

Everything's under Control: Maximizing Biosensor Performance through Negative Control Probe Selection

The rapid rise of label-free biosensing technologies has led to multiple creative strategies for the detection of macromolecules in complex biological solutions for disease state monitoring, drug discovery, and basic science research. A challenge with these techniques is that assays conducted in complex media such as serum suffer from nonspecific binding of matrix constituents. In label-free biosensors, it is virtually impossible to distinguish these nonspecific interactions without the use of a reference (negative control) probe. Only with reference subtraction can the specific binding signal be faithfully reported. To date, this has been a sparsely studied area in the biosensing field. Here, we report an FDA-inspired framework for optimum control probe selection and a systematic analysis for determining the optimal negative control probe given two monoclonal antibody capture probes on photonic ring resonator sensors. Briefly, while the differences in assay performance for IL-17A and CRP were found to be subtle, the best-scoring reference control based on the bioanalytical parameters of linearity, accuracy, and selectivity differed for each analyte. In the IL-17A assay, BSA scored the highest at 83%, while mouse IgG1 isotype control antibody placed a close second with 75%. With respect to the CRP assay, the rat IgG1 isotype control antibody scored the highest at 95%, while anti-FITC scored the second highest at 89%. These results suggest that although isotype-matching to the capture antibody may be tempting, the best on-chip reference control must be optimized on a case-by-case basis using the framework we report.

A 36-base hairpin within lncRNA DRAIC , modulated by alternative splicing, interacts with the IKKα coiled-coil domain and inhibits NF-κB and tumor cell phenotypes

The 1.7 kb DRAIC long noncoding RNA inhibits tumor growth, inhibits cancer cell invasion, migration, colony formation and interacts with IKK (IκB kinase) subunits, inhibiting the phosphorylation and degradation of the NF-κB inhibitor, IκB, to suppress the activation of NF-κB. Whether these activities are all linked is unclear. We used SHAPE-MaP to obtain the secondary structure of the lncRNA to perform structure-functions studies which identified the minimal region of DRAIC necessary for repressing NF-κB. A 36-nucleotide hairpin (A+B) within DRAIC inhibits NF-κB, inhibits IκB phosphorylation and binds specifically with the IKKα coiled- coil domain with a very high affinity: K D of ∼1-5 nM. This interaction weakens the dimerization of of the coiled coil domains of two IKK subunits, a dimerization that is indispensable for IKK activity. A+B is sufficient and necessary to inhibit the oncogenic phenotypes in multiple cancer cell-lines, demonstrating that interaction with IKK and inhibition of NF-κB is key for cancer suppression by DRAIC. Presence of this critical hairpin is modulated by alternative splicing the extends exon 4 to exon 4a of DRAIC and the expression of exon 4a in lung tumors is associated with low NF-κB activity. This is also the first demonstration that a short RNA can disrupt coiled- coil dimerization.

Exploring the Interaction of Biotinylated FcGamma RI and IgG1 Monoclonal Antibodies on Streptavidin-Coated Plasmonic Sensor Chips for Label-Free VEGF Detection

Vascular endothelial growth factor (VEGF) is a critical angiogenesis biomarker associated with various pathological conditions, including cancer. This study leverages pre-biotinylated FcγRI interactions with IgG1-type monoclonal antibodies to develop a sensitive VEGF detection method. Utilizing surface plasmon resonance (SPR) technology, we characterized the binding dynamics of immobilized biotinylated FcγRI to an IgG1-type antibody, Bevacizumab (AVT), through kinetic studies and investigated suitable conditions for sensor surface regeneration. Subsequently, we characterized the binding of FcγRI-captured AVT to VEGF, calculating kinetic constants and binding affinity. A calibration curve was established to analyze the VEGF quantification capacity and accuracy of the biosensor, computing the limits of blank, detection, and quantification at a 95% confidence interval. Additionally, the specificity of the biosensor for VEGF over other protein analytes was assessed. This innovative biomimetic approach enabled FcγRI-mediated site-specific AVT capture, establishing a stable and reusable platform for detecting and accurately quantifying VEGF. The results indicate the effectiveness of the plasmonic sensor platform for VEGF detection, making it suitable for research applications and, potentially, clinical diagnostics. Utilizing FcγRI-IgG1 antibody binding, this study highlights the industrial and clinical value of advanced biosensing technologies, offering insights to enhance therapeutic monitoring and improve outcomes in anti-VEGF therapies.

Development of Biolayer Interferometry (BLI)-Based Double-Stranded RNA Detection Method with Application in mRNA-Based Therapeutics and Vaccines

Background: In vitro-transcribed (IVT) mRNA has been established as a promising platform for therapeutics and vaccine development. Double-stranded RNA (dsRNA) is a major impurity of IVT mRNA and can trigger unfavored immune responses, potentially causing adverse events in patients. Existing dsRNA detection and quantitation methods, such as gel electrophoresis, ELISA, or homogeneous time-resolved fluorescence (HTRF), have low sensitivity or are time-consuming. A recently published lateral flow immunoassay (LFSA) was shown to be fast, but it lacks the sensitivity for dsRNA with uridine modifications. Methods: In this study, we provided a possible explanation for the reduced sensitivity of existing quantitation methods for dsRNA with modified uridines by characterizing the binding affinities of commonly used anti-dsRNA antibodies. Then, a rapid and sensitive biolayer interferometry (BLI) dsRNA detection assay utilizing Flock House Virus (FHV) B2 protein was developed to overcome the challenges in dsRNA detection and the reduced sensitivity. Results: This assay allows the detection of dsRNA with different uridine modifications (ψ, m1ψ, 5 moU) with similar sensitivity as dsRNA without modification. Furthermore, we demonstrated this method can be used to quantify both short and long dsRNA, as well as hairpin-structured dsRNA, providing a more comprehensive detection for dsRNA impurities. Moreover, we applied this assay to monitor dsRNA removal through a purification process. Conclusions: Taken together, this BLI method could enable real-time monitoring of impurities in IVT mRNA production to prevent immunogenicity stemming from dsRNA.

Machine learning-augmented molecular dynamics simulations (MD) reveal insights into the disconnect between affinity and activation of ZTP riboswitch ligands

The challenge of targeting RNA with small molecules necessitates a better understanding of RNA-ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand-induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure-informed design, crystallography, and machine learning-augmented all-atom molecular dynamics simulations (MD) we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the ZTP riboswitch, a ligand-binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on-rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Structure-Informed Design of an Ultra Bright RNA-activated Fluorophore

Fluorogenic RNAs such as the Mango aptamers are uniquely powerful tools for imaging RNA. A central challenge has been to develop brighter, more specific, and higher affinity aptamer-ligand systems for cellular imaging. Here, we report an ultra-bright fluorophore for the Mango II system discovered using a structure-informed, fragment-based small molecule microarray approach. The new dye, Structure informed, Array-enabled LigAnD 1 (SALAD1) exhibits 3.5-fold brighter fluorescence than TO1-Biotin and subnanomolar aptamer affinity. Improved performance comes solely from alteration of dye-RNA interactions, without alteration of the chromophore itself. Multiple high-resolution structures reveal a unique and specific binding mode for the new dye resulting from improved pocket occupancy, a more defined binding pose, and a novel bonding interaction with potassium. The dye notably improves in-cell confocal RNA imaging. This work provides both introduces a new RNA-activated fluorophore and also a powerful demonstration of how to leverage fragment-based ligand discovery against RNA targets.

Fragment-to-Lead Medicinal Chemistry Publications in 2022

This Perspective is the eighth in an annual series that summarizes successful fragment-to-lead (F2L) case studies published each year. A tabulated summary of relevant articles published in 2022 is provided, and features such as target class, screening methods, and ligand efficiency are discussed both for the 2022 examples and for the combined examples over the years 2015-2022. In addition, trends and new developments in the field are summarized. In 2022, 18 publications described successful fragment-to-lead studies, including the development of three clinical compounds (MTRX1719, MK-8189, and BI-823911).

RNA-Selective Small-Molecule Ligands: Recent Advances in Live-Cell Imaging and Drug Discovery

RNA structures, including those formed from coding and noncoding RNAs, alternative to protein-based drug targets, could be a promising target of small molecules for drug discovery against various human diseases, particularly in anticancer, antibacterial and antivirus development. The normal cellular activity of cells is critically dependent on the function of various RNA molecules generated from DNA transcription. Moreover, many studies support that mRNA-targeting small molecules may regulate the synthesis of disease-related proteins via the non-covalent mRNA-ligand interactions that do not involve gene modification. RNA-ligand interaction is thus an attractive approach to address the challenge of "undruggable" proteins in drug discovery because the intracellular activity of these proteins is hard to be suppressed with small molecule ligands. We selectively surveyed a specific area of RNA structure-selective small molecule ligands in fluorescence live cell imaging and drug discovery because the area was currently underexplored. This state-of-the-art review thus mainly focuses on the research published within the past three years and aims to provide the most recent information on this research area; hopefully, it could be complementary to the previously reported reviews and give new insights into the future development on RNA-specific small molecule ligands for live cell imaging and drug discovery.

Repurposing Fc gamma receptor I (FcγRI, CD64) for site-oriented monoclonal antibody capture: A proof-of-concept study for real-time detection of tumor necrosis factor-alpha (TNF -α)

The controlled orientation of biomolecules on the sensor surface is crucial for achieving high sensitivity and accurate detection of target molecules in biosensing. FcγRI is an immune cell surface receptor for recognizing IgG-coated targets, such as opsonized pathogens or immune complexes. It plays a crucial role in T cell activation and internalization of the cargos, leading downstream signaling cascades. In this study, we repurposed the FcγRI as an analytical ligand molecule for site-oriented ADA capture, a monoclonal antibody-based biosimilar drug, on a plasmonic sensor surface and demonstrated the real-time detection of the corresponding analyte molecule, TNF-α. The study encompasses the analysis of comparative ligand behaviors on the surface, biosensor kinetics, concentration-dependent studies, and sensor specificity assays. The findings of this study suggest that FcγRI has a significant potential to serve as a universal ligand molecule for site-specific monoclonal antibody capture, and it can be used for biosensing studies, as it represents low nanomolar range affinity and excellent selectivity towards the target. However, there is still room for improvement in the surface stability and sensing response, and further studies are needed to reveal its performance on the monoclonal antibodies with various antigen binding sites and glycoforms.

In vitro Selection and in vivo Testing of Riboswitch-inspired Aptamers

Engineered aptamers for new compounds are typically produced by using in vitro selection methods. However, aptamers that are developed in vitro might not function as expected when introduced into complex cellular environments. One approach that addresses this concern is the design of initial RNA pools for selection that contain structural scaffolds from naturally occurring riboswitch aptamers. Here, we provide guidance on design and experimental principles for developing riboswitch-inspired aptamers for new ligands. The in vitro selection protocol (based on Capture-SELEX) is generalizable to diverse RNA scaffold types and amenable to multiplexing of ligand candidates. We discuss strategies to avoid propagation of selfish sequences that can easily dominate the selection. We also detail the identification of aptamer candidates using next-generation sequencing and bioinformatics, and subsequent biochemical validation of aptamer candidates. Finally, we describe functional testing of aptamer candidates in bacterial cell culture. Key features Develop riboswitch-inspired aptamers for new ligands using in vitro selection. Ligand candidates can be multiplexed to conserve time and resources. Test aptamer candidates in bacterial cells by grafting the aptamer back onto its expression platform.

Sequence- and structure-specific RNA oligonucleotide binding attenuates heterogeneous nuclear ribonucleoprotein A1 dysfunction

The RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (A1) regulates RNA metabolism, which is crucial to maintaining cellular homeostasis. A1 dysfunction mechanistically contributes to reduced cell viability and loss, but molecular mechanisms of how A1 dysfunction affects cell viability and loss, and methodologies to attenuate its dysfunction, are lacking. Utilizing in silico molecular modeling and an in vitro optogenetic system, this study examined the consequences of RNA oligonucleotide (RNAO) treatment on attenuating A1 dysfunction and its downstream cellular effects. In silico and thermal shift experiments revealed that binding of RNAOs to the RNA Recognition Motif 1 of A1 is stabilized by sequence- and structure-specific RNAO-A1 interactions. Using optogenetics to model A1 cellular dysfunction, we show that sequence- and structure-specific RNAOs significantly attenuated abnormal cytoplasmic A1 self-association kinetics and A1 cytoplasmic clustering. Downstream of A1 dysfunction, we demonstrate that A1 clustering affects the formation of stress granules, activates cell stress, and inhibits protein translation. With RNAO treatment, we show that stress granule formation is attenuated, cell stress is inhibited, and protein translation is restored. This study provides evidence that sequence- and structure-specific RNAO treatment attenuates A1 dysfunction and its downstream effects, thus allowing for the development of A1-specific therapies that attenuate A1 dysfunction and restore cellular homeostasis.

Fragment-Based Approaches to Identify RNA Binders

Although fragment-based drug discovery (FBDD) has been successfully implemented and well-explored for protein targets, its feasibility for RNA targets is emerging. Despite the challenges associated with the selective targeting of RNA, efforts to integrate known methods of RNA binder discovery with fragment-based approaches have been fruitful, as a few bioactive ligands have been identified. Here, we review various fragment-based approaches implemented for RNA targets and provide insights into experimental design and outcomes to guide future work in the area. Indeed, investigations surrounding the molecular recognition of RNA by fragments address rather important questions such as the limits of molecular weight that confer selective binding and the physicochemical properties favorable for RNA binding and bioactivity.