Measured Binding Affinities Research Articles

Improved Rapid Equilibrium Dialysis-Mass Spectrometry (RED-MS) Method for Measuring Small Molecule-Protein Complex Binding Affinities in Solution.

Rapid equilibrium dialysis (RED) is predominantly used for the characterization of drug absorption, distribution, metabolism, and excretion (ADME) properties in plasma and biological fluids. We describe herein improvements in the use of RED in conjunction with mass spectrometry (RED-MS) to enable robust binding affinity measurements of small molecules for recombinant proteins and complexes from a single dialysis data set. The affinities calculated from RED-MS correlated well with measurements by both surface plasmon resonance (SPR) and affinity selection mass spectrometry (AS-MS). The method was particularly useful for quantifying the binding of small molecules to large protein complexes that were not amendable by common biophysical characterization techniques. Compound pooling and integration with automated liquid handling increased assay throughput and enabled the analysis of hundreds of measurements per week. RED-MS offers a viable option for measuring compound binding in solution and may facilitate small molecule affinity optimization toward difficult-to-drug protein complexes.

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agonist has been observed to promote the proliferation and viability of hypothalamus cells in the human body. The objective of the present study is to discover new agonist compounds for the GPR40 protein through the utilization of machine learning and pharmacophore-based screening techniques, in conjunction with other computational methodologies such as docking, molecular dynamics simulations, free energy calculations, and investigations of the free energy landscape. In the course of our investigation, we successfully identified five unreported agonist compounds that exhibit robust docking score, displayed stability in ligand RMSD and consistent hydrogen bonding with the receptor in the MD trajectories. Free energy calculations were observed to be higher than control molecule. The measured binding affinities of compounds namely 1, 3, 4, 6 and 10 were -13.9, -13.5, -13.4, -12.9, and -12.1 Kcal/mol, respectively. The identified molecular agonist that has been found can be assessed in terms of its therapeutic efficacy in the treatment of neurological diseases.

Fluorescent Labeling Can Significantly Perturb Measured Binding Affinity and Selectivity of Peptide-Protein Interactions.

Peptide-based drugs are powerful inhibitors of therapeutically relevant protein-protein interactions. Their affinity and selectivity for target proteins are commonly assessed using fluorescence-based assays such as anisotropy/polarization or quantitative microarrays. This study reveals that labeling can perturb peptide/protein binding by more than 1 order of magnitude. We have recently developed inhibitors targeted to the N-terminal Src homology 2 (SH2) domain of oncogenic phosphatase SHP2. Despite their high activity and selectivity, these molecules demonstrated an undesired interaction with the SH2 domain of another protein, known as APS, in a fluorescence microarray assay. Fluorescence anisotropy measurement in solution showed that the dissociation constant was significantly influenced by labeling (∼10 times), and the effect depended on the specific fluorophore and SH2 domain. Notably, displacement assays performed with unlabeled peptides were successfully used to eliminate these artifacts, demonstrating that the inhibitors' affinity for their target is over 1,000 times higher than for APS.

Oligomerization-driven avidity correlates with SARS-CoV-2 cellular binding and inhibition

Cellular processes are controlled by the thermodynamics of the underlying biomolecular interactions. Frequently, structural investigations use one monomeric binding partner, while ensemble measurements of binding affinities generally yield one affinity representative of a 1:1 interaction, despite the majority of the proteome consisting of oligomeric proteins. For example, viral entry and inhibition in SARS-CoV-2 involve a trimeric spike surface protein, a dimeric angiotensin-converting enzyme 2 (ACE2) cell-surface receptor and dimeric antibodies. Here, we reveal that cooperativity correlates with infectivity and inhibition as opposed to 1:1 binding strength. We show that ACE2 oligomerizes spike more strongly for more infectious variants, while exhibiting weaker 1:1 affinity. Furthermore, we find that antibodies use induced oligomerization both as a primary inhibition mechanism and to enhance the effects of receptor-site blocking. Our results suggest that naive affinity measurements are poor predictors of potency, and introduce an antibody-based inhibition mechanism for oligomeric targets. More generally, they point toward a much broader role of induced oligomerization in controlling biomolecular interactions.

High-throughput affinity measurements of direct interactions between activation domains and co-activators.

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 K ds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

Fc engineering by monoclonal mammalian cell display for improved affinity and selectivity towards FcγRs.

Fc optimization can significantly enhance therapeutic efficacy of monoclonal antibodies. However, existing Fc engineering approaches are sub-optimal with noted limitations, such as inappropriate glycosylation, polyclonal libraries, and utilizing fragment but not full-length IgG display. Applying cell cycle arrested recombinase-mediated cassette exchange, this study constructed high-quality monoclonal Fc libraries in CHO cells, displayed full-length IgG on cell surface, and preformed ratiometric fluorescence activated cell sorting (FACS) with the antigen and individual FcγRs. Identified Fc variants were quantitatively evaluated by flow cytometry, ELISA, kinetic and steady-state binding affinity measurements, and cytotoxicity assays. An error-prone Fc library focusing on the hinge-CH2 region was constructed in CHO cells with a functional diversity of 7.5×106. Panels of novel Fc variants with enhanced affinity and selectivity for FcγRs were isolated. Particularly, clone 2a-10 (G236E/K288R/K290W/K320M) showed increased binding strength towards FcγRIIa-131R and 131H allotypes with kinetic dissociation constants (KD-K) of 140nM and 220nM, respectively, while reduced binding strength towards FcγRIIb compared to WT Fc; clone 2b-1 (K222I/V302E/L328F/K334E) had KD-K of 180nM towards FcγRIIb; clone 3a-2 (P247L/K248E/K334I) exhibited KD-K of 190nM and 100nM towards FcγRIIIa-176F and 176V allotypes, respectively, and improved potency of 2.0ng/ml in ADCC assays. Key mutation hotspots were identified, including P247 for FcγRIIIa, K290 for FcγRIIa, and K334 for FcγRIIb bindings. Discovery of Fc variants with enhanced affinity and selectivity towards individual FcγR and the identification of novel mutation hotspots provide valuable insights for further Fc optimization and serve as a foundation for advancing antibody therapeutics development.

#2931 Advancing personalized extracorporeal therapies for albumin bound toxins through serum albumin binding function

Abstract Background and Aims Liver failure poses a significant clinical challenge with compromised functionality of the liver and accumulation of toxic metabolites. The lack of bedside-compatible methods to evaluate treatment response during therapy underscores the urgent need for refined diagnostic tools. Albumin, the principal transport protein, reflecting the elevated toxin and metabolite levels in liver failure, assumes a critical role in assessing the severity of hepatic dysfunction. This research introduces a novel method centered on a dansylsarcosine-based kinetic modeling of serum albumin binding, aiming to improve therapeutic decision-making in hepatic failure. This biomarker is designed to offer critical insights into albumin's dynamic interactions, essential for assessing liver function and effectiveness of therapeutic interventions. The study aims to supplement existing diagnostic methods, providing a more comprehensive evaluation of irreversible damages of albumin in liver failure and guiding more effective treatment strategies. Method In this study, we employed a novel method for measuring binding affinity at varying levels of dansylsarcosine, aiming to describe the kinetic properties of the Sudlow II binding site. The binding behavior of albumin under various concentrations of dansylsarcosine was characterized using a modified Michaelis-Menten kinetic model. To enhance accuracy, a formula correction was employed for both intrinsic absorbance and the influence of dansylsarcosine. The method facilitates kinetic modeling, exploring the behavior of the binding site under diverse ligand concentrations. Emerging from this are two key players: Bmax and Kd. Bmax quantifies the maximum capacity of binding sites available for occupation by dansylsarcosine. Kd signifies the concentration of dansylsarcosine at which half of the Sudlow II binding sites are saturated. The method was applied to a patient database of patients with hepatic failure treated with OPAL or MARS liver support systems (albumin dialysis with albumin regeneration). The applied analysis focused on the correlation with existing clinical scores and the description of therapeutic effects. Results The new method correlates with established markers, scores and therapeutic effects. For Patient samples from the OPALESCE database, Bmax and Kd exhibited correlations with established albumin biomarkers. Subgroup analysis revealed associations with clinical scores such as GCS, SOFA, MELD, ACLF, CLIF-OF and ABiC emphasizing the clinical relevance of Bmax and Kd values. Therapeutic effects were assessed using the OPALESCE database, demonstrating the capability of Bmax and Kd to describe therapy outcomes. Significant differences between the start and end of the first treatment were detected for MARS with Bmax and OPAL with Kd. Bmax and Kd values proved to be valuable indicators for therapeutic effects, aligning with established parameters such as ABiC and the CLIF-OF Score. These findings underscore the potential clinical utility of Bmax and Kd as robust markers for assessing therapy outcomes in liver support interventions. Conclusion The results help to provide a comprehensive understanding of albumin binding dynamics in patients with hepatic failure, offering a valuable tool for therapeutic decisions. The impact of therapy can be described using Bmax and Kd, both of which correlate with established clinical scores within the patient population. Bmax unveils irreversible damages to the albumin molecule, showcasing its potential to identify scenarios where applied therapies fail to enhance albumin binding functions. This insight prompts the consideration of alternative therapeutic pathways, including the consideration of therapeutic plasma exchange when extracorporeal methods are insufficient, showcasing the biomarker's utility in guiding precision therapeutic decisions for improved outcomes in hepatic failure management. The significance of albumin function for uraemic toxins needs to be further investigated. Optimization of dialysis for patients with renal insufficiency appears possible.

A micro-fabricated potentiometric platform with application in a COVID-19 drug quantification and the determination of its binding affinity to a supramolecular host

Recent applications in host–guest chemistry include advanced sensing technologies, drug formulations, and drug delivery. Quantification of host–guest binding constants is significant to the continued optimization of their use. Binding constants offer valuable information about the stability and affinity of the formedcomplex that is essential to both drug formulations and delivery. In this study a novel green, portable, disposable, and cost-efficient microfabricated potentiometric sensor is introduced and successfully applied to determine the concentration of remdesivir (RMD), a COVID-19 drug, and its binding affinity to the supramolecular host sulfobutylether-β-cyclodextrin (SBEβCD) as a compatible solubilizing agent; to overcome the low water solubility of RMD which presents a significant challenge in its intravenous administration. The sensor incorporates multi-walled carbon nanotubes to improve stability and detection limits. To reduce interference effects and enhance the selectivity of the sensor for RMD, 4-tert-butyl-calix[8]arene was employed as an ionophore. A Nernstian potentiometric response for RMD over a concentration range of 1.0 × 10−3 to 1.0 × 10−6 M with a slope of 58.2 ± 0.9 mV decade−1 and a detection limit of 0.23 µM were achieved. The binding constant of the RMD-SBEβCD complex was determined to be 9.76 × 103 M−1. The proposed sensor was shown to be effective for drug quantification and measurements of binding affinities, providing valuable insights into the physicochemical properties of target drugs.

Using the Dynamic Well-Stirred Model to Extrapolate Hepatic Clearance of Organic Anion-Transporting Polypeptide Transporter Substrates without Assuming Albumin-Mediated Hepatic Drug Uptake.

Extrapolating in vivo hepatic clearance from in vitro uptake data is a known challenge, especially for organic anion-transporting polypeptide transporter (OATP) substrates, and the well-stirred model (WSM) commonly yields systematic underpredictions for those anionic drugs, hypothetically due to "albumin-mediated hepatic drug uptake". In the present study, we demonstrate that the WSM incorporating the dynamic free fraction (f D), a measure of drug protein binding affinity, performs reasonably well in predicting hepatic clearance of OATP substrates. For a selection of anionic drugs, including atorvastatin, fluvastatin, pravastatin, rosuvastatin, pitavastatin, cerivastatin, and repaglinide, this dynamic well-stirred model (dWSM) correctly predicts hepatic plasma clearance within 2-fold error for six out of seven OATP substrates examined. The geometric mean of clearance ratios between the predicted and the observed values falls in the range of 1.21-1.38. As expected, the WSM with unbound fraction (f u) systematically underpredicts hepatic clearance with greater than 2-fold error for five out of seven drugs, and the geometric mean of clearance ratios between the predicted and the observed values is in the range of 0.20-0.29. The results suggest that, despite its simplicity, the dWSM operates well for transporter-mediated uptake clearance, and that clearance under-prediction of OATP substrates may not necessarily be associated with the chemical class of the anionic drugs, nor is it a result of albumin-mediated hepatic drug uptake as currently hypothesized. Instead, the superior prediction power of the dWSM confirms the utility of the dynamic free fraction in clearance prediction and the importance of drug plasma binding kinetics in hepatic uptake clearance. SIGNIFICANCE STATEMENT: The traditional well-stirred model (WSM) consistently underpredicts organin anion-transporting polypeptide transporter (OATP)-mediated hepatic uptake clearance, hypothetically due to the albumin-mediated hepatic drug uptake. In this manuscript, we apply the dynamic WSM to extrapolate hepatic clearance of the OATP substrates, and our results show significant improvements in clearance prediction without assuming albumin-mediated hepatic drug uptake.

Abstract 6025: Discovery of novel macrocyclic peptide radioligands for tumor therapy by mRNA display

Abstract Radioligand therapeutics (RLT) aim to selectively deliver radioisotopes to cancer tissues to eradicate tumor cells while limiting the damage to surrounding tissues. They are composed of a tumor-targeting ligand and a radioactive payload. The therapeutic potential of RLT strongly depends on their biostability and residence time in tumor and healthy tissue after injection. Macrocyclic peptides are effective tumor-targeting ligands because their size and molecular characteristics confer optimal pharmacologic properties that can be fine-tuned during the development process. mRNA display is a powerful platform for macrocyclic peptide drug discovery that identifies high affinity peptide ligands through in vitro selection. Here, we describe the selection and initial characterization of macrocyclic peptides discovered by mRNA display that bind to the extracellular domain of a tumor target. Enrichment of the library for peptides that bound to the recombinant target protein was observed after four rounds of selection with no further enrichment in round five. The library of round 5 was sequenced by Illumina sequencing and the top 1,000 sequences (highest counts) were used to perform cluster analysis. We tested the top sequences of 43 different clusters in in vitro translation binding assays to validate the selection output and identify sequences for hit confirmation. Most of the sequences tested showed clear binding to the target protein and only a few peptides showed binding to a closely related protein from the same protein family. No non-specific binding of the discovered peptides to an irrelevant protein was observed showing the high specificity of the generated library. From this confirmatory selection data, we chose a subset of peptides for chemical synthesis. Binding affinity measurements were determined by surface plasmon resonance (SPR) and cellular binding of selected candidates to target-positive and -negative tumor cells was analyzed by flow cytometry. From this hit confirmation data, in vitro internalization and in vitro anti-tumor efficacy of radiolabeled peptide candidates for RLT are assessed. Our data highlights the potential of mRNA display platforms for fast and efficient discovery of highly specific macrocyclic peptides with optimal binding properties suitable for RLT. Citation Format: Anika Jaekel, Kathryn Sherry, Prachi Desai, David Fox, Tang Guo-Qing, Manuel Sturzbecher-Hoehne, Dennis Mewis, Kaori Noridomi, Liam Rayman, Ping Ye. Discovery of novel macrocyclic peptide radioligands for tumor therapy by mRNA display [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6025.

In Silico Prediction and Molecular Simulation of Antimicrobial Peptide Variants From Lactobacillus sp. Against Porphyromonas gingivalis and Fusobacterium nucleatum in Oral Squamous Cell Carcinoma

ABSTRACT Porphyromonas gingivalis and Fusobacterium nucleatum are known to contribute to a variety of tumorigenic pathways linked to the progression of oral squamous cell carcinoma (OSCC). The growing global incidence of antibiotic resistance highlights the critical need to consider the use of antimicrobial peptides (AMPs) as a viable alternative to conventional antibiotics. The current study comprehensively tested Lactobacillus sp.−derived AMPs against bacterially exacerbated OSCC. A total of 52 AMPs were obtained from various databases, and an in silico analysis determined their potent antibacterial and anticancer characteristics after a rigorous screening and pruning approach. Twelve AMPs were tested for 3D structural alignment prediction and validation, with the GH12 synthetic AMP serving as a control. These candidate peptides were thoroughly screened against six important virulence proteins of P. gingivalis and four of F. nucleatum, with the lowest energy score of the docked complexes measuring binding affinity and interactions with active residues being chosen. plpl_18 was determined as the most efficient new AMP that interacted with the virulence protein RagB of P. gingivalis and Fap2 of F. nucleatum with docking scores of −238.24 and −254.27 kcal/mol, respectively. This AMP plpl_18 was docked against selective target OSCC regulatory proteins such as cytokines, metallomatrix proteinase, MAPK, E‐cadherin, and JAK‐1 proteins. Among these proteins, it docked against matrix metalloproteinase‐9 with the highest negative docking scores of −7.5, −260.956, and −1361.9 kcal/mol using AutoDock Vina, HPEPDOCK, and ClusPro 2.0, respectively. Molecular dynamic simulation was used to perform extrapolated validation. These computational studies provide an essential foundation for anticipated laboratory and clinical investigations concerning the possibility of adapting therapeutic peptides based on probiotics to combat the proliferation of OSCC, which is accelerated by F. nucleatum and P. gingivalis.

PRA-Pred: Structure-based prediction of protein-RNA binding affinity

Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.

Introducing the Dynamic Well-Stirred Model for Predicting Hepatic Clearance and Extraction Ratio

The well-stirred model (WSM) incorporating the fraction of unbound drug (fu) to account for the effect of plasma binding on intrinsic clearance has been widely used for predicting hepatic clearance under the assumption that drug protein binding reaches equilibrium instantaneously. Our theoretical analysis reveals that the effect of protein binding on intrinsic clearance is better accounted for with the dynamic free fraction (fD), a measure of drug protein binding affinity, which leads to a putative dynamic well-stirred model (dWSM) without the instantaneous equilibrium assumption. Using recombinant CYP3A4 as the in vitro clearance system, we demonstrate that the binding effect of albumin on the intrinsic clearance of both highly bound midazolam and highly free verapamil is fully corrected by their corresponding fD values, respectively. On the other hand, fu only corrects the binding effect of albumin on the intrinsic clearance of verapamil, and yields severe over-correction of the intrinsic clearance of midazolam. The results suggest that the traditional WSM is suitable for highly free drugs like verapamil but not necessarily for highly bound drugs such as midazolam due to the violation of the instantaneous equilibrium assumption or under-estimating the true free drug concentration. In comparison, the dWSM incorporating fD holds true as long as drug elimination follows steady-state kinetics, and hence, it is more broadly applicable to drugs with different protein binding characteristics. Here we demonstrate with 36 diverse drugs, that the dWSM significantly improves the accuracy of predicting human hepatic clearance and liver extraction ratio from in vitro microsomal clearance data, highlighting the importance of drug plasma protein binding kinetics in addressing the under-prediction of hepatic clearance by the WSM.

A Semiquantitative Protein-Fragment Complementation Assay to Study Protein-Protein Interactions of the Polymerase Complex in Cellula.

Protein-fragment complementation assays (PCAs) are powerful tools to investigate protein-protein interactions in a cellular context. These are especially useful to study unstable proteins and weak interactions that may not resist protein isolation or purification. The PCA based on the reconstitution of the Gaussia princeps luciferase (split-luc) is a sensitive approach allowing the mapping of protein-protein interactions and the semiquantitative measurement of binding affinity. Here, we describe the split-luc protocol we used to map the viral interactome of measles virus polymerase complex.

Direct Binding Methods to Measure Receptor-Ligand Interactions.

G-protein-coupled receptors (GPCRs) contribute to numerous physiological processes via complex network mechanisms. While indirect signaling assays (Ca2+ mobilization, cAMP production, and GTPγS binding) have been useful in identifying and characterizing downstream signaling mechanisms of GPCRs, these methods lack measurements of direct binding affinities, kinetics, binding specificity, and selectivity that are important parameters in GPCR drug discovery. In comparison to existing direct methods that use radio- or fluorescent labels, label-free techniques can closely emulate the native interactions around binding partners. Surface plasmon resonance (SPR) is a label-free technique that utilizes the refractive index (RI) property and is applied widely in quantitative GPCR-ligand binding kinetics measurement including small molecules screening. However, purified GPCRs are further embedded in a synthetic lipid environment which is immobilized through different tags to the SPR sensor surface, resulting in a non-native environment. Here, we introduced a methodology that also uses the RI property to measure binding interactions in a label-free, immobilization-free arrangement. The free-solution technique is successfully applied in quantifying the interaction of bioactive lipids to cognate lipid GPCRs, which is not purified but rather present in near-native conditions, i.e., in milieu of other cytoplasmic lipids and proteins. To further consider the wide applicability of these free-solution approaches in biomolecular interaction research, additional applications on a variety of receptor-ligand pairs are imperative.

Nonspecificity fingerprints for clinical-stage antibodies in solution.

Monoclonal antibodies (mAbs) have successfully been developed for the treatment of a wide range of diseases. The clinical success of mAbs does not solely rely on optimal potency and safety but also require good biophysical properties to ensure a high developability potential. In particular, nonspecific interactions are a key developability parameter to monitor during discovery and development. Despite an increased focus on the detection of nonspecific interactions, their underlying physicochemical origins remain poorly understood. Here, we employ solution-based microfluidic technologies to characterize a set of clinical-stage mAbs and their interactions with commonly used nonspecificity ligands to generate nonspecificity fingerprints, providing quantitative data on the underlying physical chemistry. Furthermore, the solution-based analysis enables us to measure binding affinities directly, and we evaluate the contribution of avidity in nonspecific binding by mAbs. We find that avidity can increase the apparent affinity by two orders of magnitude. Notably, we find that a subset of these highly developed mAbs show nonspecific electrostatic interactions, even at physiological pH and ionic strength, and that they can form microscale particles with charge-complementary polymers. The group of mAb constructs flagged here for nonspecificity are among the worst performers in independent reports of surface and column-based screens. The solution measurements improve on the state-of-the-art by providing a stand-alone result for individual mAbs without the need to benchmark against cohort data. Based on our findings, we propose a quantitative solution-based nonspecificity score, which can be integrated in the development workflow for biological therapeutics and more widely in protein engineering.

Binding of microRNA-122 to the hepatitis C virus 5' untranslated region modifies interactions with poly(C) binding protein 2 and the NS5B viral polymerase.

Hepatitis C virus (HCV) requires two cellular factors, microRNA-122 (miR-122) and poly(C) binding protein 2 (PCBP2), for optimal replication. These host factors compete for binding to the 5' end of the single-stranded RNA genome to regulate the viral replication cycle. To understand how they interact with the RNA, we measured binding affinities of both factors for an RNA probe representing the 5' 45 nucleotides of the HCV genome (HCV45). Isothermal titration calorimetry revealed two, unequal miR-122 binding sites in HCV45, high-affinity (S1) and low-affinity (S2), differing roughly 100-fold in binding affinity. PCBP2 binds a site overlapping S2 with affinity similar to miR-122 binding to S2. PCBP2 circularizes the genome by also binding to the 3' UTR, bridging the 5' and 3' ends of the genome. By competing with PCBP2 for binding at S2, miR-122 disrupts PCBP2-mediated genome circularization. We show that the viral RNA-dependent RNA polymerase, NS5B, also binds to HCV45, and that the binding affinity of NS5B is increased in the presence of miR-122, suggesting miR-122 promotes recruitment of the polymerase. We propose that competition between miR-122 and PCBP2 for HCV45 functions as a translation-to-replication switch, determining whether the RNA genome templates protein synthesis or RNA replication.

Molecular insight into the specific enzymatic properties of TREX1 revealing the diverse functions in processing RNA and DNA/RNA hybrids.

In various autoimmune diseases, dysfunctional TREX1 (Three prime RepairExonuclease 1) leads to accumulation of endogenous single-stranded DNA (ssDNA), double-stranded DNA (dsDNA) and DNA/RNA hybrids in the cytoplasm and triggers immune activation through the cGAS-STING pathway. Although inhibition of TREX1 could be a useful strategy for cancer immunotherapy, profiling cellular functions in terms of its potential substrates is a key step. Particularly important is the functionality of processing DNA/RNA hybrids and RNA substrates. The exonuclease activity measurements conducted here establish that TREX1 can digest both ssRNA and DNA/RNA hybrids but not dsRNA. The newly solved structures of TREX1-RNA product and TREX1-nucleotide complexes show that 2'-OH does not impose steric hindrance or specific interactions for the recognition of RNA. Through all-atom molecular dynamics simulations, we illustrate that the 2'-OH-mediated intra-chain hydrogen bonding in RNA would affect the binding with TREX1 and thereby reduce the exonuclease activity. This notion of higher conformational rigidity in RNA leading TREX1 to exhibit weaker catalytic cleavage is further validated by the binding affinity measurements with various synthetic DNA-RNA junctions. The results of this work thus provide new insights into the mechanism by which TREX1 processes RNA and DNA/RNA hybrids and contribute to the molecular-level understanding of the complex cellular functions of TREX1 as an exonuclease.

Investigating potential of cholic acid, syringic acid, and mangiferin as cancer therapeutics through sphingosine kinase 1 inhibition

The signaling of sphingosine kinase 1 (SphK1) and sphingosine-1-phosphate (S1P) regulates various diseases, including multiple sclerosis, atherosclerosis, rheumatoid arthritis, inflammation-related ailments, diabetes, and cancer. SphK1 is considered an attractive potential drug target and is extensively explored in cancer and other inflammatory diseases. In this study, we have investigated the inhibitory potential and binding affinity of SphK1 with cholic acid (CA), syringic acid (SA), and mangiferin (MF) using a combination of docking and molecular dynamics (MD) simulation studies followed by experimental measurements of binding affinity and enzyme inhibition assays. We observed these compounds bind to SphK1 with a significantly high affinity and eventually inhibit its kinase activity with IC50 values of 28.23 μM, 33.35 μM, and 57.2 μM for CA, SA, and MF, respectively. Further, the docking and 100 ns MD simulation studies showed that CA, SA, and MF bind with the active site residues of SphK1 with favorable energy and strong non-covalent interactions that might be accountable for inhibiting its kinase activity. Our finding indicates that CA, SA, and MF may be implicated in designing novel anti-cancer therapeutics with an improved affinity and lesser side effects by targeting SphK1.

Cooperativity at the Solution/Solid Interface: Formation and Reactivity of Self-Assembled Monolayers

Cooperativity is a molecular phenomenon, in which multiple interactions participate synchronously to significantly affect the activity and/or selectivity of the interacting components by accelerating (or impeding) their chemical or physical processes. Cooperative effects are prevalent in biology and can be observed in chemistry and materials science. Synthesis of functional materials frequently requires surfaces and interfaces where cooperative interactions can propagate with substrate assistance. In this context, of great interest is the characterization and control of ligands binding to metal centers, given their rich chemistry and prevalence in functional materials. In addition, self-assembly of compounds on surfaces can be steered by cooperative effects, and supramolecular polymerization can also proceed in a cooperative manner. Essential for these kinds of chemistries is the availability of highly resolving analysis methods to investigate reaction and adsorption outcomes directly at the surface. Scanning tunneling microscopy (STM) is particularly well suited to the study of such processes on surfaces and interfaces by offering both molecular resolution and real time and space information regarding changes in the electronic structure of materials upon adsorption and/or reaction.In this contribution, we present quantitative STM experimental results and supporting theoretical calculations of the binding dynamics of neutral ligands (heteroaromatics, O2) to metal porphyrins adsorbed on solid substrates as well as self-assembly of polyaromatics on metallic supports. Single molecule level STM imaging was accomplished in-situ under ambient solution conditions. We measured binding affinities and uncovered surface modulated cooperativity both in coordination reactions and in molecular self-assembly -- results essential for understanding adsorbate-surface interactions. Computations corroborate our experimental findings. The high-resolution, quantitative knowledge gained at the molecular level combined with theoretical studies offer not only insights into chelation, adsorption, and cooperativity but also into control parameters for surface-driven synthesis, tunable surface functionalization, and catalysis selectivity control.