Binding Data Research Articles

MAM-2201 acute administration impairs motor, sensorimotor, prepulse inhibition, and memory functions in mice: a comparison with its analogue AM-2201

Rationale1-[(5-fluoropentyl)-1H-indol-3-yl](4-methyl-1-naphthalenyl) methanone (MAM-2201) is a potent synthetic cannabinoid receptor agonist illegally marketed in “spice” products and as “synthacaine” for its psychoactive effects. It is a naphthoyl-indole derivative which differs from its analogue 1-[(5-Fluoropentyl)-1H-indol-3-yl](1-naphthylenyl) methanone (AM-2201) by the presence of a methyl substituent on carbon 4 (C-4) of the naphthoyl moiety. Multiple cases of intoxication and impaired driving have been linked to AM-2201 and MAM-2201 consumption.ObjectivesThis study aims to investigate the in vitro (murine and human cannabinoid receptors) and in vivo (CD-1 male mice) pharmacodynamic activity of MAM-2201 and compare its effects with those induced by its desmethylated analogue, AM-2201.ResultsIn vitro competition binding studies confirmed that MAM-2201 and AM-2201 possess nanomolar affinity for both CD-1 murine and human CB1 and CB2 receptors, with preference for the CB1 receptor. In agreement with the in vitro binding data, in vivo studies showed that MAM-2201 induces visual, acoustic, and tactile impairments that were fully prevented by pretreatment with CB1 receptor antagonist/partial agonist AM-251, indicating a CB1 receptor mediated mechanism of action. Administration of MAM-2201 also altered locomotor activity and PPI responses of mice, pointing out its detrimental effect on motor and sensory gating functions and confirming its potential use liability. MAM-2201 and AM-2201 also caused deficits in short- and long-term working memory.ConclusionThese findings point to the potential public health burden that these synthetic cannabinoids may pose, with particular emphasis on impaired driving and workplace performance.

MachineTFBS: Motif-based method to predict transcription factor binding sites with first-best models from machine learning library

This study aims to model the high-affinity transcription factor (TF) binding sites (TFBSs) in Saccharomyces cerevisiae promoters using high-throughput experimental TF binding data to train, test, and validate models. We introduce MachineTFBS, a machine learning based TFBS prediction tool consisting of a library of machine learning models generated by treating each TF's protein binding microarray (PBM) dataset as an individual optimization problem and selectively generating the first-best performing models. Since the modeling quality of machine learning methods varies for different TFs, we tested Random Forest, eXtreme Gradient Boosting, and up to 5-depth Deep Learning models. We extracted 274 high-resolution PBM datasets of 159 S. cerevisiae TFs from the UniProbe database. We designed an algorithm for the greedy selection of the first-best models and feature combinations from five feature extraction functions for each PBM data. The optimal subset of the functions was identified using a feature elimination algorithm. The feature elimination algorithm advances in successive cycles for each PBM data until the elimination of any feature(s) improves model performance or the feature set size is reduced to 1; in turn, reports the optimal subset. MachineTFBS predicts TFBSs with an average Matthews Correlation Coefficient score of 0.873.

Structure-affinity relationships of stereoisomers of norbenzomorphan-derived σ2R/TMEM97 modulators

The sigma 2 receptor (σ2R), which is identical to transmembrane protein 97 (TMEM97), is attracting increasing interest as a possible therapeutic target for various indications in neuroscience. In continuation of a program to identify novel compounds that bind with high affinity and selectivity to σ2R/TMEM97, we performed structure-affinity-relationship (SAfiR) studies of several sets of σ2R/TMEM97 ligands having a B-norbenzomorphan ring core. Binding data for σ2R/TMEM97 and σ1R of several enantiomeric pairs of piperazine-substituted norbenzomorphans show the (1S,5R)-enantiomers have affinities (Ki = 9–75 nM) for σ2R/TMEM97 that are 2–3-fold higher than their enantiomorphic (1R,5S)-analogs; however, there is no clear trend for selectivity for σ2R/TMEM97 vs σ1R. A series of N-alkyl piperazino (1S,5R)-norbenzomorphans was then evaluated, and with the exception of compounds having N-alkyl groups substituted with oxygen or amino groups at C (2) of an ethylene chain, Ki values for σ2R/TMEM97 are less than 25 nM, and several compounds have good selectivities (ca 7–16-fold) for σ2R/TMEM97 vs σ1R. Mono-substituted carbobenzyloxy analogs have Ki values for σ2R/TMEM97 comparable to the unsubstituted parent (Ki = ca 7–27 nM), but replacing the N-acyloxy group with N-acyl or N-arylsulfonyl groups provides analogs having lower affinity and selectivity. Some congeners with bioisosteric replacements of the piperazine group on the (1S,5R)-norbenzomorphan core have high affinity (Ki = <30 nM) for σ2R/TMEM97, but selectivities are modest. Computational docking studies for racemic pairs of piperazino norbenzomorphans show that individual (1S,5R)- and (1R,5S)-enantiomers adopt distinct poses upon binding to σ2R/TMEM97, whereas ligands belongingto the same enantiomeric series adopt closely similar binding poses. The protonated amino group in each of the enantiomorphic ligands engages in highly conserved salt bridges with Asp29 and cation-π interactions with Tyr150 that are the primary determinants of binding affinity. There is no correlation between any of the computational parameter outputs and Ki values, but this is unsurprising given the small energetic differences involved. Modeling also suggest sthat some compounds can extend deeper into σ2R/TMEM97 binding pocket forming salt bridges with Glu73.

Predicting binding between 55 cannabinoids and 4,799 biological targets by in silico methods.

Recently, there has been an increase in cannabis-derived products being marketed as foods, dietary supplements, and other consumer products. Cannabis contains over a hundred cannabinoids, many of which have unknown physiological effects. Since there are large numbers of cannabinoids, and many are not commercially available for in vitro testing, an in silico tool (Chemotargets Clarity software) was used to predict binding between 55 cannabinoids and 4,799 biological targets (enzymes, ion channels, receptors, and transporters). This tool relied on quantitative structure activity relationships (QSAR), structural similarity, and other approaches to predict binding. From this screening, 827 cannabinoid-target binding pairs were predicted, which included 143 unique targets. Many cannabinoids sharing core structures (cannabinoid "types") had similar binding profiles, whereas most cannabinoids containing carboxylic acid groups were similar without regards to their core structure. For some of the binding predictions (43), in vitro binding data were available, and they agreed well with in silico binding data (median fourfold difference in binding concentrations). Finally, clinical adverse effects associated with 22 predicted targets were identified from an online database (Clarivate Off-X), providing important insights on potential human health hazards. Overall, in silico biological target predictions are a rapid means to identify potential hazards due to cannabinoid-target interactions, and the data can be used to prioritize subsequent in vitro and in vivo testing.

Lectin Fingerprinting Distinguishes Antibody Neutralization in SARS-CoV-2.

Enveloped viruses co-opt host glycosylation pathways to decorate their surface proteins. As viruses evolve, emerging strains can modify their glycosylation patterns to influence host interactions and subvert immune recognition. Still, changes in viral glycosylation or their impact on antibody protection cannot be predicted from genomic sequences alone. Using the highly glycosylated SARS-CoV-2 Spike protein as a model system, we present a lectin fingerprinting method that rapidly reports on changes in variant glycosylation state, which are linked to antibody neutralization. In the presence of antibodies or convalescent and vaccinated patient sera, unique lectin fingerprints emerge that distinguish neutralizing versus non-neutralizing antibodies. This information could not be inferred from direct binding interactions between antibodies and the Spike receptor-binding domain (RBD) binding data alone. Comparative glycoproteomics of the Spike RBD of wild-type (Wuhan-Hu-1) and Delta (B.1.617.2) variants reveal O-glycosylation differences as a key determinant of immune recognition differences. These data underscore the interplay between viral glycosylation and immune recognition and reveal lectin fingerprinting to be a rapid, sensitive, and high-throughput assay to distinguish the neutralization potential of antibodies that target critical viral glycoproteins.

Preclinical data on morpholine (3,5-di-tertbutyl-4-hydroxyphenyl) methanone induced anxiolysis.

Trimetozine is used to be indicated for the treatment of mental illnesses, particularly anxiety. The present study provides data on the pharmacological profile of trimetozine derivative morpholine (3,5-di-tert-butyl-4-hydroxyphenyl) methanone (LQFM289) which was designed from molecular hybridization of trimetozine lead compound and 2,6-di-tert-butyl-hydroxytoluene to develop new anxiolytic drugs. Here, we conduct molecular dynamics simulations, docking studies, receptor binding assays, andin silicoADMET profiling of LQFM289 before its behavioral and biochemical assessment in mice within the dose range of 5-20mg/kg. The docking of LQFM289 showed strong interactions with the benzodiazepine binding sites and matched well with receptor binding data. With the ADMET profile of this trimetozine derivative that predicts a high intestinal absorption and permeability to blood-brain barrier without being inhibited by the permeability glycoprotein, the oral administration of LQFM289 10mg/kg consistently induced anxiolytic-like behavior of the mice exposed to the open field and light-dark box apparatus without eliciting motor incoordination in the wire, rotarod, and chimney tests. A decrease in the wire and rotarod´s fall latency coupled with an increase in the chimney test´s climbing time and a decrease in the number of crossings in the open field apparatus at the dose of 20mg/kg of this trimetozine derivative suggest sedative or motor coordination impairment at this highest dose. The attenuation of the anxiolytic-like effects of LQFM289 (10mg/kg) by flumazenil pretreatment implicates the participation of benzodiazepine binding sites. The lowering of corticosterone and tumor necrosis factor alpha (cytokine) in LQFM289-treated mice at a single oral (acute) dose of 10mg/kg suggests that the anxiolytic-like effect of this compound also involves the recruitment of non-benzodiazepine binding sites/GABAergic molecular machinery.

Next generation multimodal chromatography resins via an iterative mapping approach: Chemical diversity, high-throughput screening, and chromatographic modelling

Multimodal chromatography resins are becoming a key tool in the purification of biomolecules. The main objective of this research was the establishment of an iterative framework for the rapid development of new multimodal resins to provide novel selectivity for the future purification challenges. A large chemically diverse virtual library of 100 multimodal Capto™ MMC ligand analogues was created, and a broad array of chemical descriptors were calculated for each ligand in silico. Principal component analysis (PCA) was used to map the chemical diversity and guide selection of ligands for synthesis and coupling to the Capto ImpRes agarose base matrix. Twelve new ligands were prepared in two groups: ‘group one’ consist of L00-L07 and ‘group two’ consist of L08-L12. These ligands are diverse in the influence of varied secondary interactions such as hydrophobic interactions, H-bonding, etc. Additional resin prototypes were also prepared to look at the chromatographic impact of ligand density variation. High-throughput plate-based studies were performed for parallel resin screening for batch-binding of six model proteins at different chromatographic binding pH and sodium chloride concentration conditions. Principal component analysis of the binding data provided a chromatographic diversity map leading to the identification of ligands with improved binding. Further, the new ligands have improved separation resolution between a monoclonal antibody (mAb1) and product related impurities, a Fab fragment and high molecular weight (HMW) aggregates, using linear salt gradient elutions. To quantify the importance of secondary interactions, analysis of the retention factor of mAb1 on the ligands at various isocratic conditions lead to estimations of (a) the total number of water molecules and counter salt ions released during adsorption, and (b) hydrophobic contact area (HCA). The iterative mapping approach of chemical and chromatography diversity maps described in the paper proves to be a promising method for identifying new chromatography ligands for biopharmaceutical purification challenges.

Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency.

The interaction of T-cell receptors with peptide-major histocompatibility complex molecules (TCR-pMHC) plays a crucial role in adaptive immune responses. Currently there are various models aiming at predicting TCR-pMHC binding, while a standard dataset and procedure to compare the performance of these approaches is still missing. In this work we provide a general method for data collection, preprocessing, splitting and generation of negative examples, as well as comprehensive datasets to compare TCR-pMHC prediction models. We collected, harmonized, and merged all the major publicly available TCR-pMHC binding data and compared the performance of five state-of-the-art deep learning models (TITAN, NetTCR-2.0, ERGO, DLpTCR and ImRex) using this data. Our performance evaluation focuses on two scenarios: 1) different splitting methods for generating training and testing data to assess model generalization and 2) different data versions that vary in size and peptide imbalance to assess model robustness. Our results indicate that the five contemporary models do not generalize to peptides that have not been in the training set. We can also show that model performance is strongly dependent on the data balance and size, which indicates a relatively low model robustness. These results suggest that TCR-pMHC binding prediction remains highly challenging and requires further high quality data and novel algorithmic approaches.

LigBind: Identifying Binding Residues for Over 1000 Ligands with Relation-Aware Graph Neural Networks

Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind’s efficacy on large-scale ligand-specific benchmark datasets, and it generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The web server and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.

Binding Interaction Activities Powered by Desmos

Three computer-based activities focusing on fundamentals of binding interactions are described that are powered by Desmos, freely available web-based software. The software allows the instructor to monitor the progress of each student completing the activity by privately viewing their answers in real time, to enforce class pacing by modulating which parts of the activity are available at certain times, to acquire the full attention of the class for vital teaching moments by pausing the activity at will, and to promote learning by seamlessly graphing and fitting nonlinear data. Students learn that binding interactions are governed by kinetics and energetics which dictate affinities and binding curves. A generic binding interaction is used to derive an equation describing a hyperbolic-binding curve, and given binding curves are used to identify Kd values and relate them to relative affinities. This leads into a case study using myoglobin and oxygen where given binding curves are used to identify P50 values and relative values are justified via the energetics of the particular binding site. Last, students use nonlinear regression to fit imported binding data to the derived equation to construct a binding curve and determine the P50 affinity value. Students report acclimating to the Desmos environment quickly and an increased understanding of the relationship between Kd and binding affinity. After the activities, students are prepared for a lab analyzing hyperbolic binding and discussions on allosteric binding and sigmoidal curves. Ultimately, steps are laid out for instructors to try using Desmos, and thoroughly developed and assessed activities involving graphical biochemistry content are provided for use.

Cryo-EM structures and binding of mouse and human ACE2 to SARS-CoV-2 variants of concern indicate that mutations enabling immune escape could expand host range.

Investigation of potential hosts of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is crucial to understanding future risks of spillover and spillback. SARS-CoV-2 has been reported to be transmitted from humans to various animals after requiring relatively few mutations. There is significant interest in describing how the virus interacts with mice as they are well adapted to human environments, are used widely as infection models and can be infected. Structural and binding data of the mouse ACE2 receptor with the Spike protein of newly identified SARS-CoV-2 variants are needed to better understand the impact of immune system evading mutations present in variants of concern (VOC). Previous studies have developed mouse-adapted variants and identified residues critical for binding to heterologous ACE2 receptors. Here we report the cryo-EM structures of mouse ACE2 bound to trimeric Spike ectodomains of four different VOC: Beta, Omicron BA.1, Omicron BA.2.12.1 and Omicron BA.4/5. These variants represent the oldest to the newest variants known to bind the mouse ACE2 receptor. Our high-resolution structural data complemented with bio-layer interferometry (BLI) binding assays reveal a requirement for a combination of mutations in the Spike protein that enable binding to the mouse ACE2 receptor.

Abstract 5376: Protein structure-based modeling to improve MHC class I epitope predictions

Abstract The ability to accurately identify peptide ligands for a given major histocompatibility complex class I (MHC-I) molecule has immense value for targeted anticancer therapeutics. However, the highly polymorphic nature of the MHC-I protein makes universal prediction of peptide ligands challenging due to lack of experimental data describing most MHC-I variants. To address this challenge, we have developed a deep convolutional neural network, HLA-Inception, capable of predicting MHC-I peptide binding motifs using electrostatic properties of the MHC-I binding pocket. By approaching this immunological issue using molecular biophysics, we measure the impact of sidechain arrangement and topology on peptide binding, feature not captured by sequence-based MHC-I prediction methods. Through a combination of molecular modeling and simulation, 5821 MHC-I alleles were modeled, providing extensive coverage across human populations. Predicted peptide binding motifs fell into distinct clusters, each defined with different degrees of submotif heterogeneity. Peptide binding scores generated by HLA-Inception are strongly correlated with quantitative MHC-I binding data, indicating predicted peptides can be ranked, both within and between alleles. HLA-inception also showed high precision when predicting naturally presented peptides and can be used for rapid proteome-scale MHC-I peptide binding predictions. Finally, we show that the binding pocket diversity measured by HLA inception predicts response to checkpoint blockade. Citation Format: Eric A. Wilson, John Kevin Cava, Diego Chowell, Abhishek Singharoy, Karen S. Anderson. Protein structure-based modeling to improve MHC class I epitope predictions. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5376.

Immunology: Meta-learning for T cell-receptor binding specificity and beyond.

Predicting whether T-cell receptors bind to specific peptides is a challenging problem as the majority of binding examples in the training data involves only a few peptides. A new approach employs meta-learning to improve predictions for binding to peptides for which no or little binding data exists.

Determinants of Superselectivity─Practical Concepts for Application in Biology and Medicine.

Multivalent interactions are common in biological systems and are also widely deployed for targeting applications in biomedicine. A unique feature of multivalent binding is "superselectivity". Superselectivity refers to the sharp discrimination of surfaces (e.g., on cells or cell compartments) by their comparative surface densities of a given receptor. This feature is different from the conventional "type" selectivity, which discriminates surfaces by their distinct receptor types. In a broader definition, a probe is superselective if it converts a gradual change in any one interaction parameter into a sharp on/off dependency in probe binding.This Account describes our systematic experimental and theoretical efforts over the past decade to analyze the determinants of superselective binding. It aims to offer chemical biologists, biophysicists, biologists, and biomedical scientists a set of guidelines for the interpretation of multivalent binding data, and design rules for tuning superselective targeting. We first provide a basic introduction that identifies multiple low-affinity interactions and combinatorial entropy as the minimal set of conditions required for superselective recognition. We then introduce the main experimental and theoretical tools and analyze how salient features of the multivalent probes (i.e., their concentration, size, ligand valency, and scaffold type), of the surface receptors (i.e., their affinity for ligands, surface density, and mobility), and of competitors and cofactors (i.e., their concentration and affinity for the ligands and/or receptors) influence the sharpness and the position of the threshold for superselective recognition.Emerging from this work are a set of relatively simple yet quantitative data analysis guidelines and superselectivity design rules that apply to a broad range of probe types and interaction systems. The key finding is the scaling variable xS which faithfully predicts the influence of the surface receptor density, probe ligand valency, receptor-ligand affinity, and competitor/cofactor concentrations and affinities on superselective recognition. The scaling variable is a simple yet versatile tool to quantitatively tune the on/off threshold of superselective probes. We exemplify its application by reviewing and reinterpreting literature data for selected biological and biomedical interaction systems where superselectivity clearly is important.Our guidelines can be deployed to generate a new mechanistic understanding of multivalent recognition events inside and outside cells and the downstream physiological/pathological implications. Moreover, the design rules can be harnessed to develop novel superselective probes for analytical purposes in the life sciences and for diagnostic/therapeutic intervention in biomedicine.

Routine EWS Fusion Analysis in the Oncology Clinic to Identify Cancer-Specific Peptide Sequence Patterns That Span Breakpoints in Ewing Sarcoma and DSRCT.

(1) Background: EWS fusion genes are associated with Ewing sarcoma and other Ewing family tumors including desmoplastic small round tumor, DSRCT. We utilize a clinical genomics workflow to reveal real-world frequencies of EWS fusion events, cataloging events that are similar, or divergent at the EWS breakpoint. (2) Methods: EWS fusion events from our next-generation sequencing panel (NGS) samples were first sorted by breakpoint or fusion junctions to map out the frequency of breakpoints. Fusion results were illustrated as in-frame fusion peptides involving EWS and a partner gene. (3) Results: From 2471 patient pool samples for fusion analysis at the Cleveland Clinic Molecular Pathology Laboratory, we identified 182 fusion samples evolved with the EWS gene. They are clustered in several breakpoints: chr22:29683123 (65.9%), and chr22:29688595 (2.7%). About 3/4 of Ewing sarcoma and DSRCT tumors have an identical EWS breakpoint motif at Exon 7 (SQQSSSYGQQ-) fused to a specific part of FLI1 (NPSYDSVRRG or-SSLLAYNTSS), ERG (NLPYEPPRRS), FEV (NPVGDGLFKD) or WT1 (SEKPYQCDFK). Our method also worked with Caris transcriptome data, too. Our primary clinical utility is to use this information to identify neoantigens for therapeutic purposes. (4) Conclusions and future perspectives: our method allows interpretation of what peptides result from the in-frame translation of EWS fusion junctions. These sequences, coupled with HLA-peptide binding data, are used to identify potential sequences of cancer-specific immunogenic peptides for Ewing sarcoma or DSRCT patients. This information may also be useful for immune monitoring (e.g., circulating T-cells with fusion-peptide specificity) to detect vaccine candidates, responses, or residual disease.

Barbiturates and pyrazolopyridines for the treatment of postpartum depression-repurposing of two drug classes.

Zulresso (brexanolone) is an aqueous formulation of the neurosteroid, allopregnanolone, and the only FDA-approved medication for the treatment of postpartum depression (PPD). While brexanolone is effective for the treatment of PPD, lengthy infusion time and high cost can be prohibitive. Failure of GABAA receptors to adapt to fluctuating neurosteroid levels is considered to predispose women to mood disorders in the postpartum period. Brexanolone is thought to act via stimulation of δ subunit-containing GABAA receptors, which are extrasynaptic and localized to particular brain regions. Neurosteroid stimulation of δ subunit-containing GABAA receptors leads to sustained inhibition (hyperpolarization) of GABAergic neurons, which makes δ subunit-containing GABAA receptors a potentially important pharmacologic target. Barbiturates and pyrazolopyridines are potent stimulators of δ subunit-containing GABAA receptors and therefore potentially cost-effective treatments for PPD. Barbiturates are often not prescribed, owing to risk of dependence and respiratory depression. The pyrazolopyridines were tested several decades ago for anxiety and depression but never developed commercially. Herein we use the FDA-approved dosing schedule of brexanolone and GABAA receptor binding data from various animal models to examine the safety, efficacy, and potential clinical utility of barbiturates and pyrazolopyridines for the treatment of PPD. We suggest consideration of repurposing barbiturates and pyrazolopyridines as safe and readily available treatment alternatives for PPD.

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks

Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.

Ligand Screening System for the RXRα Heterodimer Using the Fluorescence RXR Agonist CU-6PMN.

Retinoid X receptor (RXR), a nuclear receptor (NR) that regulates transcription of target genes in a ligand binding-dependent manner, is of interest as a drug target. RXR agonists have been developed as therapeutic agents for cutaneous invasive T-cell lymphoma (e.g., bexarotene (1)) and investigated as potential anti-inflammatory agents. Screening systems for the binding of RXR alone have been reported. However, although RXRs function as RXR heterodimers, information on systems to evaluate the differential binding of RXR agonists as RXR heterodimers has not been available until recently. Here we show that the fluorescent RXR agonist CU-6PMN (3), designed by our group, can be useful for assessing RXR binding to PPARγ/RXRα, and that the binding data differ from those of RXRα alone. This screening method opens a new avenue for binding assays for RXR heterodimers.

A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli.

Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.

Mathematical modeling and biochemical analysis support partially ordered calmodulin-myosin light chain kinase binding

Activation of myosin light chain kinase (MLCK) by calcium ions (Ca2+) and calmodulin (CaM) plays an important role in numerous cellular functions including vascular smooth muscle contraction and cellular motility. Despite extensive biochemical analysis, aspects of the mechanism of activation remain controversial, and competing theoretical models have been proposed for the binding of Ca2+ and CaM to MLCK. The models are analytically solvable for an equilibrium steady state and give rise to distinct predictions that hold regardless of the numerical values assigned to parameters. These predictions form the basis of a recently proposed, multi-part experimental strategy for model discrimination. Here we implement this strategy by measuring CaM-MLCK binding using an invitro FRET system. Interpretation of binding data in light of the mathematical models suggests a partially ordered mechanism for binding CaM to MLCK. Complementary data collected using orthogonal approaches that assess CaM-MLCK binding further support this conclusion.