Unrelated Targets Research Articles

A conformational switch-controlled RNA sensor based on orthogonal dCas12a for RNA imaging in live cells.

RNA imaging technology is essential for understanding the complex RNA regulatory mechanisms and serves as a powerful tool for disease diagnosis. However, conventional RNA imaging methods often require multiple fluorescent tags for the specific labeling of individual targets, complicating both the imaging process and subsequent analysis. Herein, we develop an RNA sensor that integrates a blocked CRISPR RNA (crRNA)-based conformational switch with a controllable CRISPR activation (CRISPRa) system and apply for RNA imaging. By leveraging nuclease-inactive Cas12a (dCas12a)-mediated processing of precursor crRNA (pre-crRNA) and the orthogonality of dCas12a from different bacteria, our sensor establishes an artificial link between two unrelated RNA targets, enabling cells to sense one RNA target and image another with a single fluorescent signal. By visualizing a single target for dual-target analysis, our method significantly reduces the reliance on multiple fluorescent tags. Our sensor provides a new platform for RNA imaging, enhancing both biomedical research and the development of advanced molecular diagnostics.

Exploring the Utility of the Muse Headset for Capturing the N400: Dependability and Single-Trial Analysis.

Consumer-grade EEG devices, such as the InteraXon Muse 2 headband, present a promising opportunity to enhance the accessibility and inclusivity of neuroscience research. However, their effectiveness in capturing language-related ERP components, such as the N400, remains underexplored. This study thus aimed to investigate the feasibility of using the Muse 2 to measure the N400 effect in a semantic relatedness judgment task. Thirty-seven participants evaluated the semantic relatedness of word pairs while their EEG was recorded using the Muse 2. Single-trial ERPs were analyzed using robust Yuen t-tests and hierarchical linear modeling (HLM) to assess the N400 difference between semantically related and unrelated target words. ERP analyses indicated a significantly larger N400 effect in response to unrelated word pairs over the right frontal electrode. Additionally, dependability estimates suggested acceptable internal consistency for the N400 data. Overall, these findings illustrate the capability of the Muse 2 to reliably measure the N400 effect, reinforcing its potential as a valuable tool for language research. This study highlights the potential of affordable, wearable EEG technology to expand access to brain research by offering an affordable and portable way to study language and cognition in diverse populations and settings.

Identify Diabetes-related Targets based on ForgeNet_GPC.

Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development. Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors. In this study, a disease target identification algorithm based on protein recognition is proposed. In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors. A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins. In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

Lexicosemantic prediction in native speakers of English and Swedish-speaking learners of english: An event-related potential study.

The present study uses event-related potentials (ERPs) to investigate lexicosemantic prediction in native speakers (L1) of English and advanced second language (L2) learners of English with Swedish as their L1. The main goal of the study was to examine whether learners recruit predictive mechanisms to the same extent as L1 speakers when a change in the linguistic environment renders prediction a useful strategy to pursue. The study, which uses a relatedness proportion paradigm adapted from Lau et al. (2013), focuses on the N400, an ERP component that is sensitive to the ease of lexical access/retrieval, including lexical prediction. Participants read 800 prime-target pairs, presented word by word and divided into two blocks, while they searched for animal words. Unknown to them, some of the pairs were semantically associated, which is known to reduce the amplitude of the N400 via spreading semantic activation. Most importantly, the proportion of semantically related pairs increased in the second experimental block (via fillers), thereby increasing the reliability of the primes as predictive cues and encouraging prediction. Results from 36 L1-English speakers and 53 L2 learners showed an N400 reduction for related (remain-stay ) relative to unrelated targets (silver-stay ) across blocks. Crucially, this N400 reduction for related targets was significantly larger in the block that encouraged prediction, in both L1 and L2 speakers, consistent with the possibility that both groups recruited similar predictive mechanisms when the context encouraged prediction. These results suggest that, at high levels of proficiency, L2 speakers engage similar predictive strategies to L1 speakers. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Is That You I Hear? Speaker Familiarity Modulates Neural Signatures of Lexical-semantic Activation in 18-month-old Infants.

Developmental language studies have shown that lexical-semantic organization develops between 18 and 24 months of age in monolingual infants. In the present study, we aimed to examine whether voice familiarity facilitates lexical-semantic activation in the infant brain. We recorded the brain activity of 18-month-old, French-learning infants using EEG while they listened to taxonomically related and unrelated spoken word pairs by one voice with which they were familiarized with before the experiment, and one voice with which they were not familiarized. The ERPs were measured in response to related and unrelated target words. Our results showed an N400 effect (greater amplitudes for unrelated as opposed to related target words) over the left hemisphere, only for the familiar voice, suggesting that the voice familiarity facilitated lexical-semantic activation. For unfamiliar voices, we observed an earlier congruence effect (greater amplitudes for related than for unrelated target words). This suggests that although 18-month-olds process lexical-semantic information from unfamiliar speakers, their neural signatures of lexical-semantic processing are less mature. Our results show that even in the absence of personal relation with a speaker, familiarity with a voice augments infant lexical-semantic processing. This supports the idea that extralinguistic information plays a role in infant lexical-semantic activation.

PTGS2 as target of compound Huangbai liquid in the nursing of pressure ulcer.

Pressure ulcer refers to ulceration and necrosis caused by local skin and cell tissues being compressed for a long time, continuous ischemia, hypoxia, and malnutrition. However, role of prostaglandin-endoperoxide synthase 2 (PTGS2) in the management of pressure ulcers in with compound Huangbai liquid is still unclear. Traditional Chinese medicine components and related targets of compound Huangbai liquid were collected through traditional Chinese medicine systems pharmacology (TCMSP) and Batman-traditional Chinese medicine database. Disease-related targets were obtained using the Gene Cards database. The protein-protein interaction (PPI) network was constructed using the Search tool for retrieval of interacting genes (STRING) and analyzed by Cytoscape to obtain the core components. To evaluate the clinical efficacy of the compound Huangbai liquid in the treatment of pressure ulcers, 40 patients with pressure ulcers were selected and divided into an observation group and a control group, with 20 individuals in each group. The observation group received treatment with compound Huangbai liquid. Sixty-five components and 480 targets of compound Huangbai liquid were obtained from TCMSP and Batman - traditional Chinese medicine databases. Two hundred seventy-three pressure ulcer-related targets were obtained. Seventy-two potential targets of compound Huangbai pigment in treatment of pressure ulcer were obtained, and 2 unrelated targets were deleted. There were 70 nodes and 1167 edges in PPI network. Gene ontology (GO) function is involved in biological processes such as reactive oxygen species metabolism and cellular response to chemical stress. Cellular components such as platelet α granules lumen and membrane rafts were involved. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results showed that compound Huangbai liquid in treatment of pressure ulcer. The clinical results indicate that the compound Huangbai liquid has a good therapeutic effect on pressure ulcers. PTGS2 may be a target for treatment of pressure ulcers with compound Huangbai liquid, providing a new direction for its treatment.

Covalent adduct formation of histone with organophosphorus pesticides in vitro

It is established that organophosphorus pesticide (OPP) toxicity results from modification of amino acids in active sites of target proteins. OPPs can also modify unrelated target proteins such as histones and such covalent histone modifications can alter DNA-binding properties and lead to aberrant gene expression. In the present study, we report on non-enzymatic covalent modifications of calf thymus histones adducted to selected OPPs and organophosphate flame retardants (OPFRs) in vitro using a bottom-up proteomics method approach. Histones were not found to form detectable adducts with the two tested OPFRs but were avidly modified by a few of the seven OPPs that were tested in vitro. Dimethyl phosphate (or diethyl phosphate) adducts were identified on Tyr, Lys and Ser residues. Most of the dialkyl phosphate adducts were identified on Tyr residues. Methyl and ethyl modified histones were also detected. Eleven amino residues in histones showed non-enzymatic covalent methylation by exposure of dichlorvos and malathion. Our bottom-up proteomics approach showing histone-OPP adduct formation warrants future studies on the underlying mechanism of chronic illness from exposure to OPPs.

Circadian modulation of the time course of automatic and controlled semantic processing

ABSTRACT We investigated whether chronotype and time-of-day modulate the time course of automatic and controlled semantic processing. Participants performed a category semantic priming task at either the optimal or non-optimal time of day. We varied the prime-target onset asynchrony (100-, 450-, 650-, and 850-ms SOAs) and kept the percentage of unrelated targets constant at 80%. Automatic processing was expected with the short SOA, and controlled processing with longer SOAs. Intermediate-types (Experiment 1) verified that our task was sensitive to capturing both types of processes and served as a reference to assess themin extreme chronotypes. Morning-type and evening-type participants (Experiment 2) differed in the influence of time of testing on priming effects. Morning-types applied control in all conditions, and no performance modulation by time-of-day was observed. In contrast, evening-types were most adversely affected by the time of day to shift from automatic-based to controlled-based responses. Also, they were considerably affected in successfully implementing controlled processing with long intervals, particularly at the non-optimal time of day, with inhibitory priming showing only a marginally significant effect at the longest SOA. These results suggest that extreme chronotypes may be associated with different styles of cognitive control. Morning-types would be driven by a proactive control style, whereas a reactive control style might be applied by evening-types.

Visual-linguistic-stylistic Triple Reward for Cross-lingual Image Captioning

Generating image captions in different languages is worth exploring and essential for non-native speakers. Nevertheless, collecting paired annotation for every language is time-consuming and impractical, particularly for minor languages. To this end, the cross-lingual image captioning task is proposed, which leverages existing image-source caption annotation data and wild unrelated target corpus to generate satisfactory caption in the target language. Current methods perform a two-step translation process of image-to-pivot (source) and pivot-to-target. The distinct two-step process comes with certain caption issues, such as the weak semantic alignment between the image and the generated caption and the generated caption’s non-target language style. To address these issues, we propose an end-to-end reinforce learning framework with Visual-linguistic-stylistic Triple Reward named TriR. In TriR, we jointly consider the visual, linguistic, and stylistic alignments to generate factual, fluent, and natural caption in the target language. To be specific, the image-source caption annotation provides factual semantic guidance, whereas the unrelated target corpus guides the language style of generated caption. To achieve this, we construct a visual reward module to measure the cross-modal semantic embedding of image and target caption, a linguistic reward module to measure the cross-linguistic embedding of source and target captions, and a stylistic reward module to imitate the presentation style of target corpus. The TriR can be implemented with either classical CNN-LSTM or prevalent Transformer architecture. Extensive experiments are conducted with four cross-lingual settings, i.e., Chinese-to-English, English-to-Chinese, English-to-German, and English-to-French. Experimental results demonstrate the remarkable superiority of our method, and sufficient ablation experiments validate the beneficial impact of every reward.

Mechanistic Insight into Poly-Reactivity of Immune Antibodies upon Acid Denaturation or Arginine Mutation in Antigen-Binding Regions.

The poly-reactivity of antibodies is defined as their binding to specific antigens as well as to related proteins and also to unrelated targets. Poly-reactivity can occur in individual molecules of natural serum antibodies, likely due to their conformation flexibility, and, for therapeutic antibodies, it plays a critical role in their clinical development. On the one hand, it can enhance their binding to target antigens and cognate receptors, but, on the other hand, it may lead to a loss of antibody function by binding to off-target proteins. Notably, poly-reactivity has been observed in antibodies subjected to treatments with dissociating, destabilizing or denaturing agents, in particular acidic pH, a common step in the therapeutic antibody production process involving the elution of Protein-A bound antibodies and viral clearance using low pH buffers. Additionally, poly-reactivity can emerge during the affinity maturation in the immune system, such as the germinal center. This review delves into the underlying potential causes of poly-reactivity, highlighting the importance of conformational flexibility, which can be further augmented by the acid denaturation of antibodies and the introduction of arginine mutations into the complementary regions of antibody-variable domains. The focus is placed on a particular antibody's acid conformation, meticulously characterized through circular dichroism, differential scanning calorimetry, and sedimentation velocity analyses. By gaining a deeper understanding of these mechanisms, we aim to shed light on the complexities of antibody poly-reactivity and its implications for therapeutic applications.

Modality-specific brain representations during automatic processing of face, voice and body expressions.

A central question in affective science and one that is relevant for its clinical applications is how emotions provided by different stimuli are experienced and represented in the brain. Following the traditional view emotional signals are recognized with the help of emotion concepts that are typically used in descriptions of mental states and emotional experiences, irrespective of the sensory modality. This perspective motivated the search for abstract representations of emotions in the brain, shared across variations in stimulus type (face, body, voice) and sensory origin (visual, auditory). On the other hand, emotion signals like for example an aggressive gesture, trigger rapid automatic behavioral responses and this may take place before or independently of full abstract representation of the emotion. This pleads in favor specific emotion signals that may trigger rapid adaptative behavior only by mobilizing modality and stimulus specific brain representations without relying on higher order abstract emotion categories. To test this hypothesis, we presented participants with naturalistic dynamic emotion expressions of the face, the whole body, or the voice in a functional magnetic resonance (fMRI) study. To focus on automatic emotion processing and sidestep explicit concept-based emotion recognition, participants performed an unrelated target detection task presented in a different sensory modality than the stimulus. By using multivariate analyses to assess neural activity patterns in response to the different stimulus types, we reveal a stimulus category and modality specific brain organization of affective signals. Our findings are consistent with the notion that under ecological conditions emotion expressions of the face, body and voice may have different functional roles in triggering rapid adaptive behavior, even if when viewed from an abstract conceptual vantage point, they may all exemplify the same emotion. This has implications for a neuroethologically grounded emotion research program that should start from detailed behavioral observations of how face, body, and voice expressions function in naturalistic contexts.

Incidental recognition reveals attentional tradeoffs shaped by categorical similarity.

Search efficiency suffers when observers look for multiple targets or a single imprecisely defined target. These conditions prevent a narrow target template, resulting in improved delayed distractor recognition. In our first experiment with hybrid visual and memory search, we investigated the interaction of target variety and target number on search efficiency. Results supported the hypothesis that numerous targets impair search efficiency much more when targets are unrelated. These efficiency impairments were linked to distractor processing, indicated by increased delayed recognition. A second experiment manipulated target-distractor similarity to determine whether prioritization of target-defining features is totally eliminated in search for eight unrelated targets. For related and unrelated targets alike, recognition declined for distractors bearing less resemblance to targets and more to each other. This suggests templates for unrelated targets successfully prioritize relevant features at some stage of attention. Avoidance of random distractors was stronger when targets were related, at the price of slower, more error-prone identification of within-category distractors. Within-category processing difficulty for related targets likely stems from categorical interference as previously demonstrated in recognition memory. Thus, target variety versus homogeneity afforded different advantages and limitations depending on target number, target-distractor, and distractor-distractor resemblance. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Acquisition Relatedness in Family Firms: Do the Environment and the Institutional Context Matter?

AbstractResearch on the acquisition behaviour of family firms has produced conflicting theoretical arguments and mixed empirical findings on their propensity to acquire related or unrelated targets. While previous work has mainly focused on firm‐level variables, this study examines the environment in which family firms operate and the institutional context where acquisitions take place. Drawing on the mixed gambles logic of the behavioural agency model, we theorize that family firms are more likely than nonfamily firms to undertake related acquisitions when they operate in uncertain environments to avoid losses to the family's current socioemotional wealth. However, family firms are more likely to undertake unrelated acquisitions, when the environment is uncertain but the target operates in a similar and more developed institutional context where prospective financial gains are more predictable. Overall, building on a sample of 1014 international acquisitions, our study offers important contributions to the literature on family firms and acquisitions.

A tiny loop in the Argonaute PIWI domain tunes small RNA seed strength

Argonaute (AGO) proteins use microRNAs (miRNAs) and small interfering RNAs (siRNAs) as guides to regulate gene expression in plants and animals. AGOs that use miRNAs in bilaterian animals recognize short (6–8 nt.) elements complementary to the miRNA seed region, enabling each miRNA to interact with hundreds of otherwise unrelated targets. By contrast, AGOs that use miRNAs in plants employ longer (> 13 nt.) recognition elements such that each miRNA silences a small number of physiologically related targets. Here, we show that this major functional distinction depends on a minor structural difference between plant and animal AGO proteins: a 9‐amino acid loop in the PIWI domain. Swapping the PIWI loop from human Argonaute2 (HsAGO2) into Arabidopsis Argonaute10 (AtAGO10) increases seed strength, resulting in animal‐like miRNA targeting. Conversely, swapping the plant PIWI loop into HsAGO2 reduces seed strength and accelerates the turnover of cleaved targets. The loop‐swapped HsAGO2 silences targets more potently, with reduced miRNA‐like targeting, than wild‐type HsAGO2 in mammalian cells. Thus, tiny structural differences can tune the targeting properties of AGO proteins for distinct biological roles.

Why do judgments of learning modify memory? Evidence from identical pairs and relatedness judgments.

Research has observed that monitoring one's own learning modifies memory for some materials but not for others. Specifically, making judgments of learning (JOLs) while learning word pairs improves subsequent cued-recall memory performance for related word pairs but not for unrelated word pairs. Theories that have attempted to explain this pattern of results assume that people attend to and process cue-target relatedness during learning more when making JOLs than they spontaneously do when not making JOLs. The present research directly tested this relatedness-processing assumption with unrelated and related word pairs as well as with hitherto unexamined materials: identical word pairs. In three experiments, participants studied word pairs while either making or not making JOLs. Results revealed that making JOLs improved memory for related word pairs as well as for identical word pairs, but not for unrelated word pairs. Importantly, in two of the experiments, participants were further asked to judge at test whether each cue appeared with an unrelated, related, or identical target before attempting to recall it. Results revealed that making JOLs improved the accuracy of these relatedness judgments independently from its effect on recall, thus providing direct evidence that people process cue-target relatedness when making JOLs more than they spontaneously do when not making JOLs. These findings contribute to a more comprehensive understanding of why judging one's own learning can modify memory and, more broadly, suggest that instructions to monitor learning can direct people's attention to information that is not or less processed otherwise. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Network-medicine approach for the identification of genetic association of parathyroid adenoma with cardiovascular disease and type-2 diabetes.

Primary hyperparathyroidism is caused by solitary parathyroid adenomas (PTAs) in most cases (⁓85%), and it has been previously reported that PTAs are associated with cardiovascular disease (CVD) and type-2 diabetes (T2D). To understand the molecular basis of PTAs, we have investigated the genetic association amongst PTAs, CVD and T2D through an integrative network-based approach and observed a remarkable resemblance. The current study proposed to compare the PTAs-associated proteins with the overlapping proteins of CVD and T2D to determine the disease relationship. We constructed the protein-protein interaction network by integrating curated and experimentally validated interactions in humans. We found the $11$ highly clustered modules in the network, which contain a total of $13$ hub proteins (TP53, ESR1, EGFR, POTEF, MEN1, FLNA, CDKN2B, ACTB, CTNNB1, CAV1, MAPK1, G6PD and CCND1) that commonly co-exist in PTAs, CDV and T2D and reached to network's hierarchically modular organization. Additionally, we implemented a gene-set over-representation analysis over biological processes and pathways that helped to identify disease-associated pathways and prioritize target disease proteins. Moreover, we identified the respective drugs of these hub proteins. We built a bipartite network that helps decipher the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drug combinations or drug-repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs and give a synergistic effect with better outcomes. This network-based analysis opens a new horizon for more personalized treatment and drug-repurposing opportunities to investigate new targets and multi-drug treatment and may be helpful in further analysis of the mechanisms underlying PTA and associated diseases.

Development of machine learning models for prediction of antibody non-specificity.

For the development of therapeutic antibodies, it is acknowledged that optimal target binding affinity should be accompanied by high specificity. In recent years, there has been an increased focus on lowering non-specific binding during early-stage drug development. In particular, non-specificity has been recognised as a root-cause for failure in numerous antibody-based drug programs due to unexpected pharmacokinetics and elevated toxicity. Hence, there is a need for predictive tools for flagging and deselecting non-specific antibodies at an early stage to mitigate failure at clinical late-stage development. From a computational design perspective, prediction has remained a challenge as non-specificity is currently a poorly understood biophysical phenomena, typically involving weak interactions to various, unrelated targets and surfaces. In this work, we describe the development of sequence-based machine learning (ML) models for prediction of antibody non-specificity. A dataset of >1000 antibody sequences paired with their respective non-specificity flag, defined by ELISA using a panel of common antigens, were retrieved from public sources (Jain et al., 2017; Boughter et al., 2020). The sequences were embedded using state-of-the-art protein language models for representation of their physiochemical properties, whereafter the vectorized embeddings were served as features for training of binary classifiers for non-specificity flags. A total of 12 different antibody domain-specific models were generated. Highest predictability of non-specificity was obtained for ML algorithms trained on the heavy-chain variable domain (VH), and the complementarity determining regions (CDRs), and resulted in a mean accuracy of 73% across 3-, 5-, and 10-fold cross-validations. The classification probability of non-specificity followed an uptrend with the experimental data (non-specificity flags), thereby mimicking regression behaviour. Altogether, our work demonstrates feasibility for prediction of non-specific binding from the antibody sequence, and that the non-specificity primarily stems from the VH domain, with main contributions from the heavy-chain CDRs.

Fragment-based screening by protein-detected NMR spectroscopy.

Fragment-based drug discovery (FBDD) identifies low molecular weight compounds that can be developed into ligands with high affinity and selectivity for therapeutic targets. Screening fragment libraries (<10,000 molecules) with biophysical techniques against macromolecules provides information about novel chemical spacesthat bind the macromolecule and scaffolds that can be modified to increase potency. A fragment-screening pipeline requires a standardized protocol for target selection, library assembly and maintenance, library screening, and hit validation to ensure hit integrity. Herein, the fundamental aspects of a fragment screening pipeline-focusing on protein-detected NMR data collection and analysis-are discussed in detail for researchers to use as a resource in their FBDD projects. Selected screening targets must undergo rigorous stability and buffer testing by NMR spectroscopy to ensure the protein structure is stable for the entire screen. Biophysical instrumentation that rapidly measures protein thermostability is helpful in buffer screening. Molecules in fragment libraries are analyzed computationally and physically, stored at appropriate temperatures, and multiplexed in well plates for library conservation. The screening protocol is streamlined using liquid handling robotics for sample preparation and customized Python scripts for protein-detected NMR data analysis. Molecules identified from the screen are titrated to determine their binding site(s) and Kd values and confirmed with an orthogonal biophysical assay. This detailed FBDD screening pipeline developed by the Program in Chemical Biology at the Medical College of Wisconsin has successfully screened many unrelated target proteins to identified novel molecules that selectively bind to these target proteins.

CaRA – A multi-purpose phage display library for selection of calcium-regulated affinity proteins

Protein activity regulated by interactions with metal ions can be utilized for many different purposes, including biological therapies and bioprocessing, among others. Calcium ions are known to interact with the frequently occurring EF-hand motif, which can alter protein activity upon binding through an induced conformational change. The calcium-binding loop of the EF-hand motif has previously been introduced into a small protein domain derived from staphylococcal Protein A in a successful effort to render antibody binding dependent on calcium. Presented here, is a combinatorial library for calcium-regulated affinity, CaRA, based on this domain. CaRA is the first alternative scaffold library designed to achieve novel target specificities with metal-dependent binding. From this library, several calcium-dependent binders could be isolated through phage display campaigns towards a set of unrelated target proteins (IgE Cε3-Cε4, TNFα, IL23, scFv, tPA, PCSK9 and HER3) useful for distinct applications. Overall, these monomeric CaRA variants showed high stability and target affinities within the nanomolar range. They displayed considerably higher melting temperatures in the presence of 1 mM calcium compared to without calcium. Further, all discovered binders proved to be calcium-dependent, with the great majority showing complete lack of target binding in the absence of calcium. As demonstrated, the CaRA library is highly capable of providing protein-binding domains with calcium-dependent behavior, independent of the type of target protein. These binding domains could subsequently be of great use in gentle protein purification or as novel therapeutic modalities.