High-throughput Affinity Research Articles

Unveiling the hidden interactome of CRBN molecular glues with chemoproteomics.

Targeted protein degradation and induced proximity refer to strategies that leverage the recruitment of proteins to facilitate their modification, regulation or degradation. As prospective design of glues remains challenging, unbiased discovery methods are needed to unveil hidden chemical targets. Here we establish a high throughput affinity purification mass spectrometry workflow in cell lysates for the unbiased identification of molecular glue targets. By mapping the targets of 20 CRBN-binding molecular glues, we identify 298 protein targets and demonstrate the utility of enrichment methods for identifying novel targets overlooked using established methods. We use a computational workflow to estimate target confidence and perform a biochemical screen to identify a lead compound for the new non-ZF target PPIL4. Our study provides a comprehensive inventory of targets chemically recruited to CRBN and delivers a robust and scalable workflow for identifying new drug-induced protein interactions in cell lysates.

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.

Sample-Treatment with the Virucidal β-Propiolactone Does Not Preclude Analysis by Large Panel Affinity Proteomics, Including the Discovery of Biomarker Candidates.

Virus inactivation is a prerequisite for safe handling of high-risk infectious samples. β-Propiolactone (BPL) is an established reagent with proven virucidal efficacy. BPL primarily reacts with DNA, RNA, and amino acids. The latter may modify antigenic protein epitopes interfering with binding properties of affinity reagents such as antibodies and aptamers used in affinity proteomic screens. We investigated (i) the impact of BPL treatment on the analysis of protein levels in plasma samples using the aptamer-based affinity proteomic platform SomaScan and (ii) effects on protein detection in conditioned medium samples using the proximity extension assay-based Olink Target platform. In the former setup, BPL-treated and native plasma samples from patients with ovarian cancer (n = 12) and benign diseases (n = 12) were analyzed using the SomaScan platform. In the latter, conditioned media samples collected from cultured T cells with (n = 3) or without (n = 3) anti-CD3 antibody stimulation were analyzed using the Olink Target platform. BPL-related changes in protein detection were evaluated comparing native and BPL-treated states, simulating virus inactivation, and impact on measurable group differences was assessed. While approximately one-third of SomaScan measurements were significantly changed by the BPL treatment, a majority of antigen/aptamer interactions remained unaffected. Interaction effects of BPL treatment and disease state, potentially altering detectability of group differences, were observable for less than one percent of targets (0.6%). BPL effects on protein detection with Olink Target were also limited, affecting 3.6% of detected proteins with no observable interaction effects. Thus, effects of BPL treatment only moderately interfere with affinity proteomic detectability of differential protein expression between different experimental groups. Overall, the results prove high-throughput affinity proteomics well suited for the analysis of high-risk samples inactivated using BPL.

Azithromycin, a potent autophagy inhibitor for cancer therapy, perturbs cytoskeletal protein dynamics

BackgroundAutophagy plays an important role in tumour cell growth and survival and also promotes resistance to chemotherapy. Hence, autophagy has been targeted for cancer therapy. We previously reported that macrolide antibiotics including azithromycin (AZM) inhibit autophagy in various types of cancer cells in vitro. However, the underlying molecular mechanism for autophagy inhibition remains unclear. Here, we aimed to identify the molecular target of AZM for inhibiting autophagy.MethodsWe identified the AZM-binding proteins using AZM-conjugated magnetic nanobeads for high-throughput affinity purification. Autophagy inhibitory mechanism of AZM was analysed by confocal microscopic and transmission electron microscopic observation. The anti-tumour effect with autophagy inhibition by oral AZM administration was assessed in the xenografted mice model.ResultsWe elucidated that keratin-18 (KRT18) and α/β-tubulin specifically bind to AZM. Treatment of the cells with AZM disrupts intracellular KRT18 dynamics, and KRT18 knockdown resulted in autophagy inhibition. Additionally, AZM treatment suppresses intracellular lysosomal trafficking along the microtubules for blocking autophagic flux. Oral AZM administration suppressed tumour growth while inhibiting autophagy in tumour tissue.ConclusionsAs drug-repurposing, our results indicate that AZM is a potent autophagy inhibitor for cancer treatment, which acts by directly interacting with cytoskeletal proteins and perturbing their dynamics.

Evaluation of deep learning approaches for modeling transcription factor sequence specificity

As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identify TF binding sites and predict their affinities. Recently, deep learning (DL) algorithms have achieved promising results in the prediction of DNA-TF binding, however, various deep learning architectures have not been systematically compared, and the relative merit of each architecture remains unclear. To address this problem, we applied four different deep learning architectures to SELEX-seq and HT-SELEX data, covering three species and 35 families. We evaluated and compared the performance of different deep neural models using 10-fold cross-validation. Our results indicate that the hybrid CNN + DNN model shows the best performances. We expect that our study will be broadly applicable to modeling and predicting TF binding specificity when more high-throughput affinity data are available.

An Economical and Versatile High-Throughput Protein Purification System Using a Multi-Column Plate Adapter.

Protein purification is imperative to the study of protein structure and function and is usually used in combination with biophysical techniques. It is also a key component in the development of new therapeutics. The evolving era of functional proteomics is fueling the demand for high-throughput protein purification and improved techniques to facilitate this. It was hypothesized that a multi column plate adaptor (MCPA) can interface multiple chromatography columns of different resins with multi-well plates for parallel purification. This method offers an economical and versatile method of protein purification that can be used under gravity or vacuum, rivaling the speed of an automated system. The MCPA can be used to recover milligram yields of protein by an affordable and time efficient method for subsequent characterization and analysis. The MCPA has been used for high-throughput affinity purification of SH3 domains. Ion exchange has also been demonstrated via the MCPA to purify protein post Ni-NTA affinity chromatography, indicating how this system can be adapted to other purification types. Due to its setup with multiple columns, individual customization of parameters can be made in the same purification, unachievable by the current plate-based methods.

Computational modeling of microfluidic data provides high-throughput affinity estimates for monoclonal antibodies

Affinity measurement is a fundamental step in the discovery of monoclonal antibodies (mAbs) and of antigens suitable for vaccine development. Innovative affinity assays are needed due to the low throughput and/or limited dynamic range of available technologies.We combined microfluidic technology with quantum-mechanical scattering theory, in order to develop a high-throughput, broad-range methodology to measure affinity. Fluorescence intensity profiles were generated for out-of-equilibrium solutions of labelled mAbs and their antigen-binding fragments migrating along micro-columns with immobilized cognate antigen. Affinity quantification was performed by computational data analysis based on the Landau probability distribution.Experiments using a wide array of human or murine antibodies against bacterial or viral, protein or polysaccharide antigens, showed that all the antibody-antigen capture profiles (n = 841) generated at different concentrations were accurately described by the Landau distribution.A scale parameter W, proportional to the full-width-at-half-maximum of the capture profile, was shown to be independent of the antibody concentration. The W parameter correlated significantly (Pearson’s r [p–value]: 0.89 [3 × 10−8]) with the equilibrium dissociation constant KD, a gold-standard affinity measure.Our method showed good intermediate precision (median coefficient of variation: 5%) and a dynamic range corresponding to KD values spanning from ~10−7 to ~10−11 Molar. Relative to assays relying on antibody-antigen equilibrium in solution, even when they are microfluidic-based, the method’s turnaround times were decreased from 2 days to 2 h.The described computational modelling of antibody capture profiles represents a fast, reproducible, high-throughput methodology to accurately measure a broad range of antibody affinities in very low volumes of solution.

High-Throughput Affinity Measurements of Transcription Factor and DNA Mutations Reveal Affinity and Specificity Determinants.

Transcription factors (TFs) bind regulatory DNA to control gene expression, and mutations to either TFs or DNA can alter binding affinities to rewire regulatory networks and drive phenotypic variation. While studies have profiled energetic effects of DNA mutations extensively, we lack similar information for TF variants. Here, we present STAMMP (simultaneous transcription factor affinity measurements via microfluidic protein arrays), a high-throughput microfluidic platform enabling quantitative characterization of hundreds of TF variants simultaneously. Measured affinities for ∼210 mutants of a model yeast TF (Pho4) interacting with 9 oligonucleotides (>1,800 Kds) reveal that many combinations of mutations to poorly conserved TF residues and nucleotides flanking the core binding site alter but preserve physiological binding, providing a mechanism by which combinations of mutations in cis and trans could modulate TF binding to tune occupancies during evolution. Moreover, biochemical double-mutant cycles across the TF-DNA interface reveal molecular mechanisms driving recognition, linking sequence to function. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Development of a High-Throughput Affinity Mass Spectrometry (AMS) Platform Using Laser Diode Thermal Desorption Ionization Coupled to Mass Spectrometry (LDTD-MS).

Affinity selection mass spectrometry (MS) or, simply, affinity mass spectrometry (AMS) is a label-free technology that has been used to identify high-affinity ligands of target proteins of interest by screening against small-molecule compound libraries and identifying molecules that are enriched in the presence of the target protein. We have previously applied Agilent Technology's (Santa Clara, CA) RapidFire solid-phase extraction (SPE)-based high-throughput MS technology to screen small-molecule libraries using AMS. However, SPE-based technologies rely on fluidics for desalting and separation prior to mass analysis with attendant high solvent consumption, relatively high sample volume requirements, risk of sample carryover, and frequent maintenance. To address these challenges, we have established an AMS platform using a laser diode thermal desorption-atmospheric pressure chemical ionization (LDTD-APCI) ionization source (Phytronix, Quebec, Canada) coupled with a SCIEX 5600+ TripleTOF MS (Framingham, MA). We also validated a data-independent acquisition (DIA) Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) method for the robust detection and analysis of small-molecule affinity hits. An informatics platform developed in-house has resulted in a streamlined data analysis workflow for high-throughput AMS screening campaigns and reduced data processing time without compromising data quality. Finally, 68,000 compounds were screened in a single plate and affinity selected hits were confirmed in an orthogonal enzyme activity assay.

Abstract 1227: Identification of the molecular target of azithromycin as an autophagy inhibitor and its potential application in cancer therapeutics

Abstract Autophagy is a self-digestive cellular mechanism that enables survival under nutrient depleted conditions. In normal cells, autophagy has been known to prevent tumorigenesis by removal of genotoxicity-causing mitochondria, as well as transformed cells. In contrast, once cancer has been established, autophagy appears to play a cytoprotective role to adapt to the insufficiently vascularized hypoxic and low nutrient environment; and also works for metastasis and therapeutic resistance. Therefore, autophagic inhibitors could be suitable candidates for cancer therapeutics. However, the only two autophagic inhibitors clinically available at present are chloroquine (CQ) and hydroxychloroquine (HCQ). We previously reported that azithromycin (AZM), a macrolide antibiotic, has an inhibitory effect on autophagy and that combined administration of AZM with various anti-cancer drugs, such as a tyrosine kinase inhibitor and a proteasomal inhibitor, appears to enhance the anti-cancer effect. With an aim to explore the clinical application of AZM for cancer therapy, we attempted to identify the molecular target of AZM for autophagy inhibition. High throughput affinity purification using AZM-conjugated magnetic nano-beads was used to identify several candidates for the AZM-binding protein involved in autophagy inhibition from human NSCLC-derived A549 cell lysates. Knockdown of one of the candidate genes resulted in a prominent accumulation of autophagosomes and lysosomes, leading to autophagy inhibition. Fluorescence microscopic observation of A549 cells expressing GFP-LC3 and LAMP1-mCherry markers suggested that AZM suppressed the fusion between autophagosomes and lysosomes. Comparison of autophagy inhibitory capacity using the GFP-LC3/RFP-LC3ΔG fluorescent reporter system revealed an almost equivalent autophagic inhibition between AZM and HCQ but less cytotoxicity by itself at the same concentrations. Suppression of lysosomal acidification was not observed upon AZM administration as same as in the case of HCQ administration. However, unlike HCQ, AZM did not increase the levels of HIF-1α and phospho-ERK1/2. This indicates the existence of different autophagy inhibitory mechanisms for AZM and HCQ. In the A549 murine xenograft model, daily oral administration of AZM (100 μg/g/day) significantly suppressed tumor cell growth. These results from the present study encourage the use of AZM as an autophagy inhibitor for cancer therapy. Citation Format: Naoharu Takano, Yumiko Yamada, Mayumi Tokuhisa, Hirotsugu Hino, Masaki Hiramoto, Keisuke Miyazawa. Identification of the molecular target of azithromycin as an autophagy inhibitor and its potential application in cancer therapeutics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1227.

Characterisation of aptamer-anchored poly(EDMA-co-GMA) monolith for high throughput affinity binding

Immobilisation of aptameric ligands on solid stationary supports for effective binding of target molecules requires understanding of the relationship between aptamer-polymer interactions and the conditions governing the mass transfer of the binding process. Herein, key process parameters affecting the molecular anchoring of a thrombin-binding aptamer (TBA) onto polymethacrylate monolith pore surface, and the binding characteristics of the resulting macroporous aptasensor were investigated. Molecular dynamics (MD) simulations of the TBA-thrombin binding indicated enhanced Guanine 4 (G4) structural stability of TBA upon interaction with thrombin in an ionic environment. Fourier-transform infrared spectroscopy and thermogravimetric analyses were used to characterise the available functional groups and thermo-molecular stability of the immobilised polymer generated with Schiff-base activation and immobilisation scheme. The initial degradation temperature of the polymethacrylate stationary support increased with each step of the Schiff-base process: poly(Ethylene glycol Dimethacrylate-co-Glycidyl methacrylate) or poly(EDMA-co-GMA) [196.0 °C (±1.8)]; poly(EDMA-co-GMA)-Ethylenediamine [235.9 °C (±6.1)]; poly(EDMA-co-GMA)-Ethylenediamine-Glutaraldehyde [255.4 °C (±2.7)]; and aptamer-modified monolith [273.7 °C (±2.5)]. These initial temperature increments reflected in the associated endothermic energies were determined with differential scanning calorimetry. The aptameric ligand density obtained after immobilisation was 480 pmol/μL. Increase in pH and ionic concentration affected the surface charge distribution and the binding characteristics of the aptamer-modified disk-monoliths, resulting in the optimum binding pH and ionic concentration of 8.0 and 5 mM Mg2+, respectively. These results are critical in understanding and setting parametric constraints indispensable to develop and enhance the performance of aptasensors.

Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity.

MotivationNowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα).ResultsVS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted.Availability and implementation http://edmon.cbs.cnrs.fr.Supplementary information Supplementary data are available at Bioinformatics online.

Discovery of novel elongator protein 2 inhibitors by compound library screening using surface plasmon resonance.

Tumour necrosis factor-α (TNF-α) is a pleiotropic cytokine that becomes elevated in chronic inflammatory states, including slowing down osteogenic differentiation, which leads to bone dysplasia in long-term inflammatory microenvironments. The elongator complex plays a role in gene regulation and association with various cellular activities, including the downstream signal transduction of TNF-α in osteogenic cells. To find an inhibitor of Elongator Protein 2 (Elp2), we performed a compound library screen and verified the pharmaceutical effects of candidate compounds on the mouse myoblast cell (C2C12) and mouse osteoblastic cells (MC3T3-E1). The commercial FDA-approved drug (FD) library and the bioactive compound (BC) library were used as candidate libraries. After a label-free, high-throughput affinity measurement with surface plasmon resonance (SPRi), seven kinds of compounds showed binding affinity with mouse Elp2 protein. The seven candidates were then used to perform an inhibition test with TNF-α-induced C2C12 and MC3T3-E1 cell lines. One candidate compound reduced the differentiation suppression caused by TNF-α with resuscitated alkaline phosphatase (ALP) activity, mineralization intensity and expression of osteogenic differentiation marker genes. The results of our study provide a competitive candidate to mitigate the TNF-α-induced osteogenic differentia.

High-throughput binding affinity calculations at extreme scales

BackgroundResistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.ResultsWe demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.ConclusionsAs such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.

Levels of human proteins in plasma associated with acute paediatric malaria

BackgroundThe intimate interaction between the pathophysiology of the human host and the biology of the Plasmodium falciparum parasite results in a wide spectrum of disease outcomes in malaria. Development of severe disease is associated with a progressively augmented imbalance in pro- and anti-inflammatory responses to high parasite loads and sequestration of parasitized erythrocytes. Although these phenomena collectively constitute common denominators for the wide variety of discrete severe malaria manifestations, the mechanistic rationales behind discrepancies in outcome are poorly understood. Exploration of the human pathophysiological response by variations in protein profiles in plasma presents an excellent opportunity to increase the understanding. This is ultimately required for better prediction, prevention and treatment of malaria, which is essential for ongoing elimination and eradication efforts.ResultsAn affinity proteomics approach was used to analyse 541 paediatric plasma samples collected from community controls and patients with mild or severe malaria in Rwanda. Protein profiles were generated with an antibody-based suspension bead array containing 255 antibodies targetting 115 human proteins. Here, 57 proteins were identified with significantly altered levels (adjusted p-values < 0.001) in patients with malaria compared to controls. From these, the 27 most significant proteins (adjusted p-values < 10−14) were selected for a stringent analysis approach. Here, 24 proteins showed elevated levels in malaria patients and included proteins involved in acute inflammatory response as well as cell adhesion. The remaining three proteins, also implicated in immune regulation and cellular adhesivity, displayed lower abundance in malaria patients. In addition, 37 proteins (adjusted p-values < 0.05) were identified with increased levels in patients with severe compared to mild malaria. This set includes, proteins involved in tissue remodelling and erythrocyte membrane proteins. Collectively, this approach has been successfully used to identify proteins both with known and unknown association with different stages of malaria.ConclusionIn this study, a high-throughput affinity proteomics approach was used to find protein profiles in plasma linked to P. falciparum infection and malaria disease progression. The proteins presented herein are mainly involved in inflammatory response, cellular adhesion and as constituents of erythrocyte membrane. These findings have a great potential to provide increased conceptual understanding of host-parasite interaction and malaria pathogenesis.

Inferring secondary cell wall-associated transcriptional regulatory networks in Eucalyptus grandis through high-throughput DNA affinity purification sequencing

Inferring secondary cell wall-associated transcriptional regulatory networks in Eucalyptus grandis through high-throughput DNA affinity purification sequencing

PDGFB, a new candidate plasma biomarker for venous thromboembolism: results from the VEREMA affinity proteomics study

AbstractThere is a clear clinical need for high-specificity plasma biomarkers for predicting risk of venous thromboembolism (VTE), but thus far, such markers have remained elusive. Utilizing affinity reagents from the Human Protein Atlas project and multiplexed immuoassays, we extensively analyzed plasma samples from 2 individual studies to identify candidate protein markers associated with VTE risk. We screened plasma samples from 88 VTE cases and 85 matched controls, collected as part of the Swedish “Venous Thromboembolism Biomarker Study,” using suspension bead arrays composed of 755 antibodies targeting 408 candidate proteins. We identified significant associations between VTE occurrence and plasma levels of human immunodeficiency virus type I enhancer binding protein 1 (HIVEP1), von Willebrand factor (VWF), glutathione peroxidase 3 (GPX3), and platelet-derived growth factor β (PDGFB). For replication, we profiled plasma samples of 580 cases and 589 controls from the French FARIVE study. These results confirmed the association of VWF and PDGFB with VTE after correction for multiple testing, whereas only weak trends were observed for HIVEP1 and GPX3. Although plasma levels of VWF and PDGFB correlated modestly (ρ ∼ 0.30) with each other, they were independently associated with VTE risk in a joint model in FARIVE (VWF P < .001; PDGFB P = .002). PDGFΒ was verified as the target of the capture antibody by immunocapture mass spectrometry and sandwich enzyme-linked immunosorbent assay. In conclusion, we demonstrate that high-throughput affinity plasma proteomic profiling is a valuable research strategy to identify potential candidate biomarkers for thrombosis-related disorders, and our study suggests a novel association of PDGFB plasma levels with VTE.

Determination of equilibrium dissociation constants for recombinant antibodies by high-throughput affinity electrophoresis.

High-quality immunoreagents enhance the performance and reproducibility of immunoassays and, in turn, the quality of both biological and clinical measurements. High quality recombinant immunoreagents are generated using antibody-phage display. One metric of antibody quality – the binding affinity – is quantified through the dissociation constant (KD) of each recombinant antibody and the target antigen. To characterize the KD of recombinant antibodies and target antigen, we introduce affinity electrophoretic mobility shift assays (EMSAs) in a high-throughput format suitable for small volume samples. A microfluidic card comprised of free-standing polyacrylamide gel (fsPAG) separation lanes supports 384 concurrent EMSAs in 30 s using a single power source. Sample is dispensed onto the microfluidic EMSA card by acoustic droplet ejection (ADE), which reduces EMSA variability compared to sample dispensing using manual or pin tools. The KD for each of a six-member fragment antigen-binding fragment library is reported using ~25-fold less sample mass and ~5-fold less time than conventional heterogeneous assays. Given the form factor and performance of this micro- and mesofluidic workflow, we have developed a sample-sparing, high-throughput, solution-phase alternative for biomolecular affinity characterization.

Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments.

The ability to de novo design proteins that can bind small molecules has wide implications for synthetic biology and medicine. Combining computational protein design with the high-throughput screening of mutagenic libraries of computationally designed proteins is emerging as a general approach for creating binding proteins with programmable binding modes, affinities, and selectivities. The computational step enables the creation of a binding site in a protein that otherwise does not (measurably) bind the intended ligand, and targeted mutagenic screening allows for validation and refinement of the computational model as well as provides orders-of-magnitude increases in the binding affinity. Deep sequencing of mutagenic libraries can provide insights into the mutagenic binding landscape and enable further affinity improvements. Moreover, in such a combined computational-experimental approach where the binding mode is preprogrammed and iteratively refined, selectivity can be achieved (and modulated) by the placement of specified amino acid side chain groups around the ligand in defined orientations. Here, we describe the experimental aspects of a combined computational-experimental approach for designing-using the software suite Rosetta-proteins that bind a small molecule of choice and engineering, using fluorescence-activated cell sorting and high-throughput yeast surface display, high affinity and ligand selectivity. We illustrated the utility of this approach by performing the design of a selective digoxigenin (DIG)-binding protein that, after affinity maturation, binds DIG with picomolar affinity and high selectivity over structurally related steroids.

Affinity regression predicts the recognition code of nucleic acid-binding proteins.

Predicting the affinity profiles of nucleic acid-binding proteins directly from the protein sequence is a major unsolved problem. We present a statistical approach for learning the recognition code of a family of transcription factors (TFs) or RNA-binding proteins (RBPs) from high-throughput binding assays. Our method, called affinity regression, trains on protein binding microarray (PBM) or RNA compete experiments to learn an interaction model between proteins and nucleic acids, using only protein domain and probe sequences as inputs. By training on mouse homeodomain PBM profiles, our model correctly identifies residues that confer DNA-binding specificity and accurately predicts binding motifs for an independent set of divergent homeodomains. Similarly, learning from RNA compete profiles for diverse RBPs, our model can predict the binding affinities of held-out proteins and identify key RNA-binding residues. More broadly, we envision applying our method to model and predict biological interactions in any setting where there is a high-throughput ‘affinity’ readout.