One-pot green synthesis of ZIF-8/IgG composite for the precise orientation and protection of antibody and its application in purification and detection of aflatoxins in peanut oil.

This study presents a novel approach toward the one-pot green synthesis of ZIF-8/IgG composite, focusing on its precise orientation and protection of the anti-aflatoxins antibody. The antibody orientation is achieved through the specific binding of IgG to the Fc region of the antibody, while the antibody protection is accomplished by the structural change restriction of ZIF-8 framework to the antibody. Consequently, the antibody exhibits enhanced target capability and significantly improved tolerance to organic solvents. The ZIF-8/IgG/anti-AFT was employed for the purification and detection of AFTs by coupling with UPLC. Under optimized conditions, the recoveries of spiked AFTs in peanut oils are between 86.1% and 106.4%, with relative standard deviations (RSDs) ranging from 0.8% to 8.8%. The linearity range is 0.5-20.0 ng for AFB1 and AFG1, 0.125-5.0 ng for AFB2 and AFG2, the limit of detection is 0.1 ng for AFB1 and AFG1, 0.03 ng for AFB2 and AFG2.

For antibody sequence generative modeling, mixture models may be all you need.

MOTIVATION: Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity. RESULTS: In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature. AVAILABILITY AND IMPLEMENTATION: All tools developed in this study are available at https://github.com/Wang-lab-UCSD/AntPack.

Antibody Design for the Quantification of Photosynthetic Proteins and Their Isoforms.

Antibodies are a valuable research tool, with uses including detection and quantification of specific proteins. By using peptide fragments to raise antibodies, they can be designed to differentiate between structurally similar proteins, or to bind conserved motifs in divergent proteins. Peptide sequence selection and antibody validation are crucial to ensure reliable results from antibody-based experiments. This chapter describes the steps for the identification of peptide sequences to produce protein- or isoform-specific antibodies using recombinant technologies as well as the subsequent validation of such antibodies. The photosynthetic protein Rubisco activase is used as a case study to explain the various steps involved and key aspects to take into consideration.

Central Chirality and Axial Chirality Recognition of the Enantioselective Antibodies to Herbicide Metolachlor.

Enantioselective antibodies have emerged as efficient tools in the field of chiral chemical detection and separation. However, it is complicated to obtain a highly stereoselective antibody due to the unclear recognition mechanism. In this study, the hapten of metolachlor was synthesized and enantio-separated. The absolute configuration of the four haptens obtained was identified by the computed and experimental electronic circular dichroism comparison. Five polyclonal antibodies against the Rac-metolachlor and its enantiomers were generated by immunization. The cross-activity of all the 5 antibodies with 44 structural analogues, including metolachlor enantiomers, was tested. It demonstrated that antibodies have higher specificity to recognize central chirality than axial chirality. Especially, αRR-MET-Ab exhibited excellent specificity and stereoselectivity. Accordingly, 3D-QSAR models were constructed and revealed that paired stereoisomers exhibited opposite interactions with the antibodies. It is the first time that the antibodies against four stereoisomers were prepared and analyzed, which will be conducive to the rational design of the stereoselective antibodies.

Control of Biological Surface States on Chlorine-Doped Amorphous Silica Particles and Their Effective Absorptive Ability for Antibody Protein.

Amorphous silica particles (ASPs) have low biotoxicity and are used in foodstuffs; however, the adsorption states of proteins on their surfaces have not yet been clarified. If the adsorption states can be clarified and controlled, then a wide range of biological and medical applications can be expected. The conventional amorphous silica particles have the problem of protein adsorption due to the strong interaction with their dense silanol groups and denaturation. In this study, the surfaces of amorphous silica particles with a lower silanol group density were modified with a small amount of chlorine during the synthesis process to form a specific surface layer by adsorbing water molecules and ions in the biological fluid, thereby controlling the protein adsorption state. Specifically, the hydration state on the surface of the amorphous silica particles containing trace amounts of chlorine was evaluated, and the surface layer (especially the hydration state) for the adsorption of antibody proteins while maintaining their steric structures was evaluated and discussed. The results showed that the inclusion of trace amounts of chlorine increased the silanol groups and Si-Cl bonds in the topmost surface layer of the particles, thereby inducing the adsorption of ions and water molecules in the biological fluid. Then, it was found that a novel surface layer was formed by the effective adsorption of Na and phosphate ions, which would change the proportion of the components in the hydration layer. In particular, the proportion of the free water component increased by 21% with the doping of chlorine. Antibody proteins were effectively adsorbed on the particles doped with trace amounts of chlorine, and their steric adsorption states were evaluated. It was found that the proteins were clearly adsorbed and maintained the steric state of their secondary structure. In the immunoreactivity tests using streptavidin and biotin, biotin bound to the chlorine-doped particles showed efficient reactivity. In conclusion, this study is the first to discover the surface layer of the amorphous silica particles to maintain the steric structures of adsorbed proteins, which is expected to be used as a carrier particle for antibody test kits and immunochromatography.

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions.

MOTIVATION: Pre-trained protein language and/or structural models are often fine-tuned on drug development properties (i.e. developability properties) to accelerate drug discovery initiatives. However, these models generally rely on a single structural conformation and/or a single sequence as a molecular representation. We present a physics-based model, whereby 3D conformational ensemble representations are fused by a transformer-based architecture and concatenated to a language representation to predict antibody protein properties. Antibody language ensemble fusion enables the direct infusion of thermodynamic information into latent space and this enhances property prediction by explicitly infusing dynamic molecular behavior that occurs during experimental measurement. RESULTS: We showcase the antibody language ensemble fusion model on two developability properties: hydrophobic interaction chromatography retention time and temperature of aggregation (Tagg). We find that (i) 3D conformational ensembles that are generated from molecular simulation can further improve antibody property prediction for small datasets, (ii) the performance benefit from 3D conformational ensembles matches shallow machine learning methods in the small data regime, and (iii) fine-tuned large protein language models can match smaller antibody-specific language models at predicting antibody properties. AVAILABILITY AND IMPLEMENTATION: AbLEF codebase is available at https://github.com/merck/AbLEF.

Multiphosphorylation-Dependent Recognition of Anti-pS2 Antibodies against RNA Polymerase II C-Terminal Domain Revealed by Chemical Synthesis.

Phosphorylation is a major constituent of the CTD code, which describes the set of post-translational modifications on 52 repeats of a YSPTSPS consensus heptad that orchestrates the binding of regulatory proteins to the C-terminal domain (CTD) of RNA polymerase II. Phospho-specific antibodies are used to detect CTD phosphorylation patterns. However, their recognition repertoire is underexplored due to limitations in the synthesis of long multiphosphorylated peptides. Herein, we describe the development of a synthesis strategy that provides access to multiphosphorylated CTD peptides in high purity without HPLC purification for immobilization onto microtiter plates. Native chemical ligation was used to assemble 12 heptad repeats in various phosphoforms. The synthesis of >60 CTD peptides, 48-90 amino acids in length and containing up to 6 phosphosites, enabled a detailed and rapid analysis of the binding characteristics of different anti-pSer2 antibodies. The three antibodies tested showed positional selectivity with marked differences in the affinity of the antibodies for pSer2-containing peptides. Furthermore, the length of the phosphopeptides allowed a systematic analysis of the multivalent chelate-type interactions. The absence of multivalency-induced binding enhancements is probably due to the high flexibility of the CTD scaffold. The effect of clustered phosphorylation proved to be more complex. Recognition of pSer2 by anti-pSer2-antibodies can be prevented and, perhaps surprisingly, enhanced by the phosphorylation of "bystander" amino acids in the vicinity. The results have relevance for functional analysis of the CTD in cell biological experiments.

Aerosolization of poly(sulfobetaine) microparticles that encapsulate therapeutic antibodies.

Pulmonary delivery of protein therapeutics poses significant challenges that have not been well addressed in the research literature or practice. In fact, there is currently only one commercial protein therapeutic that is delivered through aerosolization and inhalation. In this study, we propose a drug delivery strategy that enables a high-concentration dosage for the pulmonary delivery of antibodies as an aerosolizable solid powder with desired stability. We utilized zwitterionic polymers for their promising properties as drug delivery vehicles and synthesized swellable, biodegradable poly(sulfo-betaine) (pSB) microparticles. The microparticles were loaded with Immunoglobulin G (IgG) as a model antibody. We quantified the microparticle size and morphology, and the particles were found to have an average diameter of 1.6 µm, falling within the optimal range (~1-5 µm) for pulmonary drug delivery. In addition, we quantified the impact of the crosslinker to monomer ratio on particle morphology and drug loading capacity. The results showed that there is a trade-off between desired morphology and drug loading capacity as the crosslinker density increases. In addition, the particles were aerosolized, and our data indicated that the particles remained intact and retained their initial morphology and size after aerosolization. The combination of morphology, particle size, antibody loading capacity, low cytotoxicity, and ease of aerosolization support the potential use of these particles for pulmonary delivery of protein therapeutics.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

Ratiometric electrochemical immunoassay based on 2D Co/Fe MOF decorated with toluidine blue and Fc-labeled Schiff base for accurate assay of alpha-fetoprotein in clinical serum.

The high level of alpha-fetoprotein (AFP) expression is closely related to hepatocellular carcinoma (HCC). Herein, a dual signal ratiometric electrochemical immunosensor based on chitosan-ferrocenecarboxaldehyde-spindle gold (Chit-Fc-SAu) and Co/Fe metal-organic framework-toluidine blue/polydopamine (Co/Fe MOF-TB/PDA) was proposed for quantitative analysis of AFP. Specifically, Chit-Fc-SAu worked as a substrate to trap more primary antibodies (Ab1) generating the first electrochemical signal from Fc. Thanks to the large specific surface area, the synergistic and electronic effects of Co/Fe MOF nanosheets, and the rich functional groups of PDA, Co/Fe MOF-TB/PDA could load more secondary antibodies (Ab2) and signal molecules (TB) providing another amplified electrochemical signal. In the presence of AFP, Ab1-AFP-Ab2 formed a sandwich structure, and as the AFP concentration increased, the peak current ratio of TB to Fc (ITB/IFc) also increased. The dual signal ratiometric strategy can avoid environmental signal interference and achieve signal self-calibration, thereby improving the accuracy and reproducibility of detection. After a series of exploration, this self-calibrated ratiometric immunosensor exhibited a wide linear range (0.001-200 ng mL-1), a low detection limit (0.34 pg mL-1), and good repeatability. When applied to the assay of clinical serum samples, the detection results of ratiometric sensor were consistent with that of commercial electrochemiluminescence (ECL) immunoassay, significantly superior to that of non-ratiometric sensor. The self-calibrated strategy based on ratiometric sensor helps to improve the accuracy of AFP in clinical diagnosis.

A Genetically Encoded Photocaged Cysteine for Facile Site-Specific Introduction of Conjugation-Ready Thiol Residues in Antibodies.

Antibody-drug conjugates (ADCs) have emerged as a powerful class of anticancer therapeutics that enable the selective delivery of toxic payloads into target cells. There is increasing appreciation for the importance of synthesizing such ADCs in a defined manner where the payload is attached at specific permissive sites on the antibody with a defined drug to antibody ratio. Additionally, the ability to systematically alter the site of attachment is important to fine-tune the therapeutic properties of the ADC. Engineered cysteine residues have been used to achieve such site-specific programmable attachment of drug molecules onto antibodies. However, engineered cysteine residues on antibodies often get "disulfide-capped" during secretion and require reductive regeneration prior to conjugation. This reductive step also reduces structurally important disulfide bonds in the antibody itself, which must be regenerated through oxidation. This multistep, cumbersome process reduces the efficiency of conjugation and presents logistical challenges. Additionally, certain engineered cysteine sites are resistant to reductive regeneration, limiting their utility and the overall scope of this conjugation strategy. In this work, we utilize a genetically encoded photocaged cysteine residue that can be site-specifically installed into the antibody. This photocaged amino acid can be efficiently decaged using light, revealing a free cysteine residue available for conjugation without disrupting the antibody structure. We show that this ncAA can be incorporated at several positions within full-length recombinant trastuzumab and decaged efficiently. We further used this method to generate a functional ADC site-specifically modified with monomethyl auristatin F (MMAF).

Surface Modification of Polystyrene with Boronic Acid for Immunoaffinity-Based Cell Enrichment.

Obtaining an enriched and phenotypically pure cell population from heterogeneous cell mixtures is important for diagnostics and biosensing. Existing techniques such as fluorescent-activated cell sorting (FACS) and magnetic-activated cell sorting (MACS) require preincubation with antibodies (Ab) and specialized equipment. Cell immunopanning removes the need for preincubation and can be done with no specialized equipment. The majority of the available antibody-mediated analyte capture techniques require a modification to the Abs for binding. In this work, no antibody modification is used because we take advantage of the carbohydrate chain in the Fc region of Ab. We use boronic acid as a cross-linker to bind the Ab to a modified surface. The process allows for functional orientation and cleavable binding of the Ab. In this study, we created an immunoaffinity matrix on polystyrene (PS), an inexpensive and ubiquitous plastic. We observed a 37% increase in Ab binding compared with that of a passive adsorption approach. The method also displayed a more consistent antibody binding with 17 times less variation in Ab loading among replicates than did the passive adsorption approach. Surface topography analysis revealed that a dextran coating reduced nonspecific antibody binding. Elemental analysis (XPS) was used to characterize the surface at different stages and showed that APBA molecules can bind upside-down on the surface. While upside-down antibodies likely remain functional, their elution behavior might differ from those bound in the desired way. Cell capture experiments show that the new surface has 43% better selectivity and 2.4-fold higher capture efficiency compared to a control surface of passively adsorbed Abs. This specific surface chemistry modification will allow the targeted capture of cells or analytes with the option of chemical detachment for further research and characterization.

Quantum Dot-Based Immunoassays: Unraveling Sensitivity Discrepancies and Charting Future Frontiers.

The 2023 Nobel Prize in Chemistry honors the groundbreaking contributions of Alexei Ekimov, Louis Brus, and Moungi Bawendi to the field of quantum dots (QDs). In this spirit, we developed a direct competitive QD fluorescence immunoassay (dc-QD-FLISA) to detect aristolochic acid type I (AAI), a potent carcinogen found in herbal remedies. Unexpectedly, the dc-QD-FLISA exhibited lower sensitivity than that of an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA), contrary to our initial expectations. This discrepancy in the sensitivity prompted a comprehensive analysis of the entire experimental process. We propose that steric hindrance between QDs and antigen-binding sites on antibodies may significantly diminish the binding efficiency, reducing sensitivity within the dc-QD-FLISA method. Furthermore, issues such as buffer conditions, antibody handling, and separation methods are also contributing factors. We recommend site-directed QD modification and stringent consideration of the experimental conditions. This study not only provides insights into QD-based immunoassays but also highlights the need for future advancements in immunoassay technology in terms of augmenting sensitivity and specificity, potentially revolutionizing disease diagnosis, biomarker discovery, and biomedical research.

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro.

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. In an exploratory analysis of PBMC datasets, we find that some droplets that were originally called 'empty' due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a 'spongelet' which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

Computational optimization of antibody humanness and stability by systematic energy-based ranking.

Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.

ZnO nanostructures: A promising frontier in immunosensor development.

This review addresses the design of immunosensors, which employ ZnO nanostructures. Various methods of modifying ZnO nanostructures with antibodies or antigens are discussed, including covalent and non-covalent approaches and cross-linking techniques. Immunosensors based on different properties of ZnO nanomaterials are described and compared. This article provides a comprehensive review of electrochemical immunosensors based on ZnO nanostructures and various detection techniques, including cyclic voltammetry (CV), differential pulse voltammetry (DPV), photoelectrochemical (PEC) detection, electrochemical impedance spectroscopy (EIS), and other electrochemical methods. In addition, this review article examines the application of optical detection techniques, including photoluminescence (PL) and electrochemiluminescence (ECL), in the development of immunosensors based on ZnO nanostructures.

The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.

Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies they describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65 000 are unique. We describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and facilitating the compilation of bespoke datasets of antibody sequences/structures that bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).

Considerations for the design of antibody drug conjugates (ADCs) for clinical development: lessons learned.

Antibody-drug conjugates (ADCs) have emerged as a novel therapeutic strategy that has successfully reached patient treatment in different clinical scenarios. ADCs are formed by an antibody against a specific tumor-associated antigen (TAA), a cytotoxic payload, and a chemical linker that binds both. To this regard, most efforts have been focused on target identification, antibody design and linker optimization, but other relevant aspects for clinical development have not received the necessary attention. In this article using data from approved ADCs, we evaluated all characteristics of these agents, including payload physicochemical properties, in vitro potency, drug antibody ratio (DAR), exposure-response relationships, and clinical development strategies. We suggest that compounds with best options for clinical development include those with optimal payload physicochemical properties and cleavable linkers that would lead to a bystander effect. These modalities can facilitate the development of ADCs in indications with low expression of the TAA. Early clinical development strategies including changes in the schedule of administration with more frequent doses are also discussed in the context of an efficient strategy. In conclusion, we highlight relevant aspects that are needed for the optimal development of ADCs in cancer, proposing options for improvement.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling.

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.