Structural Features of Antibody-Peptide Recognition

Sequence-specific targeting of intrinsically disordered protein regions

A general approach to design proteins that bind tightly and specifically to intrinsically disordered regions (IDRs) of proteins and flexible peptides would have wide application in biological research, therapeutics, and diagnosis. However, the lack of defined structures and the high variability in sequence and conformational preferences has complicated such efforts. We sought to develop a method combining biophysical principles with deep learning to readily generate binders for any disordered sequence. Instead of assuming a fixed regular structure for the target, general recognition is achieved by threading the query sequence through diverse extended binding modes in hundreds of templates with varying pocket depths and spacings, followed by RFdiffusion refinement to optimize the binder-target fit. We tested the method by designing binders to 39 highly diverse unstructured targets. Experimental testing of ~36 designs per target yielded binders with affinities better than 100 nM in 34 cases, and in the pM range in four cases. The co-crystal structure of a designed binder in complex with dynorphin A is closely consistent with the design model. All by all binding experiments for 20 designs binding diverse targets show they are highly specific for the intended targets, with no crosstalk even for the closely related dynorphin A and dynorphin B. Our approach thus could provide a general solution to the intrinsically disordered protein and peptide recognition problem.

De novo-designed minibinders expand the synthetic biology sensing repertoire

Synthetic and chimeric receptors capable of recognizing and responding to user-defined antigens have enabled "smart" therapeutics based on engineered cells. These cell engineering tools depend on antigen sensors which are most often derived from antibodies. Advances in the de novo design of proteins have enabled the design of protein binders with the potential to target epitopes with unique properties and faster production timelines compared to antibodies. Building upon our previous work combining a de novo-designed minibinder of the Spike protein of SARS-CoV-2 with the synthetic receptor synNotch (SARSNotch), we investigated whether minibinders can be readily adapted to a diversity of cell engineering tools. We show that the Spike minibinder LCB1 easily generalizes to a next-generation proteolytic receptor SNIPR that performs similarly to our previously reported SARSNotch. LCB1-SNIPR successfully enables the detection of live SARS-CoV-2, an improvement over SARSNotch which can only detect cell-expressed Spike. To test the generalizability of minibinders to diverse applications, we tested LCB1 as an antigen sensor for a chimeric antigen receptor (CAR). LCB1-CAR enabled CD8+ T cells to cytotoxically target Spike-expressing cells. We further demonstrate that two other minibinders directed against the clinically relevant epidermal growth factor receptor are able to drive CAR-dependent cytotoxicity with efficacy similar to or better than an existing antibody-based CAR. Our findings suggest that minibinders represent a novel class of antigen sensors that have the potential to dramatically expand the sensing repertoire of cell engineering tools.

Gonococcal Mimitope Vaccine Candidate Forms a Beta-Hairpin Turn and Binds Hydrophobically to a Therapeutic Monoclonal Antibody

The spread of multidrug-resistant strains of Neisseria gonorrhoeae, the etiologic agent of gonorrhea, represents a global health emergency. Therefore, the development of a safe and effective vaccine against gonorrhea is urgently needed. In previous studies, murine monoclonal antibody (mAb) 2C7 was raised against gonococcal lipooligosaccharide (LOS). mAb 2C7 elicits complement-dependent bactericidal activity against gonococci, and its glycan epitope is expressed by almost every clinical isolate. Furthermore, we identified a peptide, cyclic peptide 2 (CP2) that mimicked the 2C7 LOS epitope, elicited bactericidal antibodies in mice, and actively protected in a mouse vaginal colonization model. In this study, we performed structural analyses of mAb 2C7 and its complex with the CP2 peptide by X-ray crystallography, NMR spectroscopy, and molecular dynamics (MD) simulations. The crystal structure of Fab 2C7 bound to CP2 showed that the peptide adopted a beta-hairpin conformation and bound the Fab primarily through hydrophobic interactions. We employed NMR spectroscopy and MD simulations to map the 2C7 epitope and identify the bioactive conformation of CP2. We also used small-angle X-ray scattering (SAXS) and native mass spectrometry to obtain further information about the shape and assembly state of the complex. Collectively, our new structural information suggests strategies for humanizing mAb 2C7 as a therapeutic against gonococcal infection and for optimizing peptide CP2 as a vaccine antigen.

Physical-Chemical Approach to Designing Drugs with Multiple Targets

Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Glycylglycine promotes the solubility and antigenic utility of recombinant HCV structural proteins in a point-of-care immunoassay for detection of active viremia

BACKGROUND

Although E. coli is generally a well-opted platform for the overproduction of recombinant antigens as heterologous proteins, the optimization of expression conditions to maximize the yield of functional proteins remains empirical. Herein, we developed an optimized E. coli (BL21)-based system for the overproduction of soluble immunoreactive HCV core/envelope proteins that were utilized to establish a novel immunoassay for discrimination of active HCV infection.

METHODS

The core/E1-E2 genes were amplified and expressed in E. coli BL21 (DE3) in the absence/presence of glycylglycine. The antigenic performance of soluble proteins was assessed against 63 HCV-seronegative (Ab-) sera that included normal and interferent sera (HBV and/or chronic renal failure), and 383 HCV-seropositive (Ab+) samples that included viremic (chronic/relapsers) and recovered patients' sera. The color intensity (OD450) and S/Co values were estimated.

RESULTS

The integration of 0.1-0.4M glycylglycine in the growth media significantly enhanced the solubility/yield of recombinant core and envelope proteins by ~ 225 and 242 fold, respectively. This was reflected in their immunoreactivity and antigenic performance in the developed immunoassay, where the soluble core/E1/E2 antigen mixture showed 100% accuracy in identifying HCV viremic sera with a viral RNA load as low as 3800 IU/mL, without cross-reactivity against normal/interferent HCV-Ab-sera. The ideal S/Co threshold predicting active viremia (> 2.75) showed an AUC value of 0.9362 (95% CI: 0.9132 to 0.9593), with 87.64, 91.23% sensitivity and specificity, and 94.14, 82.11% positive and negative predictive values, respectively. The different panels of samples assayed with our EIA showed a good concordance with the viral loads and also significant correlations with the golden standards of HCV diagnosis in viremic patients. The performance of the EIA was not affected by the immunocompromised conditions or HBV co-infection.

CONCLUSION

The applicability of the proposed platform would extend beyond the reported approach, where glycylglycine, low inducer concentration and post-induction temperature, combined with the moderately-strong constitutive promoter enables the stable production of soluble/active proteins, even those with reported toxicity. Also, the newly developed immunoassay provides a cost-effective point-of-care diagnostic tool for active HCV viremia that could be useful in resource-limited settings.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

Targeting peptide-mediated interactions in omics

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.