Botulinum neurotoxin serotype A (BoNT/A) is a widely used cosmetic agent that also has diverse therapeutic applications; however, adverse antidrug immune responses and associated loss of efficacy have been reported in clinical uses. Here, we describe computational design and ultrahigh-throughput screening of a massive BoNT/A light-chain (BoNT/A-LC) library optimized for reduced T cell epitope content and thereby dampened immunogenicity. We developed a functional assay based on bacterial co-expression of BoNT/A-LC library members with a Förster resonance energy transfer (FRET) sensor for BoNT/A-LC enzymatic activity, and we employed high-speed fluorescence-activated cell sorting (FACS) to identify numerous computationally designed variants having wild-type-like enzyme kinetics. Many of these variants exhibited decreased immunogenicity in humanized HLA transgenic mice and manifested in vivo paralytic activity when incorporated into full-length toxin. One variant achieved near-wild-type paralytic potency and a 300% reduction in antidrug antibody response in vivo. Thus, we have achieved a striking level of BoNT/A-LC functional deimmunization by combining computational library design and ultrahigh-throughput screening. This strategy holds promise for deimmunizing other biologics with complex superstructures and mechanisms of action.

The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in in vitro library display screening and next-generation sequencing have enabled accelerated development of strong binders, yet identifying their binding sites still remains a major challenge. The differentiation, or “binning”, of binders into different groups that recognize distinct binding sites on their target is a promising approach that facilitates high-throughput screening of binders that may show different biological activity. Here we study the extent to which the information contained in the amino acid sequences comprising a set of target-specific binders can be leveraged to bin them, inferring functional equivalence of their binding regions, or paratopes, based directly on comparison of the sequences, their modeled structures, or their modeled interactions. Using a leucine-rich repeat binding scaffold known as a “repebody” as the source of diversity in recognition against interleukin-6 (IL-6), we show that the “Epibin” approach introduced here effectively utilized structural modelling and docking to extract specificity information encoded in the repebody amino acid sequences and thereby successfully recapitulate IL-6 binding competition observed in immunoassays. Furthermore, our computational binning provided a basis for designing in vitro mutagenesis experiments to pinpoint specificity-determining residues. Finally, we demonstrate that the Epibin approach can extend to antibodies, retrospectively comparing its predictions to results from antigen-specific antibody competition studies. The study thus demonstrates the utility of modeling structure and binding from the amino acid sequences of different binders against the same target, and paves the way for larger-scale binning and analysis of entire repertoires.

Clostridioides difficile is the single most deadly bacterial pathogen in the United States, and its global prevalence and outsized health impacts underscore the need for more effective therapeutic options. Towards this goal, a novel group of modified peptidoglycan hydrolases with significant in vitro bactericidal activity have emerged as potential candidates for treating C. difficile infections (CDI). To date, discovery and development efforts directed at these CDI-specific lysins have been limited, and in particular there has been no systematic comparison of known or newly discovered lysin candidates. Here, we detail bioinformatics-driven discovery of six new anti-C. difficile lysins belonging to the amidase-3 family of enzymes, and we describe experimental comparison of their respective catalytic domains (CATs) with highly active CATs from the literature. Our quantitative analyses include metrics for expression level, inherent antibacterial activity, breadth of strain selectivity, killing of germinating spores, and structural and functional measures of thermal stability. Importantly, prior studies have not examined stability as a performance metric, and our results show that the panel of eight enzymes possess widely variable thermal denaturation temperatures and resistance to heat inactivation, including some enzymes that exhibit marginal stability at body temperature. Ultimately, no single enzyme dominated with respect to all performance measures, suggesting the need for a balanced assessment of lysin properties during efforts to find, engineer, and develop candidates with true clinical potential.

BACKGROUND:The critical role of antibody Fc-mediated effector functions in immune defense has been widely reported in various viral infections. These effector functions confer cellular responses through engagement with innate immune cells. The precise mechanism(s) by which immunoglobulin G (IgG) Fc domain and cognate receptors may afford protection are poorly understood, however, in the context of HIV/SHIV infections. Many different in vitro assays have been developed and utilized to measure effector functions, but the extent to which these assays capture distinct antibody activities has not been fully elucidated.RESULTS:In this study, six Fc-mediated effector function assays and two biophysical antibody profiling assays were performed on a common set of samples from HIV-1 infected and vaccinated subjects. Biophysical antibody profiles supported robust prediction of diverse IgG effector functions across distinct Fc-mediated effector function assays. While a number of assays showed correlated activities, supervised machine learning models indicated unique antibody features as primary contributing factors to the associated effector functions. Additional experiments established the mechanistic relevance of relationships discovered using this unbiased approach.CONCLUSIONS:In sum, this study provides better resolution on the diversity and complexity of effector function assays, offering a clearer perspective into this family of antibody mechanisms of action to inform future HIV-1 treatment and vaccination strategies.

As non-"self" macromolecules, biotherapeutics can trigger an immune response that can reduce drug efficacy, require patients to be taken off therapy, or even cause life-threatening reactions. To enable the flexible and facile design of protein biotherapeutics while reducing the prevalence of T-cell epitopes that drive immune recognition, we have integrated into the Rosetta protein design suite a new scoring term that allows design protocols to account for predicted or experimentally identified epitopes in the optimized objective function. This flexible scoring term can be used in any Rosetta design trajectory, can be targeted to specific regions of a protein, and can be readily extended to work with a variety of epitope predictors. By performing extensive design runs with varied design parameter choices for three case study proteins as well as a larger diverse benchmark, we show that the incorporation of this scoring term enables the effective exploration of an alternative, deimmunized sequence space to discover diverse proteins that are potentially highly deimmunized while retaining physical and chemical qualities similar to those yielded by equivalent nondeimmunizing sequence design protocols.

ABSTRACTGlycans are found across the tree of life with remarkable structural diversity enabling critical contributions to diverse biological processes, ranging from facilitating host-pathogen interactions to regulating mitosis & DNA damage repair. While functional motifs within glycan structures are largely responsible for mediating interactions, thecontextsin which the motifs are presented can drastically impact these interactions and their downstream effects. Here, we demonstrate the first deep learning method to represent both local and global context in the study of glycan structure-function relationships. Our method, glyBERT, encodes glycans with a branched biochemical language and employs an attention-based deep language model to learn biologically relevant glycan representations focused on the most important components within their global structures. Applying glyBERT to a variety of prediction tasks confirms the value of capturing rich context-dependent patterns in this attention-based model: the same monosaccharides and glycan motifs are represented differently in different contexts and thereby enable improved predictive performance relative to the previous state-of-the-art approaches. Furthermore, glyBERT supports generative exploration of context-dependent glycan structure-function space, moving from one glycan to “nearby” glycans so as to maintain or alter predicted functional properties. In a case study application to altering glycan immunogenicity, this generative process reveals the learned contextual determinants of immunogenicity while yielding both known and novel, realistic glycan structures with altered predicted immunogenicity. In summary, modeling the context dependence of glycan motifs is critical for investigating overall glycan functionality and can enable further exploration of glycan structure-function space to inform new hypotheses and synthetic efforts.

Computational methods for predicting binding interfaces between antigens and antibodies (epitopes and paratopes) are faster and cheaper than traditional experimental structure determination methods. A sufficiently reliable computational predictor that could scale to large sets of available antibody sequence data could thus inform and expedite many biomedical pursuits, such as better understanding immune responses to vaccination and natural infection and developing better drugs and vaccines. However, current state-of-the-art predictors produce discontiguous predictions, e.g., predicting the epitope in many different spots on an antigen, even though in reality they typically comprise a single localized region. We seek to produce contiguous predicted epitopes, accounting for long-range spatial relationships between residues. We therefore build a novel Graph Convolution Network (GCN) that performs graph convolutions at multiple resolutions so as to represent and constrain long-range spatial dependencies. In evaluation on a standard epitope prediction benchmark, we see a significant boost with the multi-resolution approach compared to a previous state-of-the-art GCN predictor, with half of the test cases increasing in AUC-PR by an average of 0.15 and the other half decreasing by only 0.05. We further introduce a clustering algorithm that takes advantage of the contiguity yielded by our model, grouping the raw predictions into a small set of discrete potential epitopes. We show that within the top 3 clusters, 73% of test cases contain a cluster covering most of the actual epitope, demonstrating the utility of contiguous predictions for guiding experimental methods by yielding a small set of reasonable hypotheses for further investigation.

Motivation: Protein-protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately, both physical modeling and machine learning methods suffer from poor precision and/or recall. Results: In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, `PInet' (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Bacteria utilize a wide variety of endogenous cell wall hydrolases, or autolysins, to remodel their cell walls during processes including cell division, biofilm formation, and programmed death. We here systematically investigate the composition of these enzymes in order to gain insights into their associated biological processes, potential ways to disrupt them via chemotherapeutics, and strategies by which they might be leveraged as recombinant antibacterial biotherapies. To do so, we developed LEDGOs (lytic enzyme domains grouped by organism), a pipeline to create and analyze databases of autolytic enzyme sequences, constituent domain annotations, and architectural patterns of multi-domain enzymes that integrate peptidoglycan binding and degrading functions. We applied LEDGOs to eight pathogenic bacteria, gram negatives Acinetobacter baumannii, Klebsiella pneumoniae, Neisseria gonorrhoeae, and Pseudomonas aeruginosa; and gram positives Clostridioides difficile, Enterococcus faecium, Staphylococcus aureus, and Streptococcus pneumoniae. Our analysis of the autolytic enzyme repertoires of these pathogens reveals commonalities and differences in their key domain building blocks and architectures, including correlations and preferred orders among domains in multi-domain enzymes, repetitions of homologous binding domains with potentially complementarity recognition modalities, and sequence similarity patterns indicative of potential divergence of functional specificity among related domains. We have further identified a variety of unannotated sequence regions within the lytic enzymes that may themselves contain new domains with important functions.

Lectin-glycan interactions facilitate inter- and intracellular communication in many processes including protein trafficking, host-pathogen recognition, and tumorigenesis promotion. Specific recognition of glycans by lectins is also the basis for a wide range of applications in areas including glycobiology research, cancer screening, and antiviral therapeutics. To provide a better understanding of the determinants of lectin-glycan interaction specificity and support such applications, this study comprehensively investigates specificity-conferring features of all available lectin-glycan complex structures. Systematic characterization, comparison, and predictive modeling of a set of 221 complementary physicochemical and geometric features representing these interactions highlighted specificity-conferring features with potential mechanistic insight. Univariable comparative analyses with weighted Wilcoxon-Mann-Whitney tests revealed strong statistical associations between binding site features and specificity that are conserved across unrelated lectin binding sites. Multivariable modeling with random forests demonstrated the utility of these features for predicting the identity of bound glycans based on generalized patterns learned from non-homologous lectins. These analyses revealed global determinants of lectin specificity, such as sialic acid glycan recognition in deep, concave binding sites enriched for positively charged residues, in contrast to high mannose glycan recognition in fairly shallow but well-defined pockets enriched for non-polar residues. Focused fine specificity analysis of hemagglutinin interactions with human-like and avian-like glycans uncovered features representing both known and novel mutations related to shifts in influenza tropism from avian to human tissues. As the approach presented here relies on co-crystallized lectin-glycan pairs for studying specificity, it is limited in its inferences by the quantity, quality, and diversity of the structural data available. Regardless, the systematic characterization of lectin binding sites presented here provides a novel approach to studying lectin specificity and is a step towards confidently predicting new lectin-glycan interactions.

Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody–antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody–antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental “epitope binning” of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody–antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.

Antibodies against the HIV-1 V1V2 loops were the only correlate of reduced infection risk in the RV144 vaccine trial, highlighting the V1V2 loops as promising targets for vaccine design. The V1V2 loops are structurally plastic, exhibiting either an alpha-helix-coil or beta-strand conformation. V1V2-specific antibodies may thus recognize distinct conformations, and an antibody's conformational specificity can be an important determinant of breadth and function. Restricting V1V2 conformational plasticity in an immunogen may thus provide control over the conformational specificity and quality of a vaccine-elicited antibody response. Previously, we identified a V1V2 sequence variant (K155M) that results in enhanced recognition by cross-reactive antibodies recognizing the beta-strand conformation. Here, we relate V1V2 antigenicity to immunogenicity by comparing the immunogenicity profiles of wildtype and K155M immunogens in two mouse models. In one model, immunization with gp70 V1V2 K155M but not wildtype elicited antibody responses that were cross-reactive to a panel of heterologous gp120 and gp140 antigens. In a second model, we compared the effect of K155M on immunogenicity in the context of gp70 V1V2, gD V1V2 and gp120, examining the effects of scaffold, epitope-focusing and immunization regimen. K155M variants, especially in the context of a gp120 immunogen, resulted in more robust, durable and cross-reactive antibody responses than wildtype immunogens. Restriction of the beta-stranded V1V2 conformation in K155M immunogens may thus be associated with the induction of cross-reactive antibody responses thought to be required of a protective HIV-1 vaccine. (C) 2020 The Author(s). Published by Elsevier Ltd.

There is a critical need for novel therapies to treat methicillin-resistant Staphylococcus aureus (MRSA) and other drug-resistant pathogens, and lysins are among the vanguard of innovative antibiotics under development. Unfortunately, lysins' own microbial origins can elicit detrimental antidrug antibodies (ADAs) that undermine efficacy and threaten patient safety. To create an enhanced anti-MRSA lysin, a novel variant of lysostaphin was engineered by T cell epitope deletion. This "deimmunized" lysostaphin dampened human T cell activation, mitigated ADA responses in human HLA transgenic mice, and enabled safe and efficacious repeated dosing during a 6-week longitudinal infection study. Furthermore, the deimmunized lysostaphin evaded established anti-wildtype immunity, thereby providing significant anti-MRSA protection for animals that were immune experienced to the wild-type enzyme. Last, the enzyme synergized with daptomycin to clear a stringent model of MRSA endocarditis. By mitigating T cell-driven antidrug immunity, deimmunized lysostaphin may enable safe, repeated dosing to treat refractory MRSA infections.

Mutagenesis of surface-exposed residues, or "resurfacing", is a protein engineering strategy that can be utilized to disrupt antibody recognition or modulate the capacity of a protein to elicit antibody responses. We apply resurfacing to engineer Dengue virus envelope protein domain III (DENV DIII) antigens with the goal of focusing humoral recognition on epitopes of interest by selective ablation of irrelevant and undesired epitopes. Cross-reactive but non-neutralizing antibodies have the potential to enhance Dengue virus (DENV) infection by a process called antibody-dependent enhancement, thought to be associated with severe secondary heterotypic infection. Thus, a focus on epitopes associated with broadly neutralizing antibodies is important both for understanding human antibody responses against DENV and for the development of a successful DENV vaccine. To engineer DENV DIII antigens focusing on the AG strand epitope associated with broadly neutralizing antibody responses, we generated yeast surface display libraries of DENV2 DIII where the AB loop (associated with cross-reactive but non-neutralizing antibody responses) and FG loop (associated with serotype-specific antibody responses) were mutagenized to allow for all possible amino acid substitutions. Loop variants that maintained the AG strand epitope and simultaneously disrupted the AB and FG loop epitopes exhibited high and diverse mutational loads that were amenable to loop exchange and transplantation into a DENV4 DIII background. Thus, several loop variants fulfill this antigenicity criteria regardless of serotype context. The resulting resurfaced DIII antigens may be utilized as AG strand epitope-focusing probes or immunogen candidates.

Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the “ground truth” of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. In order to increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. The framework leverages three key aspects of antibody-antigen interactions in order to learn predictive structural representations: (1) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (2) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (3) since more data is available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.

Precise binding mode identification and subsequent affinity improvement without structure determination remain a challenge in the development of therapeutic proteins. However, relevant experimental techniques are generally quite costly, and purely computational methods have been unreliable. Here, we show that integrated computational and experimental epitope localization followed by full-atom energy minimization can yield an accurate complex model structure which ultimately enables effective affinity improvement and redesign of binding specificity. As proof-of-concept, we used a leucine-rich repeat (LRR) protein binder, called a repebody (Rb), that specifically recognizes human IgG1 (hIgG1). We performed computationally-guided identification of the Rb:hIgG1 binding mode and leveraged the resulting model to reengineer the Rb so as to significantly increase its binding affinity for hIgG1 as well as redesign its specificity toward multiple IgGs from other species. Experimental structure determination verified that our Rb:hIgG1 model closely matched the co-crystal structure. Using a benchmark of other LRR protein complexes, we further demonstrated that the present approach may be broadly applicable to proteins undergoing relatively small conformational changes upon target binding.

Background: SRL172 prevented disease due to Mycobacterium tuberculosis in a Phase 3 trial. DAR-901 represents a scalable manufacturing process for SRL172. We sought to determine if DAR-901 would prevent infection with M. tuberculosis among BCG-primed adolescents age 13-15 years in Tanzania. Methods: Adolescents with a negative T- SPOT.TBR interferon gamma release assay (IGRA) were randomized 1:1 to three intradermal injections of DAR-901 or saline placebo at 0, 2 and 4 months. Repeat IGRAs were performed at 2 months, and at 1, 2, and 3 years. The primary efficacy outcome was time to new TB infection (IGRA conversion to positive); the secondary outcome was time to persistent TB infection (IGRA conversion with repeat positive IGRA). Results: Among 936 participants screened 667 were eligible and randomized to their first dose of vaccine or placebo (safety cohort). At 2 months, 625 participants remained IGRA-negative and were scheduled for the additional two doses (efficacy cohort). DAR-901 was safe and well-tolerated. One DAR-901 recipient developed a vaccine site abscess. Neither the primary nor secondary endpoints differed between the two treatment arms (p = 0.90 and p = 0.20, respectively). DAR-901 IGRA converters had median responses to ESAT-6 of 50.1 spot-forming cells (SFCs) vs. 19.6 SFCs in placebo IGRA converters (p = 0.03). Conclusions: A three-dose series of 1 mg DAR-901 was safe and well-tolerated but did not prevent initial or persistent IGRA conversion. DAR-901 recipients with IGRA conversion demonstrated enhanced immune responses to ESAT-6. Since protection against disease may require different immunologic responses than protection against infection a trial of DAR-901 to prevent TB disease is warranted. (C) 2020 Elsevier Ltd. All rights reserved.

The NKG2D ligand MHC class I chain-related protein A (MICA) is expressed on many varieties of malignant cells but is absent from most normal tissues, and thus represents a potential target for chimeric Ag receptor (CAR) T cell-based therapeutics. However, there are more than 100 alleles of MICA, so the ability to target a conserved site is needed for a therapy to be used in most patients. In this study, we describe a fully human anti-MICA CAR created by fusing the single-chain fragment variable B2 to the full length DAP10 protein and the traditional CD3ζ signaling domain. Human T cells expressing the B2 CAR killed MICA-positive tumor cells, produced IFN-γ when in contact with MICA-positive tumor cells or plate-bound MICA protein, and inhibited PANC-1 growth in a mouse xenograft model. To localize B2's epitope on MICA, we used novel computational methods to model potential binding modes and to design mutational variants of MICA testing these hypotheses. Flow cytometry using a commercial anti-MICA/MICB Ab indicated that the variant proteins were expressed at high levels on transduced P815 cell lines. One variant protein (R38S/K40T/K57E) showed reduced staining with a B2-IgG1 fusion protein compared with controls and did not induce IFN-γ production by human T cells expressing the B2 CAR. These results show antitumor activity of MICA-specific CAR T cells and indicate an essential role for a conserved site in the exposed loop involving aa 38-57 of MICA. This study describes a novel MICA-specific CAR and discusses its potential use as a cancer therapeutic.