Functional Deimmunization of Botulinum Neurotoxin Protease Domain via Computationally Driven Library Design and Ultrahigh-Throughput Screening.

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.

Computational epitope binning reveals functional equivalence of sequence-divergent paratopes.

The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in 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.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

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.

Diverse antiviral IgG effector activities are predicted by unique biophysical antibody features.

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.

Comprehensive analysis of lectin-glycan interactions reveals determinants of lectin specificity.

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.

MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform.

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.

Building blocks and blueprints for bacterial autolysins.

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.

Bioinformatics-driven discovery of novel Clostridioides difficile lysins and experimental comparison with highly active benchmarks.

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).

Protein interaction interface region prediction by geometric deep learning.

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.

A Chimeric Antigen Receptor That Binds to a Conserved Site on MICA.

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.

DAR-901 vaccine for the prevention of infection with Mycobacterium tuberculosis among BCG-immunized adolescents in Tanzania: A randomized controlled, double-blind phase 2b trial.

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.

Globally deimmunized lysostaphin evades human immune surveillance and enables highly efficacious repeat dosing.

There is a critical need for novel therapies to treat methicillin-resistant (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.

Computer-guided binding mode identification and affinity improvement of an LRR protein binder without structure determination.

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.

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition.

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.

Combinatorial Resurfacing of Dengue Envelope Protein Domain III Antigens Selectively Ablates Epitopes Associated with Serotype-Specific or Infection-Enhancing Antibody Responses.

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.

Learning context-aware structural representations to predict antigen and antibody binding interfaces.

Motivation: 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. 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.

Induction of cross-reactive HIV-1 specific antibody responses by engineered V1V2 immunogens with reduced conformational plasticity.

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 α-helix-coil or β-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.

Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity.

Background: Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity).

Antibody Fab-Fc properties outperform titer in predictive models of SIV vaccine-induced protection.

Characterizing the antigen-binding and innate immune-recruiting properties of the humoral response offers the chance to obtain deeper insights into mechanisms of protection than revealed by measuring only overall antibody titer. Here, a high-throughput, multiplexed Fab-Fc Array was employed to profile rhesus macaques vaccinated with a gp120-CD4 fusion protein in combination with different genetically encoded adjuvants, and subsequently subjected to multiple heterologous simian immunodeficiency virus (SIV) challenges. Systems analyses modeling protection and adjuvant differences using Fab-Fc Array measurements revealed a set of correlates yielding strong and robust predictive performance, while models based on measurements of response magnitude alone exhibited significantly inferior performance.

Route of immunization defines multiple mechanisms of vaccine-mediated protection against SIV.

Antibodies are the primary correlate of protection for most licensed vaccines; however, their mechanisms of protection may vary, ranging from physical blockade to clearance via the recruitment of innate immunity. Here, we uncover striking functional diversity in vaccine-induced antibodies that is driven by immunization site and is associated with reduced risk of SIV infection in nonhuman primates. While equivalent levels of protection were observed following intramuscular (IM) and aerosol (AE) immunization with an otherwise identical DNA prime-Ad5 boost regimen, reduced risk of infection was associated with IgG-driven antibody-dependent monocyte-mediated phagocytosis in the IM vaccinees, but with vaccine-elicited IgA-driven neutrophil-mediated phagocytosis in AE-immunized animals.

Pareto Optimization of Combinatorial Mutagenesis Libraries.

In order to increase the hit rate of discovering diverse, beneficial protein variants via high-throughput screening, we have developed a computational method to optimize combinatorial mutagenesis libraries for overall enrichment in two distinct properties of interest. Given scoring functions for evaluating individual variants, POCoM (Pareto Optimal Combinatorial Mutagenesis) scores entire libraries in terms of averages over their constituent members, and designs optimal libraries as sets of mutations whose combinations make the best trade-offs between average scores. This represents the first general-purpose method to directly design combinatorial libraries for multiple objectives characterizing their constituent members.

DisruPPI: structure-based computational redesign algorithm for protein binding disruption.

Motivation: Disruption of protein-protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein-Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.

Towards conformational fidelity of a quaternary HIV-1 epitope: computational design and directed evolution of a minimal V1V2 antigen.

Structure-based approaches to antigen design utilize insights from antibody (Ab):antigen interactions and a refined understanding of protective Ab responses to engineer novel antigens presenting epitopes with conformations relevant to eliciting or discovering protective humoral responses. For human immunodeficiency virus-1 (HIV-1), one model of protection is provided by broadly neutralizing Abs (bnAbs) against epitopes present in the closed prefusion trimeric conformation of HIV-1 envelope glycoprotein, such as the variable loops 1-2 (V1V2) apex. Here, computational design and directed evolution yielded a novel V1V2 sequence variant with potential utility for inclusion in an immunogen for eliciting bnAbs, or as an epitope probe for their detection.

High-resolution definition of humoral immune response correlates of effective immunity against HIV.

Defining correlates of immunity by comprehensively interrogating the extensive biological diversity in naturally or experimentally protected subjects may provide insights critical for guiding the development of effective vaccines and antibody-based therapies. We report advances in a humoral immunoprofiling approach and its application to elucidate hallmarks of effective HIV-1 viral control. Systematic serological analysis for a cohort of HIV-infected subjects with varying viral control was conducted using both a high-resolution, high-throughput biophysical antibody profiling approach, providing unbiased dissection of the humoral response, along with functional antibody assays, characterizing antibody-directed effector functions such as complement fixation and phagocytosis that are central to protective immunity.

Fine epitope signature of antibody neutralization breadth at the HIV-1 envelope CD4-binding site.

Major advances in donor identification, antigen probe design, and experimental methods to clone pathogen-specific antibodies have led to an exponential growth in the number of newly characterized broadly neutralizing antibodies (bnAbs) that recognize the HIV-1 envelope glycoprotein. Characterization of these bnAbs has defined new epitopes and novel modes of recognition that can result in potent neutralization of HIV-1. However, the translation of envelope recognition profiles in biophysical assays into an understanding of in vivo activity has lagged behind, and identification of subjects and mAbs with potent antiviral activity has remained reliant on empirical evaluation of neutralization potency and breadth.