Prediction of polyspecificity from antibody sequence data by machine learning

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.

Broad-Spectrum Defenders: γδ T Cells Take on a Multitude of Immune Challenges

In a recent PNAS article, Guo et al. investigate γδ T cell antigen specificity in mice and humans, in which they show that γδ T cell antigen specificity is not constrained to one epitope. Rather, γδ T cells recognize a broad range of diverse antigens containing similar chemical structures or properties. In this News and Views, the importance of γδ T cell antigen polyspecificity during immune responses is highlighted.

Molecular insights into the determinants of substrate specificity and efflux inhibition of the RND efflux pumps AcrB and AdeB

Gram-negative bacterial members of the Resistance Nodulation and cell Division (RND) superfamily form tripartite efflux pump systems that span the cell envelope. One of the intriguing features of the multiple drug efflux members of this superfamily is their ability to recognize different classes of antibiotics, dyes, solvents, bile salts, and detergents. This review provides an overview of the molecular mechanisms of multiple drug efflux catalysed by the tripartite RND efflux system AcrAB-TolC from Eschericha coli. The determinants for sequential or simultaneous multiple substrate binding and efflux pump inhibitor binding are discussed. A comparison is made with the determinants for substrate binding of AdeB from Acinetobacter baumannii, which acts within the AdeABC multidrug efflux system. There is an apparent general similarity between the structures of AcrB and AdeB and their substrate specificity. However, the presence of distinct conformational states and different drug efflux capacities as revealed by single-particle cryo-EM and mutational analysis suggest that the drug binding and transport features exhibited by AcrB may not be directly extrapolated to the homolog AdeB efflux pump.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

The end of the beginning in understanding SLC22 polyspecificity

SLC22 transporters involved in drug elimination and organ distribution are polyspecific. Now, the first cryo-EM structure of SLC22A3 (OCT3) is available from the Sitte and Korkhov groups. It paves the way for better understanding OCT3 function and for revealing the exact mechanisms conferring polyspecificity of the whole SLC22 family.

Human thymopoiesis produces polyspecific CD8+ α/β T cells responding to multiple viral antigens

T-cell receptors (TCRs) are formed by stochastic gene rearrangements, theoretically generating >10¹⁹ sequences. They are selected during thymopoiesis, which releases a repertoire of about 10⁸ unique TCRs per individual. How evolution shaped a process that produces TCRs that can effectively handle a countless and evolving set of infectious agents is a central question of immunology. The paradigm is that a diverse enough repertoire of TCRs should always provide a proper, though rare, specificity for any given need. Expansion of such rare T cells would provide enough fighters for an effective immune response and enough antigen-experienced cells for memory. We show here that human thymopoiesis releases a large population of clustered CD8⁺ T cells harboring α/β paired TCRs that (i) have high generation probabilities and (ii) a preferential usage of some V and J genes, (iii) which CDR3 are shared between individuals, and (iv) can each bind and be activated by multiple unrelated viral peptides, notably from EBV, CMV, and influenza. These polyspecific T cells may represent a first line of defense that is mobilized in response to infections before a more specific response subsequently ensures viral elimination. Our results support an evolutionary selection of polyspecific α/β TCRs for broad antiviral responses and heterologous immunity.

Immunogenic self-peptides - the great unknowns in autoimmunity: Identifying T-cell epitopes driving the autoimmune response in autoimmune diseases

HLA-associated autoimmune diseases likely arise from T-cell-mediated autoimmune responses against certain self-peptides from the broad HLA-presented immunopeptidomes. The limited knowledge of the autoimmune target peptides has so far compromised the basic understanding of autoimmune pathogenesis. This is due to the complexity of antigen processing and presentation as well as the polyspecificity of T-cell receptors (TCRs), which pose high methodological challenges on the discovery of immunogenic self-peptides. HLA-class I molecules present peptides to CD8⁺ T cells primarily derived from cytoplasmic proteins. Therefore, HLA-class I-restricted autoimmune responses should be directed against target cells expressing the corresponding parental protein. In HLA-class II-associated diseases, the origin of immunogenic peptides is not pre-specified, because peptides presented by HLA-class II molecules to CD4⁺ T cells may originate from both extracellular and cellular self-proteins. The different origins of HLA-class I and class II presented peptides determine the respective strategy for the discovery of immunogenic self-peptides in approaches based on the TCRs isolated from clonally expanded pathogenic T cells. Both involve identifying the respective restricting HLA allele as well as determining the recognition motif of the TCR under investigation by peptide library screening, which is required to search for homologous immunogenic self-peptides. In HLA-class I-associated autoimmune diseases, identification of the target cells allows for defining the restricting HLA allotype from the 6 different HLA-class I alleles of the individual HLA haplotype. It furthermore limits the search for immunogenic self-peptides to the transcriptome or immunopeptidome of the target cells, although neoepitopes generated by peptide splicing or translational errors may complicate identification. In HLA class II-associated autoimmune diseases, the lack of a defined target cell and differential antigen processing in different antigen-presenting cells complicate identification of the HLA restriction of autoreactive TCRs from CD4⁺ T cells. To avoid that all corresponding HLA-class II allotypes have to be included in the peptide discovery, autoantigens defined by autoantibodies can guide the search for immunogenic self-peptides presented by the respective HLA-class II risk allele. The objective of this article is to highlight important aspects to be considered in the discovery of immunogenic self-peptides in autoimmune diseases.

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

Computational counterselection identifies nonspecific therapeutic biologic candidates

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.

High-Throughput Aminoacyl-tRNA Synthetase Engineering for Genetic Code Expansion in Yeast

Protein expression with genetically encoded noncanonical amino acids (ncAAs) benefits a broad range of applications, from the discovery of biological therapeutics to fundamental biological studies. A major factor limiting the use of ncAAs is the lack of orthogonal translation systems (OTSs) that support efficient genetic code expansion at repurposed stop codons. Aminoacyl-tRNA synthetases (aaRSs) have been extensively evolved in Escherichia coli but are not always orthogonal in eukaryotes. In this work, we use a yeast display-based ncAA incorporation reporter platform with fluorescence-activated cell sorting to screen libraries of aaRSs in high throughput for (1) the incorporation of ncAAs not previously encoded in yeast; (2) the improvement of the performance of an existing aaRS; (3) highly selective OTSs capable of discriminating between closely related ncAA analogues; and (4) OTSs exhibiting enhanced polyspecificity to support translation with structurally diverse sets of ncAAs. The number of previously undiscovered aaRS variants we report in this work more than doubles the total number of translationally active aaRSs available for genetic code manipulation in yeast. The success of myriad screening strategies has important implications related to the fundamental properties and evolvability of aaRSs. Furthermore, access to OTSs with diverse activities and specific or polyspecific properties is invaluable for a range of applications within chemical biology, synthetic biology, and protein engineering.

Antigen binding by conformational selection in near-germline antibodies

Conformational flexibility in antibody-combining sites has been hypothesized to facilitate polyspecificity toward multiple unique epitopes and enable the limited germline repertoire to match an overwhelming diversity of potential antigens; however, elucidating the mechanisms of antigen recognition by flexible antibodies has been understandably challenging. Here, multiple liganded and unliganded crystal structures of the near-germline anticarbohydrate antibodies S25–2 and S25–39 are reported, which reveal an unprecedented diversity of complementarity-determining region H3 conformations in apparent equilibrium. These structures demonstrate that at least some germline or near-germline antibodies are flexible entities sensitive to their chemical environments, with conformational selection available as an evolved mechanism that preserves the inherited ability to recognize common pathogens while remaining adaptable to new threats.

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V_H: variable heavy; V_L: variable light.

Polyreactivity and polyspecificity in therapeutic antibody development: risk factors for failure in preclinical and clinical development campaigns

Antibody-based drugs, which now represent the dominant biologic therapeutic modality, are used to modulate disparate signaling pathways across diverse disease indications. One fundamental premise that has driven this therapeutic antibody revolution is the belief that each monoclonal antibody exhibits exquisitely specific binding to a single-drug target. Herein, we review emerging evidence in antibody off-target binding and relate current key findings to the risk of failure in therapeutic development. We further summarize the current state of understanding of structural mechanisms underpining the different phenomena that may drive polyreactivity and polyspecificity, and highlight current thinking on how de-risking studies may be best implemented in the screening triage. We conclude with a summary of what we believe to be key observations in the field to date, and a call for the wider antibody research community to work together to build the tools needed to maximize our understanding in this nascent area.

Beyond affinity: selection of antibody variants with optimal biophysical properties and reduced immunogenicity from mammalian display libraries

The early phase of protein drug development has traditionally focused on target binding properties leading to a desired mode of therapeutic action. As more protein therapeutics pass through the development pipeline; however, it is clear that non-optimal biophysical properties can emerge, particularly as proteins are formulated at high concentrations, causing aggregation or polyreactivity. Such late-stage "developability" problems can lead to delay or failure in traversing the development process. Aggregation propensity is also correlated with increased immunogenicity, resulting in expensive, late-stage clinical failures. Using nucleases-directed integration, we have constructed large mammalian display libraries where each cell contains a single antibody gene/cell inserted at a single locus, thereby achieving transcriptional normalization. We show a strong correlation between poor biophysical properties and display level achieved in mammalian cells, which is not replicated by yeast display. Using two well-documented examples of antibodies with poor biophysical characteristics (MEDI-1912 and bococizumab), a library of variants was created based on surface hydrophobic and positive charge patches. Mammalian display was used to select for antibodies that retained target binding and permitted increased display level. The resultant variants exhibited reduced polyreactivity and reduced aggregation propensity. Furthermore, we show in the case of bococizumab that biophysically improved variants are less immunogenic than the parental molecule. Thus, mammalian display helps to address multiple developability issues during the earliest stages of lead discovery, thereby significantly de-risking the future development of protein drugs.

Directed Mutational Strategies Reveal Drug Binding and Transport by the MDR Transporters of Candida albicans

Multidrug resistance (MDR) transporters belonging to either the ATP-Binding Cassette (ABC) or Major Facilitator Superfamily (MFS) groups are major determinants of clinical drug resistance in fungi. The overproduction of these proteins enables the extrusion of incoming drugs at rates that prevent lethal effects. The promiscuity of these proteins is intriguing because they export a wide range of structurally unrelated molecules. Research in the last two decades has used multiple approaches to dissect the molecular basis of the polyspecificity of multidrug transporters. With large numbers of drug transporters potentially involved in clinical drug resistance in pathogenic yeasts, this review focuses on the drug transporters of the important pathogen Candida albicans. This organism harbors many such proteins, several of which have been shown to actively export antifungal drugs. Of these, the ABC protein CaCdr1 and the MFS protein CaMdr1 are the two most prominent and have thus been subjected to intense site-directed mutagenesis and suppressor genetics-based analysis. Numerous results point to a common theme underlying the strategy of promiscuity adopted by both CaCdr1 and CaMdr1. This review summarizes the body of research that has provided insight into how multidrug transporters function and deliver their remarkable polyspecificity.

Mobility and disorder in antibody and antigen binding sites do not prevent immunochemical recognition

The known polyspecificity of antibodies, which is crucial for efficient immune response, is determined by the conformational flexibility and intrinsic disorder encoded in local peculiarities of the amino acid sequence of antibodies within or in the vicinity of their complementarity determining regions. Similarly, epitopes represent fuzzy binding sites, which are also characterized by local structural flexibility. Existing data suggest that the efficient interactions between antigens and antibodies rely on the conformational mobility and some disorder of their binding sites and therefore can be relatively well described by the "flexible lock - adjustable key" model, whereas both, extreme order (rigid lock-and-key) and extreme disorder (viral shape-shifters) are not compatible with the efficient antigen-antibody interactions and are not present in immune interactions.

Off-target binding of an anti-amyloid beta monoclonal antibody to platelet factor 4 causes acute and chronic toxicity in cynomolgus monkeys

ABT-736 is a humanized monoclonal antibody generated to target a specific conformation of the amyloid-beta (Aβ) protein oligomer. Development of ABT-736 for Alzheimer's disease was discontinued due to severe adverse effects (AEs) observed in cynomolgus monkey toxicity studies. The acute nature of AEs observed only at the highest doses suggested potential binding of ABT-736 to an abundant plasma protein. Follow-up investigations indicated polyspecificity of ABT-736, including unintended high-affinity binding to monkey and human plasma protein platelet factor 4 (PF-4), known to be involved in heparin-induced thrombocytopenia (HIT) in humans. The chronic AEs observed at the lower doses after repeat administration in monkeys were consistent with HIT pathology. Screening for a backup antibody revealed that ABT-736 possessed additional unintended binding characteristics to other, unknown factors. A subsequently implemented screening funnel focused on nonspecific binding led to the identification of h4D10, a high-affinity Aβ oligomer binding antibody that did not bind PF-4 or other unintended targets and had no AEs in vivo. This strengthened the hypothesis that ABT-736 toxicity was not Aβ target-related, but instead was the consequence of polyspecificity including PF-4 binding, which likely mediated the acute and chronic AEs and the HIT-like pathology. In conclusion, thorough screening of antibody candidates for nonspecific interactions with unrelated molecules at early stages of discovery can eliminate candidates with polyspecificity and reduce potential for toxicity caused by off-target binding.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

Toward Drug-Like Multispecific Antibodies by Design

The success of antibody therapeutics is strongly influenced by their multifunctional nature that couples antigen recognition mediated by their variable regions with effector functions and half-life extension mediated by a subset of their constant regions. Nevertheless, the monospecific IgG format is not optimal for many therapeutic applications, and this has led to the design of a vast number of unique multispecific antibody formats that enable targeting of multiple antigens or multiple epitopes on the same antigen. Despite the diversity of these formats, a common challenge in generating multispecific antibodies is that they display suboptimal physical and chemical properties relative to conventional IgGs and are more difficult to develop into therapeutics. Here we review advances in the design and engineering of multispecific antibodies with drug-like properties, including favorable stability, solubility, viscosity, specificity and pharmacokinetic properties. We also highlight emerging experimental and computational methods for improving the next generation of multispecific antibodies, as well as their constituent antibody fragments, with natural IgG-like properties. Finally, we identify several outstanding challenges that need to be addressed to increase the success of multispecific antibodies in the clinic.

Physicochemical Rules for Identifying Monoclonal Antibodies with Drug-like Specificity

The ability of antibodies to recognize their target antigens with high specificity is fundamental to their natural function. Nevertheless, therapeutic antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that can lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and rapid antibody clearance. Here we report a method for predicting the overall specificity of antibodies in terms of their relative risk for displaying high levels of nonspecific or self-interactions at physiological conditions. We find that individual and combined sets of chemical rules that limit the maximum and minimum numbers of certain solvent-exposed amino acids in antibody variable regions are strong predictors of specificity for large panels of preclinical and clinical-stage antibodies. We also demonstrate how the chemical rules can be used to identify sites that mediate nonspecific interactions in suboptimal antibodies and guide the design of targeted sublibraries that yield variants with high antibody specificity. These findings can be readily used to improve the selection and engineering of antibodies with drug-like specificity.

Structural definition of polyspecific compensatory ligand recognition by P-glycoprotein

The multidrug transporter P-glycoprotein (Pgp)/ABCB1/MDR1 plays an important role in multidrug resistance (MDR) and detoxification owing to its ability to efflux an unusually large and chemically diverse set of substrates. Previous phenylalanine-to-alanine scanning mutagenesis of Pgp revealed that nearly all mutations retained full MDR function and still permitted substrate transport. This suggests that either the loss of any single aromatic side chain did not affect the ligand-binding modes or that highly adaptive and compensatory drug recognition is an intrinsic property including ligand-binding shifts that preserve function. To explore this hypothesis, the ATPase function and crystallographic localization of five single-site mutations in which the native aromatic residue directly interacted with the environmental pollutant BDE-100, as shown in previous crystal structures, were tested. Two mutants, Y303A and Y306A, showed strong BDE-100 occupancy at the original site (site 1), but also revealed a novel site 2 located on the opposing pseudo-symmetric half of the drug-binding pocket (DBP). Surprisingly, the F724A mutant structure had no detectable binding in site 1 but exhibited a novel site shifted 11 Å from site 1. ATPase studies revealed shifts in ATPase kinetics for the five mutants, but otherwise indicated a catalytically active transporter that was inhibited by BDE-100, similar to wild-type Pgp. These results emphasize a high degree of compensatory drug recognition in Pgp that is made possible by aromatic amino-acid side chains concentrated in the DBP. Compensatory recognition forms the underpinning of polyspecific drug transport, but also highlights the challenges associated with the design of therapeutics that evade efflux altogether.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

Net charge of antibody complementarity-determining regions is a key predictor of specificity

Specificity is one of the most important and complex properties that is central to both natural antibody function and therapeutic antibody efficacy. However, it has proven extremely challenging to define robust guidelines for predicting antibody specificity. Here we evaluated the physicochemical determinants of antibody specificity for multiple panels of antibodies, including >100 clinical-stage antibodies. Surprisingly, we find that the theoretical net charge of the complementarity-determining regions (CDRs) is a strong predictor of antibody specificity. Antibodies with positively charged CDRs have a much higher risk of low specificity than antibodies with negatively charged CDRs. Moreover, the charge of the entire set of six CDRs is a much better predictor of antibody specificity than the charge of individual CDRs, variable domains (VH or VL) or the entire variable fragment (Fv). The best indicators of antibody specificity in terms of CDR amino acid composition are reduced levels of arginine and lysine and increased levels of aspartic and glutamic acid. Interestingly, clinical-stage antibodies with negatively charged CDRs also have a lower risk for poor biophysical properties in general, including a reduced risk for high levels of self-association. These findings provide powerful guidelines for predicting antibody specificity and for identifying safe and potent antibody therapeutics.

Incorporation of Modified Amino Acids by Engineered Elongation Factors with Expanded Substrate Capabilities

Noncanonical amino acid (ncAA) incorporation has led to significant advances in protein science and engineering. Traditionally, in vivo incorporation of ncAAs is achieved via amber codon suppression using an engineered orthogonal aminoacyl-tRNA synthetase:tRNA pair. However, as more complex protein products are targeted, researchers are identifying additional barriers limiting the scope of currently available ncAA systems. One barrier is elongation factor Tu (EF-Tu), a protein responsible for proofreading aa-tRNAs, which substantially restricts ncAA scope by limiting ncaa-tRNA delivery to the ribosome. Researchers have responded by engineering ncAA-compatible EF-Tus for key ncAAs. However, this approach fails to address the extent to which EF-Tu inhibits efficient ncAA incorporation. Here, we demonstrate an alternative strategy leveraging computational analysis to broaden EF-Tu’s substrate specificity. Evolutionary analysis of EF-Tu and a naturally evolved specialized elongation factor, SelB, provide the opportunity to engineer EF-Tu by targeting amino acid residues that are associated with functional divergence between the two ancient paralogues. Employing amber codon suppression, in combination with mass spectrometry, we identified two EF-Tu variants with non-native substrate compatibility. Additionally, we present data showing these EF-Tu variants contribute to host organismal fitness, working cooperatively with components of native and engineered translation machinery. These results demonstrate the viability of our computational method and lend support to corresponding assumptions about molecular evolution. This work promotes enhanced polyspecific EF-Tu behavior as a viable strategy to expand ncAA scope and complements ongoing research emphasizing the importance of a comprehensive approach to further expand the genetic code.

Few Conserved Amino Acids in the Small Multidrug Resistance Transporter EmrE Influence Drug Polyselectivity

EmrE is the archetypical member of the small multidrug resistance transporter family and confers resistance to a wide range of disinfectants and dyes known as quaternary cation compounds (QCCs). The aim of this study was to examine which conserved amino acids play an important role in substrate selectivity. On the basis of a previous analysis of EmrE homologues, a total of 33 conserved residues were targeted for cysteine or alanine replacement within E. coli EmrE. The antimicrobial resistance of each EmrE variant expressed in Escherichia coli strain JW0451 (lacking dominant pump acrB) to a collection of 16 different QCCs was tested using agar spot dilution plating to determine MIC values. The results determined that only a few conserved residues were drug polyselective, based on ≥4-fold decreases in MIC values: the active-site residue E14 (E14D and E14A) and 4 additional conserved residues (A10C, F44C, L47C, W63A). EmrE variants I11C, V15C, P32C, I62C, L93C, and S105C enhanced resistance to polyaromatic QCCs, while the remaining EmrE variants reduced resistance to one or more QCCs with shared chemical features: acylation, tri- and tetraphenylation, aromaticity, and dicationic charge. Mapping of EmrE variants onto transmembrane helical wheel projections using the highest resolved EmrE structure suggests that polyselective EmrE variants were located closest to the helical faces surrounding the predicted drug binding pocket, while EmrE variants with greater drug specificity mapped onto distal helical faces. This study reveals that few conserved residues are essential for drug polyselectivity and indicates that aromatic QCC selection involves a greater portion of conserved residues than that in other QCCs.

Arginine mutations in antibody complementarity-determining regions display context-dependent affinity/specificity trade-offs

Antibodies commonly accumulate charged mutations in their complementarity-determining regions (CDRs) during affinity maturation to enhance electrostatic interactions. However, charged mutations can mediate non-specific interactions, and it is unclear to what extent CDRs can accumulate charged residues to increase antibody affinity without compromising specificity. This is especially concerning for positively charged CDR mutations that are linked to antibody polyspecificity. To better understand antibody affinity/specificity trade-offs, we have selected single-chain antibody fragments specific for the negatively charged and hydrophobic Alzheimer's amyloid β peptide using weak and stringent selections for antibody specificity. Antibody variants isolated using weak selections for specificity were enriched in arginine CDR mutations and displayed low specificity. Alanine-scanning mutagenesis revealed that the affinities of these antibodies were strongly dependent on their arginine mutations. Antibody variants isolated using stringent selections for specificity were also enriched in arginine CDR mutations, but these antibodies possessed significant improvements in specificity. Importantly, the affinities of the most specific antibodies were much less dependent on their arginine mutations, suggesting that over-reliance on arginine for affinity leads to reduced specificity. Structural modeling and molecular simulations reveal unique hydrophobic environments near the arginine CDR mutations. The more specific antibodies contained arginine mutations in the most hydrophobic portions of the CDRs, whereas the less specific antibodies contained arginine mutations in more hydrophilic regions. These findings demonstrate that arginine mutations in antibody CDRs display context-dependent impacts on specificity and that affinity/specificity trade-offs are governed by the relative contribution of arginine CDR residues to the overall antibody affinity.

Crystal structure of Yersinia pestis virulence factor YfeA reveals two polyspecific metal-binding sites

Gram-negative bacteria use siderophores, outer membrane receptors, inner membrane transporters and substrate-binding proteins (SBPs) to transport transition metals through the periplasm. The SBPs share a similar protein fold that has undergone significant structural evolution to communicate with a variety of differentially regulated transporters in the cell. In Yersinia pestis, the causative agent of plague, YfeA (YPO2439, y1897), an SBP, is important for full virulence during mammalian infection. To better understand the role of YfeA in infection, crystal structures were determined under several environmental conditions with respect to transition-metal levels. Energy-dispersive X-ray spectroscopy and anomalous X-ray scattering data show that YfeA is polyspecific and can alter its substrate specificity. In minimal-media experiments, YfeA crystals grown after iron supplementation showed a threefold increase in iron fluorescence emission over the iron fluorescence emission from YfeA crystals grown from nutrient-rich conditions, and YfeA crystals grown after manganese supplementation during overexpression showed a fivefold increase in manganese fluorescence emission over the manganese fluorescence emission from YfeA crystals grown from nutrient-rich conditions. In all experiments, the YfeA crystals produced the strongest fluorescence emission from zinc and could not be manipulated otherwise. Additionally, this report documents the discovery of a novel surface metal-binding site that prefers to chelate zinc but can also bind manganese. Flexibility across YfeA crystal forms in three loops and a helix near the buried metal-binding site suggest that a structural rearrangement is required for metal loading and unloading.

A simple mathematical approach to the analysis of polypharmacology and polyspecificity data

There many possible types of drug-target interactions, because there are a surprising number of ways in which drugs and their targets can associate with one another.  These relationships are expressed as polypharmacology and polyspecificity.  Polypharmacology is the capability of a given drug to exhibit activity with respect to multiple drug targets, which are not necessarily in the same activity class. Adverse drug reactions ('side effects') are its principal manifestation, but polypharmacology is also playing a role in the repositioning of existing drugs for new therapeutic indications.  Polyspecificity, on the other hand, is the capability of a given target to exhibit activity with respect to multiple, structurally dissimilar drugs.  That these concepts are closely related to one another is, surprisingly, not well known.  It will be shown in this work that they are, in fact, mathematically related to one another and are in essence 'two sides of the same coin'.  Hence, information on polypharmacology provides equivalent information on polyspecificity, and vice versa. Networks are playing an increasingly important role in biological research. Drug-target networks, in particular, are made up of drug nodes that are linked to specific target nodes if a given drug is active with respect to that target.  Such networks provide a graphic depiction of polypharmacology and polyspecificity.  However, by their very nature they can obscure information that may be useful in their interpretation and analysis.  This work will show how such latent information can be used to determine bounds for the degrees of polypharmacology and polyspecificity, and how to estimate other useful features associated with the lack of completeness of most drug-target datasets.

Structures of the Multidrug Transporter P-glycoprotein Reveal Asymmetric ATP Binding and the Mechanism of Polyspecificity

P-glycoprotein (P-gp) is a polyspecific ATP-dependent transporter linked to multidrug resistance in cancer; it plays important roles in determining the pharmacokinetics of many drugs. Understanding the structural basis of P-gp, substrate polyspecificity has been hampered by its intrinsic flexibility, which is facilitated by a 75-residue linker that connects the two halves of P-gp. Here we constructed a mutant murine P-gp with a shortened linker to facilitate structural determination. Despite dramatic reduction in rhodamine 123 and calcein-AM transport, the linker-shortened mutant P-gp possesses basal ATPase activity and binds ATP only in its N-terminal nucleotide-binding domain. Nine independently determined structures of wild type, the linker mutant, and a methylated P-gp at up to 3.3 Å resolution display significant movements of individual transmembrane domain helices, which correlated with the opening and closing motion of the two halves of P-gp. The open-and-close motion alters the surface topology of P-gp within the drug-binding pocket, providing a mechanistic explanation for the polyspecificity of P-gp in substrate interactions.

Specificity, polyspecificity, and heterospecificity of antibody-antigen recognition

The concept of antibody specificity is analyzed and shown to reside in the ability of an antibody to discriminate between two antigens. Initially, antibody specificity was attributed to sequence differences in complementarity determining regions (CDRs), but as increasing numbers of crystallographic antibody-antigen complexes were elucidated, specificity was analyzed in terms of six antigen-binding regions (ABRs) that only roughly correspond to CDRs. It was found that each ABR differs significantly in its amino acid composition and tends to bind different types of amino acids at the surface of proteins. In spite of these differences, the combined preference of the six ABRs does not allow epitopes to be distinguished from the rest of the protein surface. These findings explain the poor success of past and newly proposed methods for predicting protein epitopes. Antibody polyspecificity refers to the ability of one antibody to bind a large variety of epitopes in different antigens, and this property explains how the immune system develops an antibody repertoire that is able to recognize every antigen the system is likely to encounter. Antibody heterospecificity arises when an antibody reacts better with another antigen than with the one used to raise the antibody. As a result, an antibody may sometimes appear to have been elicited by an antigen with which it is unable to react. The implications of antibody polyspecificity and heterospecificity in vaccine development are pointed out.

Frequency Patterns of T-Cell Exposed Amino Acid Motifs in Immunoglobulin Heavy Chain Peptides Presented by MHCs

Immunoglobulins are highly diverse protein sequences that are processed and presented to T-cells by B-cells and other antigen presenting cells. We examined a large dataset of immunoglobulin heavy chain variable regions (IGHV) to assess the diversity of T-cell exposed motifs (TCEMs). TCEM comprise those amino acids in a MHC-bound peptide, which face outwards, surrounded by the MHC histotope, and which engage the T-cell receptor. Within IGHV there is a distinct pattern of predicted MHC class II binding and a very high frequency of re-use of the TCEMs. The re-use frequency indicates that only a limited number of different cognate T-cells are required to engage many different clonal B-cells. The amino acids in each outward-facing TCEM are intercalated with the amino acids of inward-facing MHC groove-exposed motifs (GEM). Different GEM may have differing, allele-specific, MHC binding affinities. The intercalation of TCEM and GEM in a peptide allows for a vast combinatorial repertoire of epitopes, each eliciting a different response. Outcome of T-cell receptor binding is determined by overall signal strength, which is a function of the number of responding T-cells and the duration of engagement. Hence, the frequency of TCEM re-use appears to be an important determinant of whether a T-cell response is stimulatory or suppressive. The frequency distribution of TCEMs implies that somatic hypermutation is followed by T-cell clonal expansion that develops along repeated pathways. The observations of TCEM and GEM derived from immunoglobulins suggest a relatively simple, yet powerful, mechanism to correlate T-cell polyspecificity, through re-use of TCEMs, with a very high degree of specificity achieved by combination with a diversity of GEMs. The frequency profile of TCEMs also points to an economical mechanism for maintaining T-cell memory, recall, and self-discrimination based on an endogenously generated profile of motifs.