Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces

Leishmania tarentolae cell-free based approach for rapid anitbody-antigen interaction analysis

Rapid techniques for producing high-quality recombinant proteins are essential for fast protein functional analysis, as well as various screening applications. Cell-free protein expression is an enabling tool in protein research capable of producing high-quality proteins within a few hours. In this chapter, we describe the use of a Leishmania tarentolae-based cell-free expression system to produce antibody fragments coupled to the analysis of their interaction with their ligands. Interaction analysis is performed using the scalable and sensitive AlphaLISA bead proximity assay. The method presented in this chapter offers a rapid and inexpensive approach for production of putative interacting protein pairs, as well as a multiplexable approach for their rapid interaction analysis.

Biomolecular Recognition of the Glycan Neoantigen CA19-9 by Distinct Antibodies

Glycans decorate the cell surface, secreted glycoproteins and glycolipids, and altered glycans are often found in cancers. Despite their high diagnostic and therapeutic potential, however, glycans are polar and flexible molecules that are quite challenging for the development and design of high-affinity binding antibodies. To understand the mechanisms by which glycan neoantigens are specifically recognized by antibodies, we analyze the biomolecular recognition of the tumor-associated carbohydrate antigen CA19-9 by two distinct antibodies using X-ray crystallography. Despite the potential plasticity of glycans and the very different antigen-binding surfaces presented by the antibodies, both structures reveal an essentially identical extended CA19-9 conformer, suggesting that this conformer's stability selects the antibodies. Starting from the bound structure of one of the antibodies, we use the AbLIFT computational algorithm to design a variant with seven core mutations in the variable domain's light-heavy chain interface that exhibits tenfold improved affinity for CA19-9. The results reveal strategies used by antibodies to specifically recognize glycan antigens and show how automated antibody-optimization methods may be used to enhance the clinical potential of existing antibodies.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Design of a Trispecific Checkpoint Inhibitor and Natural Killer Cell Engager Based on a 2 + 1 Common Light Chain Antibody Architecture

Natural killer cell engagers gained enormous interest in recent years due to their potent anti-tumor activity and favorable safety profile. Simultaneously, chicken-derived antibodies entered clinical studies paving the way for avian-derived therapeutics. In this study, we describe the affinity maturation of a common light chain (cLC)-based, chicken-derived antibody targeting EGFR, followed by utilization of the same light chain for the isolation of CD16a- and PD-L1-specific monoclonal antibodies. The resulting binders target their respective antigen with single-digit nanomolar affinity while blocking the ligand binding of all three respective receptors. Following library-based humanization, bispecific and trispecific variants in a standard 1 + 1 or a 2 + 1 common light chain format were generated, simultaneously targeting EGFR, CD16a, and PD-L1. The trispecific antibody mediated an elevated antibody-dependent cellular cytotoxicity (ADCC) in comparison to the EGFR×CD16a bispecific variant by effectively bridging EGFR/PD-L1 double-positive cancer cells with CD16a-positive effector cells. These findings represent, to our knowledge, the first detailed report on the generation of a trispecific 2 + 1 antibodies exhibiting a common light chain and illustrate synergistic effects of trispecific antigen binding. Overall, this generic procedure paves the way for the engineering of tri- and oligospecific therapeutic antibodies derived from avian immunizations.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes

MOTIVATION

Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data.

RESULTS

Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code.

AVAILABILITY AND IMPLEMENTATION

UEP algorithm can be found at: https://github.com/pepamengual/UEP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Community-Wide Experimental Evaluation of the PROSS Stability-Design Method

Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.

Mutational fitness landscapes reveal genetic and structural improvement pathways for a vaccine-elicited HIV-1 broadly neutralizing antibody

Vaccine-based elicitation of broadly neutralizing antibodies holds great promise for preventing HIV-1 transmission. However, the key biophysical markers of improved antibody recognition remain uncertain in the diverse landscape of potential antibody mutation pathways, and a more complete understanding of anti-HIV-1 fusion peptide (FP) antibody development will accelerate rational vaccine designs. Here we survey the mutational landscape of the vaccine-elicited anti-FP antibody, vFP16.02, to determine the genetic, structural, and functional features associated with antibody improvement or fitness. Using site-saturation mutagenesis and yeast display functional screening, we found that 1% of possible single mutations improved HIV-1 envelope trimer (Env) affinity, but generally comprised rare somatic hypermutations that may not arise frequently in vivo. We observed that many single mutations in the vFP16.02 Fab could enhance affinity >1,000-fold against soluble FP, although affinity improvements against the HIV-1 trimer were more measured and rare. The most potent variants enhanced affinity to both soluble FP and Env, had mutations concentrated in antibody framework regions, and achieved up to 37% neutralization breadth compared to 28% neutralization of the template antibody. Altered heavy- and light-chain interface angles and conformational dynamics, as well as reduced Fab thermal stability, were associated with improved HIV-1 neutralization breadth and potency. We also observed parallel sets of mutations that enhanced viral neutralization through similar structural mechanisms. These data provide a quantitative understanding of the mutational landscape for vaccine-elicited FP-directed broadly neutralizing antibody and demonstrate that numerous antigen-distal framework mutations can improve antibody function by enhancing affinity simultaneously toward HIV-1 Env and FP.

A matrix of structure-based designs yields improved VRC01-class antibodies for HIV-1 therapy and prevention

Passive transfer of broadly neutralizing antibodies is showing promise in the treatment and prevention of HIV-1. One class of antibodies, the VRC01 class, appears especially promising. To improve VRC01-class antibodies, we combined structure-based design with a matrix-based approach to generate VRC01-class variants that filled an interfacial cavity, used diverse third-complementarity-determining regions, reduced potential steric clashes, or exploited extended contacts to a neighboring protomer within the envelope trimer. On a 208-strain panel, variant VRC01.23LS neutralized 90% of the panel at a geometric mean IC80 less than 1 μg/ml, and in transgenic mice with human neonatal-Fc receptor, the serum half-life of VRC01.23LS was indistinguishable from that of the parent VRC01LS, which has a half-life of 71 d in humans. A cryo-electron microscopy structure of VRC01.23 Fab in complex with BG505 DS-SOSIP.664 Env trimer determined at 3.4-Å resolution confirmed the structural basis for its ~10-fold improved potency relative to VRC01. Another variant, VRC07-523-F54-LS.v3, neutralized 95% of the 208-isolated panel at a geometric mean IC80 of less than 1 μg/ml, with a half-life comparable to that of the parental VRC07-523LS. Our matrix-based structural approach thus enables the engineering of VRC01 variants for HIV-1 therapy and prevention with improved potency, breadth, and pharmacokinetics.

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.

The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes

The functional sites of many protein families are dominated by diverse backbone regions that lack secondary structure (loops) but fold stably into their functionally competent state. Nevertheless, the design of structured loop regions from scratch, especially in functional sites, has met with great difficulty. We therefore developed an approach, called AbDesign, to exploit the natural modularity of many protein families and computationally assemble a large number of new backbones by combining naturally occurring modular fragments. This strategy yielded large, atomically accurate, and highly efficient proteins, including antibodies and enzymes exhibiting dozens of mutations from any natural protein. The combinatorial backbone‐conformation space that can be accessed by AbDesign even for a modestly sized family of homologs may exceed the diversity in the entire PDB, providing the sub‐Ångstrom level of control over the positioning of active‐site groups that is necessary for obtaining highly active proteins. This manuscript describes how to implement the pipeline using code that is freely available at https://github.com/Fleishman‐Lab/AbDesign_for_enzymes.

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.

Mass Spectrometry Analysis of Intact Proteins from Crude Samples

Analysis of intact proteins by native mass spectrometry has emerged as a powerful tool for obtaining insight into subunit diversity, post-translational modifications, stoichiometry, structural arrangement, stability, and overall architecture. Typically, such an analysis is performed following protein purification procedures, which are time consuming, costly, and labor intensive. As this technology continues to move forward, advances in sample handling and instrumentation have enabled the investigation of intact proteins in situ and in crude samples, offering rapid analysis and improved conservation of the biological context. This emerging field, which involves various ion source platforms such as matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) for both spatial imaging and solution-based analysis, is expected to impact many scientific fields, including biotechnology, pharmaceuticals, and clinical sciences. In this Perspective, we discuss the information that can be retrieved by such experiments as well as the current advantages and technical challenges associated with the different sampling strategies. Furthermore, we present future directions of these MS-based methods, including current limitations and efforts that should be made to make these approaches more accessible. Considering the vast progress we have witnessed in recent years, we anticipate that the advent of further innovations enabling minimal handling of MS samples will make this field more robust, user friendly, and widespread.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.

Practically useful protein-design methods combining phylogenetic and atomistic calculations

Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.

Inhibition of fibroblast secreted QSOX1 perturbs extracellular matrix in the tumor microenvironment and decreases tumor growth and metastasis in murine cancer models

Extracellular matrix (ECM) plays an important role in tumor development and dissemination, but few points of therapeutic intervention targeting ECM of the tumor microenvironment have been exploited to date. Recent observations suggest that the enzymatic introduction of disulfide bond cross-links into the ECM may be modulated to affect cancer progression. Specifically, the disulfide bond-forming activity of the enzyme Quiescin sulfhydryl oxidase 1 (QSOX1) is required by fibroblasts to assemble ECM components for adhesion and migration of cancer cells. Based on this finding and the increased QSOX1 expression in the stroma of aggressive breast carcinomas, we developed monoclonal antibody inhibitors with the aim of preventing QSOX1 from participating in pro-metastatic ECM remodeling. Here we show that QSOX1 inhibitory antibodies decreased tumor growth and metastasis in murine cancer models and had added benefits when provided together with chemotherapy. Mechanistically, the inhibitors dampened stromal participation in tumor development, as the tumors of treated animals showed fewer myofibroblasts and poorer ECM organization. Thus, our findings demonstrate that specifically targeting excess stromal QSOX1 secreted in response to tumor-cell signaling provides a means to modulate the tumor microenvironment and may complement other therapeutic approaches in cancer.

A lipophilicity-based energy function for membrane-protein modelling and design

Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.