Structure-based non-canonical amino acid design to covalently crosslink an antibody-antigen complex

Engineering proteins with catechol chemistry for biotechnological applications

Developing proteins with increased chemical space by expanding the amino acids alphabet has been an emerging technique to compete for the obstacle encountered by their need in various applications. 3,4-Dihydroxyphenylalanine (L-DOPA) catecholic unnatural amino acid is abundantly present in mussels foot proteins through post-translational modification of tyrosine to give a strong adhesion toward wet rocks. L-DOPA forms: bidentate coordination, H-bonding, metal-ligand complexes, long-ranged electrostatic, and van der Waals interactions via a pair of donor hydroxyl groups. Incorporating catechol in proteins through genetic code expansion paved the way for developing: protein-based bio-sensor, implant coating, bio-conjugation, adhesive bio-materials, biocatalyst, metal interaction and nano-biotechnological applications. The increased chemical spaces boost the protein properties by offering a new chemically active interaction ability to the protein. Here, we review the technique employed to develop a genetically expanded organism with catechol to provide novel properties and functionalities; and we highlight the importance of L-DOPA incorporated proteins in biomedical and industrial fields.

Design, Construction, and Validation of a Yeast-Displayed Chemically Expanded Antibody Library

In vitro display technologies, exemplified by phage and yeast display, have emerged as powerful platforms for antibody discovery and engineering. However, the identification of antibodies that disrupt target functions beyond binding remains a challenge. In particular, there are very few strategies that support identification and engineering of either protein-based irreversible binders or inhibitory enzyme binders. Expanding the range of chemistries in antibody libraries has the potential to lead to efficient discovery of function-disrupting antibodies. In this work, we describe a yeast display-based platform for the discovery of chemically diversified antibodies. We constructed a billion-member antibody library that supports the presentation of a range of chemistries within antibody variable domains via noncanonical amino acid (ncAA) incorporation and subsequent bioorthogonal click chemistry conjugations. Use of a polyspecific orthogonal translation system enables introduction of chemical groups with various properties, including photo-reactive, proximity-reactive, and click chemistry-enabled functional groups for library screening. We established conjugation conditions that facilitate modification of the full library, demonstrating the feasibility of sorting the full billion-member library in "protein-small molecule hybrid" format in future work. Here, we conducted initial library screens after introducing O-(2-bromoethyl)tyrosine (OBeY), a weakly electrophilic ncAA capable of undergoing proximity-induced crosslinking to a target. Enrichments against donkey IgG and protein tyrosine phosphatase 1B (PTP1B) each led to the identification of several OBeY-substituted clones that bind to the targets of interest. Flow cytometry analysis on the yeast surface confirmed higher retention of binding for OBeY-substituted clones compared to clones substituted with ncAAs lacking electrophilic side chains after denaturation. However, subsequent crosslinking experiments in solution with ncAA-substituted clones yielded inconclusive results, suggesting that weakly reactive OBeY side chain is not sufficient to drive robust crosslinking in the clones isolated here. Nonetheless, this work establishes a multi-modal, chemically expanded antibody library and demonstrates the feasibility of conducting discovery campaigns in chemically expanded format. This versatile platform offers new opportunities for identifying and characterizing antibodies with properties beyond what is accessible with the canonical amino acids, potentially enabling discovery of new classes of reagents, diagnostics, and even therapeutic leads.

Single-Cell B-Cell Sequencing to Generate Natively Paired scFab Yeast Surface Display Libraries

The immune cell profiling capabilities of single-cell RNA sequencing (scRNA-seq) are powerful tools that can be applied to the design of theranostic monoclonal antibodies (mAbs). Using scRNA-seq to determine natively paired B-cell receptor (BCR) sequences of immunized mice as a starting point for design, this method outlines a simplified workflow to express single-chain antibody fragments (scFabs) on the surface of yeast for high-throughput characterization and further refinement with directed evolution experiments. While not extensively detailed in this chapter, this method easily accommodates the implementation of a growing body of in silico tools that improve affinity and stability among a range of other developability criteria (e.g., solubility and immunogenicity).

Yeast Display Enables Identification of Covalent Single-Domain Antibodies against Botulinum Neurotoxin Light Chain A

While covalent drug discovery is reemerging as an important route to small-molecule therapeutic leads, strategies for the discovery and engineering of protein-based irreversible binding agents remain limited. Here, we describe the use of yeast display in combination with noncanonical amino acids (ncAAs) to identify irreversible variants of single-domain antibodies (sdAbs), also called VHHs and nanobodies, targeting botulinum neurotoxin light chain A (LC/A). Starting from a series of previously described, structurally characterized sdAbs, we evaluated the properties of antibodies substituted with reactive ncAAs capable of forming covalent bonds with nearby groups after UV irradiation (when using 4-azido-l-phenylalanine) or spontaneously (when using O-(2-bromoethyl)-l-tyrosine). Systematic evaluations in yeast display format of more than 40 ncAA-substituted variants revealed numerous clones that retain binding function while gaining either UV-mediated or spontaneous crosslinking capabilities. Solution-based analyses indicate that ncAA-substituted clones exhibit site-dependent target specificity and crosslinking capabilities uniquely conferred by ncAAs. Interestingly, not all ncAA substitution sites resulted in crosslinking events, and our data showed no apparent correlation between detected crosslinking levels and distances between sdAbs and LC/A residues. Our findings highlight the power of yeast display in combination with genetic code expansion in the discovery of binding agents that covalently engage their targets. This platform streamlines the discovery and characterization of antibodies with therapeutically relevant properties that cannot be accessed in the conventional genetic code.

Assessing the predictive capabilities of design heuristics and coarse-grain simulation toward understanding and optimizing site-specific covalent immobilization of β-lactamase

For industrial applications, covalent immobilization of enzymes provides minimum leakage, recoverability, reusability, and high stability. Yet, the suitability of a given site on the enzyme for immobilization remains a trial-and-error procedure. Here, we investigate the reliability of design heuristics and a coarse-grain molecular simulation in predicting the optimum sites for covalent immobilization of TEM-1 β-lactamase. We utilized E. coli-lysate-based cell-free protein synthesis (CFPS) to produce variants containing a site-specific incorporated unnatural amino acid with a unique moiety to facilitate site directed covalent immobilization. To constrain the number of potential immobilization sites, we investigated the predictive capability of several design heuristics. The suitability of immobilization sites was determined by analyzing expression yields, specific activity, immobilization efficiency, and stability of variants. These experimental findings are compared with coarse-grain simulation of TEM-1 domain stability and thermal stability and analyzed for a priori predictive capabilities. This work demonstrates that the design heuristics successfully identify a subset of locations for experimental validation. Specifically, the nucleotide following amber stop codon and domain stability correlate well with the expression yield and specific activity of the variants, respectively. Our approach highlights the advantages of combining coarse-grain simulation and high-throughput experimentation using CFPS to identify optimal enzyme immobilization sites. This article is protected by copyright. All rights reserved.

Biosynthesis and Genetic Incorporation of 3,4-Dihydroxy-L-Phenylalanine into Proteins in Escherichia coli

While 20 canonical amino acids are used by most organisms for protein synthesis, the creation of cells that can use noncanonical amino acids (ncAAs) as additional protein building blocks holds great promise for preparing novel medicines and for studying complex questions in biological systems. However, only a small number of biosynthetic pathways for ncAAs have been reported to date, greatly restricting our ability to generate cells with ncAA building blocks. In this study, we report the creation of a completely autonomous bacterium that utilizes 3,4-dihydroxy-L-phenylalanine (DOPA) as its 21^st amino acid building block. Like canonical amino acids, DOPA can be biosynthesized without exogenous addition and can be genetically incorporated into proteins in a site-specific manner. Equally important, the protein production yield of DOPA-containing proteins from these autonomous cells is greater than that of cells exogenously fed with 9 mM DOPA. The unique catechol moiety of DOPA can be used as a versatile handle for site-specific protein functionalizations via either oxidative coupling or strain-promoted oxidation-controlled cyclooctyne-1,2-quinone (SPOCQ) cycloaddition reactions. We further demonstrate the use of these autonomous cells in preparing fluorophore-labeled anti-human epidermal growth factor 2 (HER2) antibodies for the detection of HER2 expression on cancer cells.

Genetically encoded dihydroxyphenylalanine coupled with tyrosinase for strain promoted labeling

Protein modifications through genetic code engineering have a remarkable impact on macromolecule engineering, protein translocation, protein-protein interaction, and cell biology. We used the newly developed molecular biology approach, genetic code engineering, for fine-tuning of proteins for biological availability. Here, we have introduced 3, 4-dihydroxy-l-phenylalanine in recombinant proteins by selective pressure incorporation method for protein-based cell labeling applications. The congener proteins treated with tyrosinase convert 3, 4-dihydroxy-l-phenylalanine to dopaquinone for strain-promoted click chemistry. Initially, the single-step Strain-Promoted Oxidation-Controlled Cyclooctyne-1,2-quinone Cycloaddition was studied using tyrosinase catalyzed congener protein and optimized the temporally controlled conjugation with (1R,8S,9s)-Bicyclo[6.1.0]non-4-yn-9-ylmethanol. Then, the feasibility of tyrosinase-treated congener annexin A5 with easily reactive quinone functional moiety was conjugated with fluorescent tag dibenzocyclooctyne-PEG4-TAMRA for labeling of apoptotic cells. Thus, the congener proteins-based products demonstrate selective cell labeling and apoptosis detection in EA.hy926 cells even after the protein modifications. Hence, genetic code engineering can be coupled with click chemistry to develop various protein-based fluorescent labels.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Combining experimental techniques with molecular dynamics to investigate the impact of different enzymatic hydrolysis of β-lactoglobulin on the antigenicity reduction

β-Lactoglobulin (β-LG) is one of the major food allergens. Enzymatic hydrolysis is a promising strategy to reduce the antigenicity of β-LG in industrial production. The relationship between the cleavage sites of β-LG by protease and its antigenic active sites were explored in this study. Molecular docking and molecular dynamics (MD) were used to analyze the active sites and interaction force of β-LG and IgG antibody. Whey protein was hydrolyzed by four specific enzymes and the antigenicity of the hydrolysates were determined by ELISA. The results of MD showed that the amino acid residue Gln155 (-4.48 kcal mol^-1) played the most important roles in the process of binding. Hydrolysates produced by AY-10, which was the only one with specificity towards cleavage sites next to a Gln, had the lowest antigenicity at the same hydrolysis degree. Antigenicity decrease was related to the energy contribution of the cleavage site in the active sites.

Genetic Incorporation of Biosynthesized L-dihydroxyphenylalanine (DOPA) and Its Application to Protein Conjugation

L-dihydroxyphenylalanine (DOPA) is an amino acid found in the biosynthesis of catecholamines in animals and plants. Because of its particular biochemical properties, the amino acid has multiple uses in biochemical applications. This report describes a protocol for the genetic incorporation of biosynthesized DOPA and its application to protein conjugation. DOPA is biosynthesized by a tyrosine phenol-lyase (TPL) from catechol, pyruvate, and ammonia, and the amino acid is directly incorporated into proteins by the genetic incorporation method using an evolved aminoacyl-tRNA and aminoacyl-tRNA synthetase pair. This direct incorporation system efficiently incorporates DOPA with little incorporation of other natural amino acids and with better protein yield than the previous genetic incorporation system for DOPA. Protein conjugation with DOPA-containing proteins is efficient and site-specific and shows its usefulness for various applications. This protocol provides protein scientists with detailed procedures for the efficient biosynthesis of mutant proteins containing DOPA at desired sites and their conjugation for industrial and pharmaceutical applications.

Genetic incorporation of l-dihydroxyphenylalanine (DOPA) biosynthesized by a tyrosine phenol-lyase

l-Dihydroxyphenylalanine (DOPA) was biosynthesized by a tyrosine-phenol lyase from catechol, pyruvate, and ammonia in Escherichia coli, and the biosynthesized amino acid was directly incorporated into proteins. Three biochemical experiments with mutant proteins containing DOPA confirmed the genetic incorporation of biosynthesized DOPA, and revealed its potential for various biochemical applications.

A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design

Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.

Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint

Ab structure prediction has made great strides, but accurately modeling CDR H3 loops remains elusive. Unlike the other five CDR loops, CDR H3 does not adopt canonical conformations and must be modeled de novo. During Antibody Modeling Assessment II, we found that biasing simulations toward kinked conformations enables generating low-root mean square deviation models (Weitzner et al. 2014. Proteins 82: 1611-1623), and since then, we have presented new geometric parameters defining the kink conformation (Weitzner et al. 2015. Structure 23: 302-311). In this study, we use these parameters to develop a new biasing constraint. When applied to a benchmark set of high-quality CDR H3 loops, the average minimum root mean square deviation sampled is 0.93 Å, compared with 1.34 Å without the constraint. We then test the performance of the constrained de novo method for homology modeling and rigid-body docking and present the results for 1) the Antibody Modeling Assessment II targets, 2) the 2009 RosettaAntibody benchmark set, and 3) the high-quality set.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Antibody conjugates with unnatural amino acids

Antibody conjugates are important in many areas of medicine and biological research, and antibody-drug conjugates (ADCs) are becoming an important next generation class of therapeutics for cancer treatment. Early conjugation technologies relied upon random conjugation to multiple amino acid side chains, resulting in heterogeneous mixtures of labeled antibody. Recent studies, however, strongly support the notion that site-specific conjugation produces a homogeneous population of antibody conjugates with improved pharmacologic properties over randomly coupled molecules. Genetically incorporated unnatural amino acids (uAAs) allow unique orthogonal coupling strategies compared to those used for the 20 naturally occurring amino acids. Thus, uAAs provide a novel paradigm for creation of next generation ADCs. Additionally, uAA-based site-specific conjugation could also empower creation of additional multifunctional conjugates important as biopharmaceuticals, diagnostics, or reagents.

The origin of CDR H3 structural diversity

Antibody complementarity determining region (CDR) H3 loops are critical for adaptive immunological functions. Although the other five CDR loops adopt predictable canonical structures, H3 conformations have proven unclassifiable, other than an unusual C-terminal "kink" present in most antibodies. To determine why the majority of H3 loops are kinked and to learn whether non-antibody proteins have loop structures similar to those of H3, we searched a set of 15,679 high-quality non-antibody structures for regions geometrically similar to the residues immediately surrounding the loop. By incorporating the kink into our search, we identified 1,030 H3-like loops from 632 protein families. Some protein families, including PDZ domains, appear to use the identified region for recognition and binding. Our results suggest that the kink is conserved in the immunoglobulin heavy chain fold because it disrupts the β-strand pairing at the base of the loop. Thus, the kink is a critical driver of the observed structural diversity in CDR H3.

Antibody informatics for drug discovery

More and more antibody therapeutics are being approved every year, mainly due to their high efficacy and antigen selectivity. However, it is still difficult to identify the antigen, and thereby the function, of an antibody if no other information is available. There are obstacles inherent to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii) antibody numbering and IMGT. Here, we review "antibody informatics," which may integrate the above three fields so that bridging the gaps between industrial needs and academic solutions can be accelerated. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.

Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization

Antibody Modeling Assessment II (AMA-II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non-H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub-Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo CDR H3 modeling remains challenging at long loop lengths, but constraining the C-terminal end of H3 to a kinked conformation allows near-native conformations to be sampled more frequently. We also found that incorrect VL -VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL -VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody.

Protein folding and de novo protein design for biotechnological applications

In the postgenomic era, the medical/biological fields are advancing faster than ever. However, before the power of full-genome sequencing can be fully realized, the connection between amino acid sequence and protein structure, known as the protein folding problem, needs to be elucidated. The protein folding problem remains elusive, with significant difficulties still arising when modeling amino acid sequences lacking an identifiable template. Understanding protein folding will allow for unforeseen advances in protein design; often referred to as the inverse protein folding problem. Despite challenges in protein folding, de novo protein design has recently demonstrated significant success via computational techniques. We review advances and challenges in protein structure prediction and de novo protein design, and highlight their interplay in successful biotechnological applications.