Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening

Rationalizing mAb Candidate Screening Using a Single Holistic Developability Parameter

A framework for the rational selection of a minimal suite of nondegenerate developability assays (DAs) that maximize insight into candidate developability or storage stability is lacking. To address this, we subjected nine formulation:mAbs to 12 mechanistically distinct DAs together with measurement of their accelerated and long-term storage stability. We show that it is possible to identify a reduced set of key variables from this suite of DAs by using orthogonal statistical methods. We exemplify our approach by predicting the rank formulation:mAb degradation rate at 25 °C (determined over 6 months) using just five DAs that can be measured in less than 1 day, spanning a range of physicochemical features. Implementing such approaches focuses on resources, thus increasing sustainability and decreasing development costs.

Beyond Single Clones: High-Throughput Sequencing in Antibody Discovery

Antibody repertoire sequencing and display library screening are powerful approaches for antibody discovery and engineering that can connect DNA sequence with antibody function. Antibody display and screening studies have made a tremendous impact on immunology and biotechnology over the last decade, accelerated by technological advances in high-throughput DNA sequencing techniques. Indeed, bioinformatic analysis of antibody DNA library data has now taken a central role in modern antibody drug discovery, and is also critical for many ongoing studies of human immune development. Here, we describe current trends in antibody DNA library screening and analysis, and introduce a selection of protocols describing fundamental bioinformatic techniques to enable scientists to efficiently study antibody DNA libraries.

Cell-Free Gene Expression: Methods and Applications

Cell-free gene expression (CFE) systems empower synthetic biologists to build biological molecules and processes outside of living intact cells. The foundational principle is that precise, complex biomolecular transformations can be conducted in purified enzyme or crude cell lysate systems. This concept circumvents mechanisms that have evolved to facilitate species survival, bypasses limitations on molecular transport across the cell wall, and provides a significant departure from traditional, cell-based processes that rely on microscopic cellular "reactors." In addition, cell-free systems are inherently distributable through freeze-drying, which allows simple distribution before rehydration at the point-of-use. Furthermore, as cell-free systems are nonliving, they provide built-in safeguards for biocontainment without the constraints attendant on genetically modified organisms. These features have led to a significant increase in the development and use of CFE systems over the past two decades. Here, we discuss recent advances in CFE systems and highlight how they are transforming efforts to build cells, control genetic networks, and manufacture biobased products.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Predicting adaptive immune receptor specificities by machine learning is a data generation problem

Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted form antibodies, and T cell receptors (TCRs)-is critical for understanding immune responses and advancing immunotherapy and drug discovery. Immune receptors exhibit extensive diversity in their variable domains, enabling them to interact with a plethora of antigens. Despite the significant progress made by AI tools such as AlphaFold in predicting protein structures, challenges remain in accurately modeling the structure and specificity of immune receptors, primarily due to the limited availability of high-quality crystal structures and the complexity of immune receptor-antigen interactions. In this perspective, we highlight recent advancements in sequence-based and structure-based data generation for immune receptors, which are crucial for training machine learning models that predict receptor specificity. We discuss the current bottlenecks and potential future directions in generating and utilizing high-dimensional datasets for predicting and designing the specificity of antibodies and TCRs.

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

Nanobodies: From High-Throughput Identification to Therapeutic Development

The camelid single-domain antibody fragment, commonly referred to as a nanobody, achieves the targeting power of conventional monoclonal antibodies (mAbs) at only a fraction of their size. Isolated from camelid species (including llamas, alpacas, and camels), their small size at ∼15 kDa, low structural complexity, and high stability compared with conventional antibodies have propelled nanobody technology into the limelight of biologic development. Nanobodies are proving themselves to be a potent complement to traditional mAb therapies, showing success in the treatment of, for example, autoimmune diseases and cancer, and more recently as therapeutic options to treat infectious diseases caused by rapidly evolving biological targets such as the SARS-CoV-2 virus. This review highlights the benefits of applying a proteomic approach to identify diverse nanobody sequences against a single antigen. This proteomic approach coupled with conventional yeast/phage display methods enables the production of highly diverse repertoires of nanobodies able to bind the vast epitope landscape of an antigen, with epitope sampling surpassing that of mAbs. Additionally, we aim to highlight recent findings illuminating the structural attributes of nanobodies that make them particularly amenable to comprehensive antigen sampling and to synergistic activity-underscoring the powerful advantage of acquiring a large, diverse nanobody repertoire against a single antigen. Lastly, we highlight the efforts being made in the clinical development of nanobodies, which have great potential as powerful diagnostic reagents and treatment options, especially when targeting infectious disease agents.

Functionalized Polysaccharides Improve Sensitivity of Tyramide/Peroxidase Proximity Labeling Assays through Electrostatic Interactions

High-throughput assays that efficiently link genotype and phenotype with high fidelity are key to successful enzyme engineering campaigns. Among these assays, the tyramide/peroxidase proximity labeling method converts the product of an enzymatic reaction of a surface expressed enzyme to a highly reactive fluorescent radical, which labels the cell surface. In this context, maintaining the proximity of the readout reagents to the cell surface is crucial to prevent crosstalk and ensure that short-lived radical species react before diffusing away. Here, we investigated improvements in tyramide/peroxidase proximity labeling for enzyme screening. We modified chitosan (Cs) chains with horseradish peroxidase (HRP) and evaluated the effects of these conjugates on the efficiency of proximity labeling reactions on yeast cells displaying d-amino acid oxidase. By tethering HRP to chitosan through different chemical approaches, we localized the auxiliary enzyme close to the cell surface and enhanced the sensitivity of tyramide-peroxidase labeling reactions. We found that immobilizing HRP onto chitosan through a 5 kDa PEG linker improved labeling sensitivity by over 3.5-fold for substrates processed with a low turnover rate (e.g., d-lysine), while the sensitivity of the labeling for high activity substrates (e.g., d-alanine) was enhanced by over 0.6-fold. Such improvements in labeling efficiency broaden the range of enzymes and conditions that can be studied and screened by tyramide/peroxidase proximity labeling.

Next-Generation Therapeutic Antibodies for Cancer Treatment: Advancements, Applications, and Challenges

The field of cancer treatment has evolved significantly over the last decade with the emergence of next-generation therapeutic antibodies. Conventional treatments like chemotherapy pose significant challenges, including adverse side effects. Monoclonal antibodies have paved the way for more targeted and effective interventions. The evolution from chimeric to humanized and fully human antibodies has led to a reduction in immunogenicity and enhanced tolerance in vivo. The advent of next-generation antibodies, including bispecific antibodies, nanobodies, antibody-drug conjugates, glyco-engineered antibodies, and antibody fragments, represents a leap forward in cancer therapy. These innovations offer increased potency, adaptability, and reduced drug resistance. Challenges such as target validation, immunogenicity, and high production costs exist. However, technological advancements in antibody engineering techniques provide optimism for addressing these issues. The future promises a paradigm shift, where ongoing research will propel these powerful antibodies to the forefront, revolutionizing the fight against cancer and creating new preventive and curative treatments. This review provides an overview of three next-generation antibody-based molecules, namely bispecific antibodies, antibody-drug conjugates, and nanobodies that have shown promising results in cancer treatment. It discusses the evolution of antibodies from conventional forms to next-generation molecules, along with their applications in cancer treatment, production methods, and associated challenges. The review aims to offer researchers insights into the evolving landscape of next-generation antibody-based cancer therapeutics and their potential to revolutionize treatment strategies.

Single-molecule structural and kinetic studies across sequence space

At the core of molecular biology lies the intricate interplay between sequence, structure, and function. Single-molecule techniques provide in-depth dynamic insights into structure and function, but laborious assays impede functional screening of large sequence libraries. We introduce high-throughput Single-molecule Parallel Analysis for Rapid eXploration of Sequence space (SPARXS), integrating single-molecule fluorescence with next-generation sequencing. We applied SPARXS to study the sequence-dependent kinetics of the Holliday junction, a critical intermediate in homologous recombination. By examining the dynamics of millions of Holliday junctions, covering thousands of distinct sequences, we demonstrated the ability of SPARXS to uncover sequence patterns, evaluate sequence motifs, and construct thermodynamic models. SPARXS emerges as a versatile tool for untangling the mechanisms that underlie sequence-specific processes at the molecular scale.

Encoding and display technologies for combinatorial libraries in drug discovery: The coming of age from biology to therapy

Drug discovery is a sophisticated process that incorporates scientific innovations and cutting-edge technologies. Compared to traditional bioactivity-based screening methods, encoding and display technologies for combinatorial libraries have recently advanced from proof-of-principle experiments to promising tools for pharmaceutical hit discovery due to their high screening efficiency, throughput, and resource minimization. This review systematically summarizes the development history, typology, and prospective applications of encoding and displayed technologies, including phage display, ribosomal display, mRNA display, yeast cell display, one-bead one-compound, DNA-encoded, peptide nucleic acid-encoded, and new peptide-encoded technologies, and examples of preclinical and clinical translation. We discuss the progress of novel targeted therapeutic agents, covering a spectrum from small-molecule inhibitors and nonpeptidic macrocycles to linear, monocyclic, and bicyclic peptides, in addition to antibodies. We also address the pending challenges and future prospects of drug discovery, including the size of screening libraries, advantages and disadvantages of the technology, clinical translational potential, and market space. This review is intended to establish a comprehensive high-throughput drug discovery strategy for scientific researchers and clinical drug developers.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Engineering is evolution: a perspective on design processes to engineer biology

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.

Eukaryotic ribosome display for antibody discovery: A review

Monoclonal antibody biologics have significantly transformed the therapeutic landscape within the biopharmaceutical industry, partly due to the utilisation of discovery technologies such as the hybridoma method and phage display. While these established platforms have streamlined the development process to date, their reliance on cell transformation for antibody identification faces limitations related to library diversification and the constraints of host cell physiology. Cell-free systems like ribosome display offer a complementary approach, enabling antibody selection in a completely in vitro setting while harnessing enriched cellular molecular machinery. This review aims to provide an overview of the fundamental principles underlying the ribosome display method and its potential for advancing antibody discovery and development.