sdAb-DB: The Single Domain Antibody Database

Small but Mighty: Nanobodies in the Fight Against Infectious Diseases

Infectious diseases, caused by pathogenic microorganisms and capable of spreading, pose a significant threat to global public health. Developing efficient and cost-effective techniques for treating infectious diseases is crucial in curbing their progression and reducing patients' morbidity and mortality. Nanobodies (Nbs), a novel class of affinity reagents derived from unique heavy chain-only antibodies in camelids, represent the smallest intact and fully functional antigen-binding fragments. Compared with conventional antibodies and their antigen binding fragments, Nbs offer numerous advantages, including high affinity, exceptional target specificity, cost-effective production, easy accessibility, and robust stability, demonstrating immense potential in infectious disease treatment. This review introduces Nbs and focuses on discussing their mechanisms and intervention strategies in the treatment of viral and bacterial infections.

Mapping Alzheimer's Molecular Pathologies in Large-Scale Connectomics Data: A Publicly Accessible Correlative Microscopy Resource

Connectomics using volume-electron-microscopy enables mapping and analysis of neuronal networks, revealing insights into neural circuit function and dysfunction. In Alzheimer's disease (AD), where amyloid-β (Aβ) and hyperphosphorylated-Tau (pTau) are implicated, connectomics offers an approach to unravel how these molecules contribute to circuit alterations by enabling the study of these molecules within the context of the complete local neuronal and glial milieu. We present a volumetric-correlated-light-and-electron microscopy (vCLEM) protocol using fluorescent nanobodies to localize Aβ and pTau within a large-scale connectomics dataset from the hippocampus of the 3xTg AD mouse model. A key outcome of this work is a publicly accessible vCLEM dataset, featuring fluorescent labeling of Aβ and pTau in the ultrastructural context with segmented neurons, glia, and synapses. This dataset provides a unique resource for exploring AD pathology in the context of connectomics and fosters collaborative opportunities in neurodegenerative disease research. As a proof-of-principle, we uncovered new localizations of Aβ and pTau, including pTau-positive spine-like protrusions at the axon initial segment and changes in the number and size of synapses near Aβ plaques. Our vCLEM approach facilitates the discovery of both molecular and structural alterations within large-scale EM data, advancing connectomics research in Alzheimer's and other neurodegenerative diseases.

Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization

Nanobodies (Nbs), miniature antibodies consisting solely of the variable region of heavy chains, exhibit unique properties such as small size, high stability, and strong specificity, making them highly promising for disease diagnosis and treatment. The engineering production of Nbs has evolved into a mature process, involving library construction, screening, and expression purification. Different library types, including immune, naïve, and synthetic/semi-synthetic libraries, offer diverse options for various applications, while display platforms like phage display, cell surface display, and non-surface display provide efficient screening of target Nbs. Recent advancements in artificial intelligence (AI) have opened new avenues in Nb engineering. AI's exceptional performance in protein structure prediction and molecular interaction simulation has introduced novel perspectives and tools for Nb design and optimization. Integrating AI with traditional experimental methods is anticipated to enhance the efficiency and precision of Nb development, expediting the transition from basic research to clinical applications. This review comprehensively examines the latest progress in Nb engineering, emphasizing library construction strategies, display platform technologies, and AI applications. It evaluates the strengths and weaknesses of various libraries and display platforms and explores the potential and challenges of AI in predicting Nb structure, antigen-antibody interactions, and optimizing physicochemical properties.

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Nanobodies, the smallest functional antibody fragment derived from camelid heavy-chain-only antibodies, have emerged as powerful tools for diverse biomedical applications. In this comprehensive review, we discuss the structural characteristics, functional properties, and computational approaches driving the design and optimisation of synthetic nanobodies. We explore their unique antigen-binding domains, highlighting the critical role of complementarity-determining regions in target recognition and specificity. This review further underscores the advantages of nanobodies over conventional antibodies from a biosynthesis perspective, including their small size, stability, and solubility, which make them ideal candidates for economical antigen capture in diagnostics, therapeutics, and biosensing. We discuss the recent advancements in computational methods for nanobody modelling, epitope prediction, and affinity maturation, shedding light on their intricate antigen-binding mechanisms and conformational dynamics. Finally, we examine a direct example of how computational design strategies were implemented for improving a nanobody-based immunosensor, known as a Quenchbody. Through combining experimental findings and computational insights, this review elucidates the transformative impact of nanobodies in biotechnology and biomedical research, offering a roadmap for future advancements and applications in healthcare and diagnostics.

Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis

Immunotherapy, harnessing components like antibodies, cells, and cytokines, has become a cornerstone in treating diseases such as cancer and autoimmune disorders. Therapeutic antibodies, in particular, have transformed modern medicine, providing a targeted approach that destroys disease-causing cells while sparing healthy tissues, thereby reducing the side effects commonly associated with chemotherapy. Beyond oncology, these antibodies also hold promise in addressing chronic infections where conventional therapeutics may fall short. However, antibodies identified through in vivo or in vitro methods often require extensive engineering to enhance their therapeutic potential. This optimization process, aimed at improving affinity, specificity, and reducing immunogenicity, is both challenging and costly, often involving trade-offs between critical properties. Traditional methods of antibody development, such as hybridoma technology and display techniques, are resource-intensive and time-consuming. In contrast, computational approaches offer a faster, more efficient alternative, enabling the precise design and analysis of therapeutic antibodies. These methods include sequence and structural bioinformatics approaches, next-generation sequencing-based data mining, machine learning algorithms, systems biology, immuno-informatics, and integrative approaches. These approaches are advancing the field by providing new insights and enhancing the accuracy of antibody design and analysis. In conclusion, computational approaches are essential in the development of therapeutic antibodies, significantly improving the precision and speed of discovery, optimization, and validation. Integrating these methods with experimental approaches accelerates therapeutic antibody development, paving the way for innovative strategies and treatments for various diseases ranging from cancers to autoimmune and infectious diseases.

SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation

Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted binding properties and specific functions. Here, the SYNBIP database, a comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures of 899 SBP-target complexes to illustrate the binding epitopes of SBPs, (ii) using the structures of SBPs in the monomer or complex forms with target proteins, their sequence space has been expanded five times to 12 025 by integrating a structure-based protein generation framework and a protein property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from the RCSB Protein Data Bank, and an additional 16 401 555 ones from the AlphaFold Protein Structure Database, and (iv) the database is regularly updated, incorporating 153 new SBPs. Furthermore, the structural models of all SBPs have been enhanced through the application of the AlphaFold2, with their clinical statuses concurrently refreshed. Additionally, the design methods employed for each SBP are now prominently featured in the database. In sum, SYNBIP 2.0 is designed to provide researchers with essential SBP data, facilitating their innovation in research, diagnosis and therapy. SYNBIP 2.0 is now freely accessible at https://idrblab.org/synbip/.

PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature

Nanobodies are essential proteins of the adaptive immune systems of camelid and shark species, complementing conventional antibodies. Properties such as their relatively small size, solubility and high thermostability make VHH (variable heavy domain of the heavy chain) and VNAR (variable new antigen receptor) modalities a promising therapeutic format and a valuable resource for a wide range of biological applications. The volume of academic literature and patents related to nanobodies has risen significantly over the past decade. Here, we present PLAbDab-nano, a nanobody complement to the Patent and Literature Antibody Database (PLAbDab). PLAbDab-nano is a self-updating, searchable repository containing ∼5000 annotated VHH and VNAR sequences. We describe the methods used to curate the entries in PLAbDab-nano, and highlight how PLAbDab-nano could be used to design diverse libraries, as well as find sequences similar to known patented or therapeutic entries. PLAbDab-nano is freely available as a searchable web server (https://opig.stats.ox.ac.uk/webapps/plabdab-nano/).

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.

NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers (BERT) architecture

Antibody-mediated immunity is crucial in the vertebrate immune system. Nanobodies, also known as VHH or single-domain antibodies (sdAbs), are emerging as promising alternatives to full-length antibodies due to their compact size, precise target selectivity, and stability. However, the limited availability of nanobodies (Nbs) for numerous antigens (Ags) presents a significant obstacle to their widespread application. Understanding the interactions between Nbs and Ags is essential for enhancing their binding affinities and specificities. Experimental identification of these interactions is often costly and time-intensive. To address this issue, we introduce NABP-BERT, a deep-learning model based on the BERT architecture, designed to predict NANOBODY®-Ag binding solely from sequence information. Furthermore, we have developed a general pretrained model with transfer capabilities suitable for protein-related tasks, including protein-protein interaction tasks. NABP-BERT focuses on the surrounding amino acid contexts and outperforms existing methods, achieving an AUROC of 0.986 and an AUPR of 0.985.

Advancements in nanobody generation: Integrating conventional, in silico, and machine learning approaches

Nanobodies, derived from camelids and sharks, offer compact, single-variable heavy-chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next-generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation.

Mono- and Bi-specific Nanobodies Targeting the CUB Domains of PCPE-1 Reduce the Proteolytic Processing of Fibrillar Procollagens

The excessive deposition of fibrillar collagens is a hallmark of fibrosis. Collagen fibril formation requires proteolytic maturations by Procollagen N- and C-proteinases (PNPs and PCPs) to remove the N- and C-propeptides which maintain procollagens in the soluble form. Procollagen C-Proteinase Enhancer-1 (PCPE-1, a glycoprotein composed of two CUB domains and one NTR domain) is a regulatory protein that activates the C-terminal processing of procollagens by the main PCPs. It is often up-regulated in fibrotic diseases and represents a promising target for the development of novel anti-fibrotic strategies. Here, our objective was to develop the first antagonists of PCPE-1, based on the nanobody scaffold. Using both an in vivo selection through the immunization of a llama and an in vitro selection with a synthetic library, we generated 18 nanobodies directed against the CUB domains of PCPE1, which carry its enhancing activity. Among them, I5 from the immune library and H4 from the synthetic library have a high affinity for PCPE-1 and inhibit its interaction with procollagens. The crystal structure of the complex formed by PCPE-1, H4 and I5 showed that they have distinct epitopes and enabled the design of a biparatopic fusion, the diabody diab-D1. Diab-D1 has a sub-nanomolar affinity for PCPE-1 and is a potent antagonist of its activity, preventing the stimulation of procollagen cleavage in vitro. Moreover, Diab-D1 is also effective in reducing the proteolytic maturation of procollagen I in cultures of human dermal fibroblasts and hence holds great promise as a tool to modulate collagen deposition in fibrotic conditions.

Engineering a Low-Immunogenic Mirror-Image VHH against Vascular Endothelial Growth Factor

Immunogenicity is a major caveat of protein therapeutics. In particular, the long-term administration of protein therapeutic agents leads to the generation of antidrug antibodies (ADAs), which reduce drug efficacy while eliciting adverse events. One promising solution to this issue is the use of mirror-image proteins consisting of d-amino acids, which are resistant to proteolytic degradation in immune cells. We have recently reported the chemical synthesis of the enantiomeric form of the variable domain of the antibody heavy chain (d-VHH). However, identifying mirror-image antibodies capable of binding to natural ligands remains challenging. In this study, we developed a novel screening platform to identify a d-VHH specific for vascular endothelial growth factor A (VEGF-A). We performed mirror-image screening of two newly constructed synthetic VHH libraries displayed on T7 phage and identified VHH sequences that effectively bound to the mirror-image VEGF-A target (d-VEGF-A). We subsequently synthesized a d-VHH candidate that preferentially bound the native VEGF-A (l-VEGF-A) with submicromolar affinity. Furthermore, immunization studies in mice demonstrated that this d-VHH elicited no ADAs, unlike its corresponding l-VHH. Our findings highlight the utility of this novel d-VHH screening platform in the development of protein therapeutics exhibiting both reduced immunogenicity and improved efficacy.

NanoLAS: a comprehensive nanobody database with data integration, consolidation and application

Nanobodies, a unique subclass of antibodies first discovered in camelid animals, are composed solely of a single heavy chain's variable region. Their significantly reduced molecular weight, in comparison to conventional antibodies, confers numerous advantages in the treatment of various diseases. As research and applications involving nanobodies expand, the quantity of identified nanobodies is also rapidly growing. However, the existing antibody databases are deficient in type and coverage, failing to satisfy the comprehensive needs of researchers and thus impeding progress in nanobody research. In response to this, we have amalgamated data from multiple sources to successfully assemble a new and comprehensive nanobody database. This database has currently included the latest nanobody data and provides researchers with an excellent search and data display interface, thus facilitating the progression of nanobody research and their application in disease treatment. In summary, the newly constructed Nanobody Library and Archive System may significantly enhance the retrieval efficiency and application potential of nanobodies. We envision that Nanobody Library and Archive System will serve as an accessible, robust and efficient tool for nanobody research and development, propelling advancements in the field of biomedicine. Database URL: https://www.nanolas.cloud.

Advances in research based on antibody-cell conjugation

Antibody-cell conjugation (ACC) technology is a new research direction in medicine and biotechnology in recent years. The concept of ACC was proposed by Hsiao et al. and developed into a viable cell therapy technology, which refers to the cells with specific functions. Such as natural killer cells (NK cells), cytokine induced killer cells (CIK) and other immune cells and monoclonal antibodies through the linker together formed conjugate. ACC directly modifies specific antibodies on the cell surface through a simple and effective chemical coupling method to enable cells to have new functions. ACC has been developed for the treatment of various diseases, including cancers of the blood system and solid tumors. This paper reviews the current ACC construction methods, challenges and future development directions.

DG-Affinity: predicting antigen-antibody affinity with language models from sequences

BACKGROUND

Antibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens.

RESULTS

In this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset.

CONCLUSIONS

Compared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity .

30 years of nanobodies - an ongoing success story of small binders in biological research

A milestone in the field of recombinant binding molecules was achieved 30 years ago with the discovery of single-domain antibodies from which antigen-binding variable domains, better known as nanobodies (Nbs), can be derived. Being only one tenth the size of conventional antibodies, Nbs feature high affinity and specificity, while being highly stable and soluble. In addition, they display accessibility to cryptic sites, low off-target accumulation and deep tissue penetration. Efficient selection methods, such as (semi-)synthetic/naïve or immunized cDNA libraries and display technologies, have facilitated the isolation of Nbs against diverse targets, and their single-gene format enables easy functionalization and high-yield production. This Review highlights recent advances in Nb applications in various areas of biological research, including structural biology, proteomics and high-resolution and in vivo imaging. In addition, we provide insights into intracellular applications of Nbs, such as live-cell imaging, biosensors and targeted protein degradation.

Characterization of Foot-and-Mouth Disease Virus Serotype O-Specific Single Domain Antibody Expressed in the pET Expression System

Foot and mouth disease (FMD) is an extremely contagious disease of cloven-hoofed domesticated and wild animals, resulting in significant economic losses in many parts of the world. FMD virus (FMDV) serotype O is responsible for approximately 70% of global outbreaks. For detection of FMDV antigen or antibody, ELISAs are used worldwide and have several limitations, such as batch-to-batch variation in generating immunobiologicals, high production cost and ethical concerns over animal sacrifice. The use of single domain antibody (sdAb) or variable N-terminal domain of the heavy chain of heavy chain antibody (VHH) found naturally in camels has proven their effectiveness in diagnostics and therapeutics. In the present study, the anti-FMDV serotype O-specific VHH-C1 gene sequence (Accession no. KJ751546) was retrieved from the NCBI database. The gene was synthesized commercially in the pBluescript KS+ cloning vector and expressed in E. coli BL21 (DE3) cells using the pET303/CT-His expression system with a C-terminal 6X-His tag. The expressed sdAb, verified by SDS‒PAGE and western blotting, was purified by Ni-chelate chromatography and used as a coating antibody in double antibody sandwich (DAS) ELISA for FMDV detection and typing. The sdAb exhibited a high binding affinity for FMDV serotype O, without any cross-reactivity toward serotypes A and Asia-1. It exhibited better thermostability up to 85 °C than conventional rabbit polyclonal anti-FMDV sera. The potential of sdAbs thus produced without sacrificing lab animals could be explored for replacing polyclonal sera in DAS-ELISA as well as for developing biosensors or lateral flow devices for FMDV type O detection.

Development of a V5-tag-directed nanobody and its implementation as an intracellular biosensor of GPCR signaling

Protein-protein interactions (PPIs) form the foundation of any cell signaling network. Considering that PPIs are highly dynamic processes, cellular assays are often essential for their study because they closely mimic the biological complexities of cellular environments. However, incongruity may be observed across different PPI assays when investigating a protein partner of interest; these discrepancies can be partially attributed to the fusion of different large functional moieties, such as fluorescent proteins or enzymes, which can yield disparate perturbations to the protein's stability, subcellular localization, and interaction partners depending on the given cellular assay. Owing to their smaller size, epitope tags may exhibit a diminished susceptibility to instigate such perturbations. However, while they have been widely used for detecting or manipulating proteins in vitro, epitope tags lack the in vivo traceability and functionality needed for intracellular biosensors. Herein, we develop NbV5, an intracellular nanobody binding the V5-tag, which is suitable for use in cellular assays commonly used to study PPIs such as BRET, NanoBiT, and Tango. The NbV5:V5 tag system has been applied to interrogate G protein-coupled receptor signaling, specifically by replacing larger functional moieties attached to the protein interactors, such as fluorescent or luminescent proteins (∼30 kDa), by the significantly smaller V5-tag peptide (1.4 kDa), and for microscopy imaging which is successfully detected by NbV5-based biosensors. Therefore, the NbV5:V5 tag system presents itself as a versatile tool for live-cell imaging and a befitting adaptation to existing cellular assays dedicated to probing PPIs.

Bypassing the Need for Cell Permeabilization: Nanobody CDR3 Peptide Improves Binding on Living Bacteria

Membrane interaction constitutes to be an essential parameter in the mode of action of entities such as proteins, as well as cell-penetrating and antimicrobial peptides, resulting in noninvasive or lytic activities depending on the membrane compositions and interactions. Recently, a nanobody able to interact with the top priority, multidrug-resistant bacterial pathogen Acinetobacter baumannii was discovered, although binding took place with fixed cells only. To potentially overcome this limitation, linear peptides corresponding to the complementarity-determining regions (CDR) were synthesized and fluorescently labeled. Microscopy data indicated clear membrane interactions of the CDR3 sequence with living A. baumannii cells, indicating both the importance of the CDR3 as part of the parent nanobody paratope and the improved binding ability and thus avoiding the need for permeabilization of the cells. In addition, cyclization of the peptide with an additionally introduced rigidifying 1,2,3-triazole bridge retains its binding ability while proteolytically protecting the peptide. Overall, this study resulted in the discovery of novel peptides binding a multidrug-resistant pathogen.

Construction of an alpaca immune antibody library for the selection of nanobodies against Drosophila melanogaster proteins

Introduction: Drosophila melanogaster is a model organism for studying developmental biology and human neural disorders. Nanobodies are the variable domains of the heavy chains of camelid heavy-chain antibodies (VHHs) with high affinity to their antigens and have applications in basic research, similar to traditional antibodies. In addition, nanobodies acting as functionalized antibodies or protein binders have become an additional valuable approach in Drosophila. This study aimed to develop a VHH library against Drosophila proteins and confirm its availability by retrieving some Drosophila protein-specific nanobodies from the library. Methods: An alpaca was first immunized with Drosophila embryo lysate and then its lymphocytes were isolated. Total RNA was extracted and cDNA was synthesized. The vhh sequences were amplified by two round PCR, which were then ligated to a phage display vector pADL-10b. The ligation products were transduced into SS320 competent cells to generate a VHH library. From this library, nanobodies against CG7544, Myc, and CyclinE was enriched and screened by phage display technology and ELISA. DNA sequences of identified nanobodies were cloned into pADL-10b-Flag-His for expression and purification in Escherichia coli SS320. Binding ability of purified nanobodies with corresponding antigens were determined by ELISA and surface plasmon resonance in vitro. Results: In this study, an immune VHH library against Drosophila embryo proteins was generated with a capacity of 3 × 10⁷. From this library, eight nanobodies against three Drosophila proteins, Myc, CyclinE, and CG7544, were identified and the DNA sequences of these nanobodies were obtained. These nanobodies were successfully expressed and purified from Escherichia coli SS320, and were demonstrated to bind corresponding antigens with high affinity in vitro. Moreover, the equilibrium constant between the highest enriched nanobodies and corresponding antigens were calculated. Conclusion: In summary, we report the availability of an immune VHH library and a highly efficient panning strategy for nanobodies against proteins in Drosophila.

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Using NanoB2 to gain insights into ligand pharmacology

In this issue of Cell Reports Methods, van den Bor et al.1 report a novel assay called NanoB2, which harnesses the use of nanobodies to broaden the applications of NanoBRET approaches for investigating ligand pharmacology. They demonstrate the generalizability of NanoB2 on a range of membrane proteins.

NanoB2 to monitor interactions of ligands with membrane proteins by combining nanobodies and NanoBRET

The therapeutic potential of ligands targeting disease-associated membrane proteins is predicted by ligand-receptor binding constants, which can be determined using NanoLuciferase (NanoLuc)-based bioluminescence resonance energy transfer (NanoBRET) methods. However, the broad applicability of these methods is hampered by the restricted availability of fluorescent probes. We describe the use of antibody fragments, like nanobodies, as universal building blocks for fluorescent probes for use in NanoBRET. Our nanobody-NanoBRET (NanoB2) workflow starts with the generation of NanoLuc-tagged receptors and fluorescent nanobodies, enabling homogeneous, real-time monitoring of nanobody-receptor binding. Moreover, NanoB2 facilitates the assessment of receptor binding of unlabeled ligands in competition binding experiments. The broad significance is illustrated by the successful application of NanoB2 to different drug targets (e.g., multiple G protein-coupled receptors [GPCRs] and a receptor tyrosine kinase [RTK]) at distinct therapeutically relevant binding sites (i.e., extracellular and intracellular).

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Development of Synthetic mRNAs Encoding Split Cytotoxic Proteins for Selective Cell Elimination Based on Specific Protein Detection

For the selective elimination of deleterious cells (e.g., cancer cells and virus-infected cells), the use of a cytotoxic gene is a promising approach. DNA-based systems have achieved selective cell elimination but risk insertional mutagenesis. Here, we developed a synthetic mRNA-based system to selectively eliminate cells expressing a specific target protein. The synthetic mRNAs used in the system are designed to express an engineered protein pair that are based on a cytotoxic protein, Barnase. Each engineered protein is composed of an N- or C-terminal fragment of Barnase, a target protein binding domain, and an intein that aids in reconstituting full-length Barnase from the two fragments. When the mRNAs are transfected to cells expressing the target protein, both N- and C-terminal Barnase fragments bind to the target protein, causing the intein to excise itself and reconstitute cytotoxic full-length Barnase. In contrast, when the target protein is not present, the reconstitution of full-length Barnase is not induced. Four candidate constructs containing split Barnase were evaluated for the ability to selectively eliminate target protein-expressing cells. One of the candidate sets demonstrated highly selective cell death. This system will be a useful therapeutic tool to selectively eliminate deleterious cells.

Research progress on unique paratope structure, antigen binding modes, and systematic mutagenesis strategies of single-domain antibodies

Single-domain antibodies (sdAbs) showed the incredible advantages of small molecular weight, excellent affinity, specificity, and stability compared with traditional IgG antibodies, so their potential in binding hidden antigen epitopes and hazard detection in food, agricultural and veterinary fields were gradually explored. Moreover, its low immunogenicity, easy-to-carry target drugs, and penetration of the blood-brain barrier have made sdAbs remarkable achievements in medical treatment, toxin neutralization, and medical imaging. With the continuous development and maturity of modern molecular biology, protein analysis software and database with different algorithms, and next-generation sequencing technology, the unique paratope structure and different antigen binding modes of sdAbs compared with traditional IgG antibodies have aroused the broad interests of researchers with the increased related studies. However, the corresponding related summaries are lacking and needed. Different antigens, especially hapten antigens, show distinct binding modes with sdAbs. So, in this paper, the unique paratope structure of sdAbs, different antigen binding cases, and the current maturation strategy of sdAbs were classified and summarized. We hope this review lays a theoretical foundation to elucidate the antigen-binding mechanism of sdAbs and broaden the further application of sdAbs.

Nanobody-based RFP-dependent Cre recombinase for selective anterograde tracing in RFP-expressing transgenic animals

Transgenic animals expressing fluorescent proteins are widely used to label specific cells and proteins. By using a split Cre recombinase fused with mCherry-binding nanobodies or designed ankyrin repeat proteins, we created Cre recombinase dependent on red fluorescent protein (RFP) (Cre-DOR). Functional binding units for monomeric RFPs are different from those for polymeric RFPs. We confirmed selective target RFP-dependent gene expression in the mouse cerebral cortex using stereotaxic injection of adeno-associated virus vectors. In estrogen receptor-beta (Esr2)-mRFP1 mice and gastrin-releasing peptide receptor (Grpr)-mRFP1 rats, we confirmed that Cre-DOR can be used for selective tracing of the neural projection from RFP-expressing specific neurons. Cellular localization of RFPs affects recombination efficiency of Cre-DOR, and light and chemical-induced nuclear translocation of an RFP-fused protein can modulate Cre-DOR efficiency. Our results provide a method for manipulating gene expression in specific cells expressing RFPs and expand the repertory of nanobody-based genetic tools.

Targeted protein degradation in mammalian cells: A Promising Avenue toward Future

In the eukaryotic cellular milieu, proteins are continuously synthesized and degraded effectively via endogenous protein degradation machineries such as the ubiquitin–proteasome and lysosome pathways. By reengineering and repurposing these natural protein regulatory mechanisms, the targeted protein degradation (TPD) strategies are presenting biologists with powerful tools to manipulate the abundance of proteins of interest directly, precisely, and reversibly at the post-translational level. In recent years, TPD is gaining massive attention and is recognized as a paradigm shift both in basic research, application-oriented synthetic biology, and pioneering clinical work. In this review, we summarize the updated information, especially the engineering efforts and developmental route, of current state-of-the-art TPD technology such as Trim-Away, LYTACs, and AUTACs. Besides, the general design principle, benefits, problems, and opportunities to be addressed were further analyzed, with the aim of providing guidelines for exploration, discovery, and further application of novel TPD tools in the future.

Tyrosinase-Mediated Synthesis of Nanobody-Cell Conjugates

A convenient enzymatic strategy is reported for the modification of cell surfaces. Using a tyrosinase enzyme isolated from Agaricus bisporus, unique tyrosine residues introduced at the C-termini of nanobodies can be site-selectively oxidized to reactive o-quinones. These reactive intermediates undergo rapid modification with nucleophilic thiol, amine, and imidazole residues present on cell surfaces, producing novel nanobody-cell conjugates that display targeted antigen binding. We extend this approach toward the synthesis of nanobody-NK cell conjugates for targeted immunotherapy applications. The resulting NK cell conjugates exhibit targeted cell binding and elicit targeted cell death.

Single-domain near-infrared protein provides a scaffold for antigen-dependent fluorescent nanobodies

Small near-infrared (NIR) fluorescent proteins (FPs) are much needed as protein tags for imaging applications. We developed a 17 kDa NIR FP, called miRFP670nano3, which brightly fluoresces in mammalian cells and enables deep-brain imaging. By exploring miRFP670nano3 as an internal tag, we engineered 32 kDa NIR fluorescent nanobodies, termed NIR-Fbs, whose stability and fluorescence strongly depend on the presence of specific intracellular antigens. NIR-Fbs allowed background-free visualization of endogenous proteins, detection of viral antigens, labeling of cells expressing target molecules and identification of double-positive cell populations with bispecific NIR-Fbs against two antigens. Applying NIR-Fbs as destabilizing fusion partners, we developed molecular tools for directed degradation of targeted proteins, controllable protein expression and modulation of enzymatic activities. Altogether, NIR-Fbs enable the detection and manipulation of a variety of cellular processes based on the intracellular protein profile.

Versatile Design of Intracellular Protein-Responsive Translational Regulation System for Synthetic mRNA

Synthetic mRNA (mRNA) enables transgene expression without the necessity of nuclear import and the risk of insertional mutagenesis, which makes it an attractive tool for medical applications such as vaccination and protein replacement therapy. For further improvement of mRNA therapeutics, cell-selective translation is desirable, because transgene expression in nontarget cells sometimes causes adverse effects. In this study, we developed an intracellular protein-responsive translational regulation system based on Caliciviral VPg-based translational activator (CaVT) combined with inteins and target protein-binding nanobodies. This system enabled both translational activation and repression in a target protein-dependent manner. Importantly, the target protein can be altered by simply exchanging the nanobodies. The versatile design for target protein-responsive translational regulation holds promise for producing mRNA therapeutics with high safety.

Nanobodies as molecular imaging probes

Camelidae derived single-domain antibodies (sdAbs), commonly known as nanobodies (Nbs), are the smallest antibody fragments with full antigen-binding capacity. Owing to their desirable properties such as small size, high specificity, strong affinity, excellent stability, and modularity, nanobodies are on their way to overtake conventional antibodies in terms of popularity. To date, a broad range of nanobodies have been generated against different molecular targets with applications spanning basic research, diagnostics, and therapeutics. In the field of molecular imaging, nanobody-based probes have emerged as a powerful tool. Radioactive or fluorescently labeled nanobodies are now used to detect and track many targets in different biological systems using imaging techniques. In this review, we provide an overview of the use of nanobodies as molecular probes. Additionally, we discuss current techniques for the generation, conjugation, and intracellular delivery of nanobodies.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Designing of nanobodies against Dengue virus Capsid: a computational affinity maturation approach

Dengue virus, an arbovirus, is one of the most prevalent diseases in the tropical environment and leads to huge number of casualties every year. No therapeutics are available till date against the viral disease and the only medications provide symptomatic relief. In this study, we have focused on utilizing conventional nanobodies and repurposing them for Dengue. Computationally affinity matured, best binding nanobodies tagged with constant antibody regions, could be proposed as therapeutics. These could also be applied for drug delivery purposes due to their high specificity against the viral Capsid. Another application of these nanobodies has been thought to utilize them for diagnostic purposes, to use the nanobodies for viral detection from patient samples at the earliest stage using ELISA. This study may open a new avenue for immunologic study in foreseeable future with the usage of the same molecules for multiple purposes. HighlightsNatural nanobodies against viruses were modified for use against Dengue virus Capsid conserved regions.Computational affinity maturation was performed making use of change in binding affinities upon mutating various residues in the complementary determining regions.Docking studies performed to inspect the docking groove, interface analysis and energy calculations.MM/GBSA calculations done to calculate binding free energy of the complex to determine stability of the complex.Communicated by Ramaswamy H. Sarma.

INDI-integrated nanobody database for immunoinformatics

Nanobodies, a subclass of antibodies found in camelids, are versatile molecular binding scaffolds composed of a single polypeptide chain. The small size of nanobodies bestows multiple therapeutic advantages (stability, tumor penetration) with the first therapeutic approval in 2018 cementing the clinical viability of this format. Structured data and sequence information of nanobodies will enable the accelerated clinical development of nanobody-based therapeutics. Though the nanobody sequence and structure data are deposited in the public domain at an accelerating pace, the heterogeneity of sources and lack of standardization hampers reliable harvesting of nanobody information. We address this issue by creating the Integrated Database of Nanobodies for Immunoinformatics (INDI, http://naturalantibody.com/nanobodies). INDI collates nanobodies from all the major public outlets of biological sequences: patents, GenBank, next-generation sequencing repositories, structures and scientific publications. We equip INDI with powerful nanobody-specific sequence and text search facilitating access to >11 million nanobody sequences. INDI should facilitate development of novel nanobody-specific computational protocols helping to deliver on the therapeutic promise of this drug format.

SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker

In 2013, we released the Structural Antibody Database (SAbDab), a publicly available repository of experimentally determined antibody structures. In the interim, the rapid increase in the number of antibody structure depositions to the Protein Data Bank, driven primarily by increased interest in antibodies as biotherapeutics, has led us to implement several improvements to the original database infrastructure. These include the development of SAbDab-nano, a sub-database that tracks nanobodies (heavy chain-only antibodies) which have seen a particular growth in attention from both the academic and pharmaceutical research communities over the past few years. Both SAbDab and SAbDab-nano are updated weekly, comprehensively annotated with the latest features described here, and are freely accessible at opig.stats.ox.ac.uk/webapps/newsabdab/.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

Mandipropamid as a chemical inducer of proximity for in vivo applications

Direct control of protein interactions by chemically induced protein proximity holds great potential for both cell and synthetic biology as well as therapeutic applications. Low toxicity, orthogonality and excellent cell permeability are important criteria for chemical inducers of proximity (CIPs), in particular for in vivo applications. Here, we present the use of the agrochemical mandipropamid (Mandi) as a highly efficient CIP in cell culture systems and living organisms. Mandi specifically induces complex formation between a sixfold mutant of the plant hormone receptor pyrabactin resistance 1 (PYR1) and abscisic acid insensitive (ABI). It is orthogonal to other plant hormone-based CIPs and rapamycin-based CIP systems. We demonstrate the applicability of the Mandi system for rapid and efficient protein translocation in mammalian cells and zebrafish embryos, protein network shuttling and manipulation of endogenous proteins.

Activation of a G protein-coupled receptor through indirect antibody-mediated tethering of ligands

Antibodies raised against many cell surface proteins, including G protein-coupled receptors, remain important tools for their functional characterization. By linking antibodies to ligands for cell surface proteins, such adducts can be targeted to the surface of a cell type of choice. Site-specific functionalization of full-size antibodies with synthetic moieties remains challenging. Here we present new approaches in which single domain antibodies (known as VHHs or nanobodies) that target either cell surface proteins or conventional antibodies are used to indirectly deliver ligands for GPCRs to their sites of action. The combination of high yield production of nanobodies, facile site-specific functionalization, and compatibility with commercially available mouse and rabbit antibodies should enable wide application of this approach.

The use of antibodies to target membrane receptors enables specific and potent activation. But antibodies with appropriate specificity can be lacking. We present methods for targeting receptors without target-specific antibodies.

Show your true color: Mammalian cell surface staining for tracking cellular identity in multiplexing and beyond

Fluorescence microscopy revolutionized cell biology and changed requirements for dyes towards higher brightness, novel capacities, and specific targets. With the need for multiplexing assays in high-throughput methodologies, surface staining gained particular interest because it allows rapid application of exogenous stains to track cellular identity in mixed populations. Indeed, the last decade has enriched the toolbox of general lipid stains, fluorescent lipid analogues, sugar-binding lectins, and protein-specific antibodies enabling the first rationally designed plasma membrane-specific dyes. Still, multiple challenges exist, and the unique properties of each dye must be considered when selecting a staining approach for a specific application. Recent advances are also promising that future dyes will provide ultimate brightness and photostability in diverse colors and reduced sizes for high-resolution imaging.

Writing and erasing O-GlcNAc from target proteins in cells

O-linked N-acetylglucosamine (O-GlcNAc) is a widespread reversible modification on nucleocytoplasmic proteins that plays an important role in many biochemical processes and is highly relevant to numerous human diseases. The O-GlcNAc modification has diverse functional impacts on individual proteins and glycosites, and methods for editing this modification on substrates are essential to decipher these functions. Herein, we review recent progress in developing methods for O-GlcNAc regulation, with a focus on methods for editing O-GlcNAc with protein- and site-selectivity in cells. The applications, advantages, and limitations of currently available strategies for writing and erasing O-GlcNAc and future directions are also discussed. These emerging approaches to manipulate O-GlcNAc on a target protein in cells will greatly accelerate the development of functional studies and enable therapeutic interventions in the O-GlcNAc field.

SYNBIP: synthetic binding proteins for research, diagnosis and therapy

The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.

Strategies and Tactics for the Development of Selective Glycan-Binding Proteins

The influences of glycans impact all biological processes, disease states, and pathogenic interactions. Glycan-binding proteins (GBPs), such as lectins, are decisive tools for interrogating glycan structure and function because of their ease of use and ability to selectively bind defined carbohydrate epitopes and glycosidic linkages. GBP reagents are prominent tools for basic research, clinical diagnostics, therapeutics, and biotechnological applications. However, the study of glycans is hindered by the lack of specific and selective protein reagents to cover the massive diversity of carbohydrate structures that exist in nature. In addition, existing GBP reagents often suffer from low affinity or broad specificity, complicating data interpretation. There have been numerous efforts to expand the GBP toolkit beyond those identified from natural sources through protein engineering, to improve the properties of existing GBPs or to engineer novel specificities and potential applications. This review details the current scope of proteins that bind carbohydrates and the engineering methods that have been applied to enhance the affinity, selectivity, and specificity of binders.

A guide to the optogenetic regulation of endogenous molecules

Genetically encoded tools for the regulation of endogenous molecules (RNA, DNA elements and protein) are needed to study and control biological processes with minimal interference caused by protein overexpression and overactivation of signaling pathways. Here we focus on light-controlled optogenetic tools (OTs) that allow spatiotemporally precise regulation of gene expression and protein function. To control endogenous molecules, OTs combine light-sensing modules from natural photoreceptors with specific protein or nucleic acid binders. We discuss OT designs and group OTs according to the principles of their regulation. We outline characteristics of OT performance, discuss considerations for their use in vivo and review available OTs and their applications in cells and in vivo. Finally, we provide a brief outlook on the development of OTs.

AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins

Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of k_app, where k_app is in hour^-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.

Computational design and modeling of nanobodies toward SARS-CoV-2 receptor binding domain

The ongoing pandemic of coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has become a global health concern and pose a serious threat to humanity. There is an urgent need for developing therapeutic drugs and (or) biologics to prevent the spread of the virus. The life cycle of SARS‐CoV‐2 shows that the virus enters host cells by first binding to angiotensin‐converting enzyme 2 (ACE2) through its spike protein receptor‐binding domain (RBD). Therefore, blocking the binding between of ACE2 and SARS‐CoV‐2 RBD can inhibit the virus infection in the host cells. In this study, by grafting the complementarity‐determining regions (CDRs) of developed SARS‐CoV, MERS‐CoVs specific neutralizing antibodies (nAbs) include monoclonal antibodies (mAbs) as well as SARS‐CoV‐2 mAbs onto a known stable nanobody (Nb) scaffold, and a total of 16 Nbs sequences were designed. Five Nbs, namely CS01, CS02, CS03, CS10, and CS16, were selected based on the free energy landscape of protein docking verified by the recently reported Nb‐RBD cocrystal structures. CS01, CS02, and CS03 occupied the ACE2 binding site of RBD, while CS10 and CS16 were proposed to inhibit the interaction between RBD and ACE2 through an allosteric mechanism. Based on the structures of the five Nbs in complex with RBD, seven brand‐new Nbs with enhanced binding affinities (CS02_RD01, CS03_RD01, CS03_RD02, CS03_RD03, CS03_RD04, CS16_RD01, and CS16_RD02) were generated by redesign of residues on the interface of the five Nbs contact with SARS‐CoV‐2 RBD. In addition, the identified “hot spots” on the interface of each complex provide useful information to understand the binding mechanism of designed Nbs to SARS‐CoV‐2 RBD. In sum, the predicted stabilities and high binding affinities of the 11 (re)designed Nbs indicating the potential of the developed computational framework in this work to design effective agents to block the infection of SARS‐CoV‐2.

Designing and constructing a phage display synthesized single domain antibodies library based on camel VHHs frame for screening and identifying humanized TNF-α-specific nanobody

Tumor necrosis factor (TNF-α) is an important clinically tested cytokine that could induce autoimmune diseases and inflammation. Therefore, the anti-TNF-α therapy strategy was developed and used therapeutically in various diseases, especially in the cytokine storm associated chimeric antigen receptor (CAR) T-cell therapy and antiviral therapy. Compare with other anti-TNF-α inhibitors, anti-TNF-α Nb (nanobody) has many unique advantages. Herein, we reported a novel humanized scaffold for library construction, which could be soluble and expressed in Escherichia coli (E.coli), and the efficiency capacity could reach as high as 2.01 × 10⁹. Meanwhile, an anti-TNF-α Nb was selected for further study after 4 rounds of screening, NT-3, as the optimal Nb could effectively inhibit TNF-mediated cytotoxicity. The IC50 of NT-3 was determined as 0.804 μM, and its apoptosis inhibition rate was 62.47 % in L929 cells. Furthermore, the molecular docking results showed that complementarity-determining regions (CDRs) of NT-3 could connect to TNF for blocking function through strong hydrogen bonds and salt bridges. In general, our study not only provided a good Nb screening platform in vitro without animal immunization, but also generated a series of novel humanized anti-TNF-α Nb candidates with potential applications.

Synchronous and Asynchronous Response in Dynamically Perturbed Proteins

We present a dynamic perturbation-response model of proteins based on the Gaussian Network Model, where a residue is perturbed periodically, and the dynamic response of other residues is determined. The model shows that periodic perturbation causes a synchronous response in phase with the perturbation and an asynchronous response that is out of phase. The asynchronous component results from the viscous effects of the solvent and other dispersive factors in the system. The model is based on the solution of the Langevin equation in the presence of solvent, noise, and perturbation. We introduce several novel ideas: The concept of storage and loss compliance of the protein and their dependence on structure and frequency; the amount of work lost and the residues that contribute significantly to the lost work; new dynamic correlations that result from perturbation; causality, that is, the response of j when i is perturbed is not equal to the response of i when j is perturbed. As examples, we study two systems, namely, bovine rhodopsin and the class of nanobodies. The general results obtained are (i) synchronous and asynchronous correlations depend strongly on the frequency of perturbation, their magnitude decreases with increasing frequency, (ii) time-delayed mean-squared fluctuations of residues have only synchronous components. Asynchronicity is present only in cross correlations, that is, correlations between different residues, (iii) perturbation of loop residues leads to a large dissipation of work, (iv) correlations satisfy the hypothesis of pre-existing pathways according to which information transfer by perturbation rides on already existing equilibrium correlations in the system, (v) dynamic perturbation can introduce a selective response in the system, where the perturbation of each residue excites different sets of responding residues, and (vi) it is possible to identify nondissipative residues whose perturbation does not lead to dissipation in the protein. Despite its simplicity, the model explains several features of allosteric manipulation.

Development of potent and effective synthetic SARS-CoV-2 neutralizing nanobodies

The respiratory virus responsible for coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected nearly every aspect of life worldwide, claiming the lives of over 3.9 million people globally, at the time of this publication. Neutralizing humanized nanobody (VHH)-based antibodies (VHH-huFc) represent a promising therapeutic intervention strategy to address the current SARS-CoV-2 pandemic and provide a powerful toolkit to address future virus outbreaks. Using a synthetic, high-diversity VHH bacteriophage library, several potent neutralizing VHH-huFc antibodies were identified and evaluated for their capacity to tightly bind to the SARS-CoV-2 receptor-binding domain, to prevent binding of SARS-CoV-2 spike (S) to the cellular receptor angiotensin-converting enzyme 2, and to neutralize viral infection. Preliminary preclinical evaluation of multiple VHH-huFc antibody candidates demonstrate that they are prophylactically and therapeutically effective in vivo against wildtype SARS-CoV-2. The identified and characterized VHH-huFc antibodies described herein represent viable candidates for further preclinical evaluation and another tool to add to our therapeutic arsenal to address the COVID-19 pandemic.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.