NbX: Machine Learning-Guided Re-Ranking of Nanobody-Antigen Binding Poses

Nanobodies: From Discovery to AI-Driven Design

Nanobodies, derived from naturally occurring heavy-chain antibodies in camelids (VHHs) and sharks (VNARs), are unique single-domain antibodies that have garnered significant attention in therapeutic, diagnostic, and biotechnological applications due to their small size, stability, and high specificity. This review first traces the historical discovery of nanobodies, highlighting key milestones in their isolation, characterization, and therapeutic development. We then explore their structure-function relationship, emphasizing features like their single-domain architecture and long CDR3 loop that contribute to their binding versatility. Additionally, we examine the growing interest in multiepitope nanobodies, in which binding to different epitopes on the same antigen not only enhances neutralization and specificity but also allows these nanobodies to be used as controllable modules for precise antigen manipulation. This review also discusses the integration of AI in nanobody design and optimization, showcasing how machine learning and deep learning approaches are revolutionizing rational design, humanization, and affinity maturation processes. With continued advancements in structural biology and computational design, nanobodies are poised to play an increasingly vital role in addressing both existing and emerging biomedical challenges.

NABP-LSTM-Att: Nanobody-Antigen binding prediction using bidirectional LSTM and soft attention mechanism

In vertebrates, antibody-mediated immunity is a vital component of the immune system, and antibodies have become a rapidly expanding class of therapeutic agents. Nanobodies, a distinct type of antibody, have recently emerged as a stable and cost-effective alternative to traditional antibodies. Their small size, high target specificity, notable solubility, and stability make nanobodies promising candidates for developing high-quality drugs. However, the lack of available nanobodies for most antigens remains a key challenge. Advancing the development of nanobodies requires a better understanding of their interactions with antigens to enhance binding affinity and specificity. Experimental methods for identifying these interactions are essential but often costly and time-consuming, posing challenges for developing nanobody therapies. Although several computational approaches have been designed to screen potential nanobodies, their dependency on 3D structures limits their broad application. This research introduces NABP-LSTM-Att, a deep learning model designed to predict nanobody-antigen binding solely from sequence information. NABP-LSTM-Att leverages bidirectional long short-term memory (biLSTM) to capture both long- and short-term dependencies within nanobody and antigen sequences, combined with a soft attention mechanism to focus on key features. When evaluated on nanobody-antigen sequence pairs from the SAbDab-nano database, NABP-LSTM-Att achieved an AUROC of 0.926 and an AUPR of 0.952. Considering the significance of nanobody-based treatments and their prospective uses in immunotherapy and diagnostics, we believe that the proposed model will serve as an effective tool for predicting nanobody-antigen binding.

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.

Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies

Nanobody (Nb)-induced disassembly of surface array protein (Sap) S-layers, a two-dimensional paracrystalline protein lattice from Bacillus anthracis, has been presented as a therapeutic intervention for lethal anthrax infections. However, only a subset of existing Nbs with affinity to Sap exhibit depolymerization activity, suggesting that affinity and epitope recognition are not enough to explain inhibitory activity. In this study, we performed all-atom molecular dynamics simulations of each Nb bound to the Sap binding site and trained a collection of machine learning classifiers to predict whether each Nb induces depolymerization. We used feature importance analysis to filter out unnecessary features and engineered remaining features to regularize the feature landscape and encourage learning of the depolymerization mechanism. We find that, while not enforced in training, a gradient-boosting decision tree is able to reproduce the experimental activities of inhibitory Nbs while maintaining high classification accuracy, whereas neural networks were only able to discriminate between classes. Further feature analysis revealed that inhibitory Nbs restrain Sap motions toward an inhibitory conformational state described by domain-domain clamping and induced twisting of domains normal to the lattice plane. We believe these motions drive Sap lattice depolymerization and can be used as design targets for improved Sap-inhibitory Nbs. Finally, we expect our method of study to apply to S-layers that serve as virulence factors in other pathogens, paving the way forward for Nb therapeutics that target depolymerization mechanisms.

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.

Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions

The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These building blocks play an important role in the overall quality of the molecule and can influence many important aspects from potency and specificity to stability and half-life. Single-domain antibodies, particularly camelid-derived variable heavy domain of heavy chain (VHH) antibodies, are becoming an increasingly popular choice for bispecific construction due to their single-domain modularity, favorable biophysical properties, and potential to work in multiple antibody formats. Here, we review the use of VHH domains as building blocks in the construction of multispecific antibodies and the challenges in creating optimized molecules. In addition to exploring traditional approaches to VHH development, we review the integration of machine learning techniques at various stages of the process. Specifically, the utilization of machine learning for structural prediction, lead identification, lead optimization, and humanization of VHH antibodies.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Targeting Ras-binding domain of ELMO1 by computational nanobody design

The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.