ModiBodies: A computational method for modifying nanobodies in nanobody-antigen complexes to improve binding affinity and specificity

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.

Pharmacoinformatics-based screening and construction of a neutralizing anti-SARS-CoV-2 camelidae nanobody drug conjugate

Nanobodies or variable antigen-binding domains (VHH) derived from heavy chain-only antibodies (HcAb) occurring in the Camelidae family offer certain superior physicochemical characteristics like enhanced stability, solubility, and low immunogenicity compared to conventional antibodies. Their efficient antigen-binding capabilities make them a preferred choice for next-generation small biologics. In the present work, we design an anti-SARS-CoV-2 bi-paratopic nanobody drug conjugate by screening a nanobody database. SAbDab-nano database was screened based on the physicochemical properties and SARS-CoV-2 binding affinity of the documented nanobodies. Molecular docking, computational modeling, in silico site-directed mutagenesis, and MD simulations were performed to construct an effective nanobody bi-paratope. The construct's physicochemical properties were assessed, and its structural integrity was validated through model energy refinement and quality assessment. The triple-mutant (N78Q K116N T123F) nanobody, based on the bioinformatics analysis, exhibited enhanced binding efficiency against its targets: SARS CoV-2 WT RB (- 353.3), NRP1 (- 376.5) and Omicron RBD (- 380.8), compared to the WT nanobody (SARS CoV-2 WT RBD = - 337.5, NRP1 = - 361.5, Omicron RBD = - 359.5). In silico evaluation also predicted that the construct would demonstrate efficient solubility, high thermostability (Tm 67.4 °C), low molecular weight of 29.36 KDa, and non-toxic, non-allergenic properties. Anti-SARS-CoV-2 neutralizing nanobody-based therapeutics, as demonstrated through this computational work, represents a promising alternative to traditional COVID-19 prophylaxis.

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.

Nanobody-peptide-conjugate (NPC) for passive immunotherapy against SARS-CoV-2 variants of concern (VoC): a prospective pan-coronavirus therapeutics

The COVID-19 crisis, incited by the zoonotic SARS-CoV-2 virus, has quickly escalated into a catastrophic public health issue and a grave threat to humankind owing to the advent of mutant viruses. Multiple pharmaceutical therapies or biologics envision stopping the virus from spreading further; however, WHO has voiced concerns about the variants of concern (VoCs) inability to respond. Nanobodies are a new class of antibody mimics with binding affinity and specificity similar to classical mAbs, as well as the privileges of a small molecular weight, ease of entry into solid tissues, and binding cryptic epitopes of the antigen. Herein, we investigated multiple putative anti-SARS-CoV-2 nanobodies targeting the Receptor binding domain of the WHO-listed SARS-CoV-2 variants of concern using a comprehensive computational immunoinformatics methodology. With affinity maturation via alanine scanning mutagenesis, we remodeled an ultrapotent nanobody with substantial breadth and potency, exhibiting pico-molar binding affinities against all the VoCs. An antiviral peptide with specificity for ACE-2 receptors was affixed to make it multispecific and discourage viral entry. Collectively, we constructed a broad-spectrum therapeutic biparatopic nanobody-peptide conjugate (NPC) extending coverage to SARS-CoV-2 VoCs RBDs. We PEGylated the biparatopic construct with 20kD maleimide-terminated PEG (MAL-(PEG)n-OMe) to improve its clinical efficacy limiting rapid renal clearance, and performed in silico cloning to facilitate future experimental studies. Our findings suggest that combining biparatopic nanobody conjugate with standard treatment may be a promising bivariate tool for combating viral entry during COVID-19 illness.

NbThermo: a new thermostability database for nanobodies

We present NbThermo-a first-in-class database that collects melting temperatures (Tm), amino acid sequences and several other categories of useful data for hundreds of nanobodies (Nbs), compiled from an extensive literature search. This so-far unique database currently contains up-to-date, manually curated data for 564 Nbs. It represents a contribution to efforts aimed at developing new algorithms for reliable Tm prediction to assist Nb engineering for a wide range of applications of these unique biomolecules. Nbs from the two most common source organisms-llama and camel-show similar distributions of melting temperatures. A first exploratory research that takes advantage of this large data collection evidences that understanding the structural bases of Nb thermostability is a complex task, since there are no apparent differences in sequence patterns between the frameworks of Nbs with lower and higher melting temperatures, indicating that the highly variable loops play a relevant role in defining Nb thermostability. Database URL https://valdes-tresanco-ms.github.io/NbThermo.

Dengue virus infection - a review of pathogenesis, vaccines, diagnosis and therapy

The transmission of dengue virus (DENV) from an infected Aedes mosquito to a human, causes illness ranging from mild dengue fever to fatal dengue shock syndrome. The similar conserved structure and sequence among distinct DENV serotypes or different flaviviruses has resulted in the occurrence of cross reaction followed by antibody-dependent enhancement (ADE). Thus far, the vaccine which can provide effective protection against infection by different DENV serotypes remains the biggest hurdle to overcome. Therefore, deep investigation is crucial for the potent and effective therapeutic drugs development. In addition, the cross-reactivity of flaviviruses that leads to false diagnosis in clinical settings could result to delay proper intervention management. Thus, the accurate diagnostic with high specificity and sensitivity is highly required to provide prompt diagnosis in respect to render early treatment for DENV infected individuals. In this review, the recent development of neutralizing antibodies, antiviral agents, and vaccine candidates in therapeutic platform for DENV infection will be discussed. Moreover, the discovery of antigenic cryptic epitopes, principle of molecular mimicry, and application of single-chain or single-domain antibodies towards DENV will also be presented.

Modeling and affinity maturation of an anti-CD20 nanobody: a comprehensive in-silico investigation

B-cell Non-Hodgkin lymphomas are the malignancies of lymphocytes. CD20 is a membrane protein, which is highly expressed on the cell surface of the B-cells in NHL. Treatments using monoclonal antibodies (mAbs) have resulted in failure in some cases. Nanobodies (NBs), single-domain antibodies with low molecular weights and a high specificity in antigen recognition, could be practical alternatives for traditional mAbs with superior characteristics. To design an optimized NB as a candidate CD20 inhibitor with raised binding affinity to CD20, the structure of anti-CD20 NB was optimized to selectively target CD20. The 3D structure of the NB was constructed based on the optimal templates (6C5W and 5JQH), and the key residues were determined by applying a molecular docking study. After identifying the key residues, some mutations were introduced using a rational protocol to improve the binding affinity of the NB to CD20. The rational mutations were conducted using the experimental design (Taguchi method). Six residues (Ser27, Thr28, Phe29, Ile31, Asp99, and Asn100) were selected as the key residues, and five residues were targeted for rational mutation (Trp, Phe, His, Asp, and Tyr). Based on the mutations suggested by the experimental design, two optimized NB structures were constructed. NB2 showed a remarkable binding affinity to CD20 in docking studies with a binding energy of - 853 kcal/mol. The optimized NB was further evaluated using molecular dynamics simulation. The results revealed that CDR1 (complementarity determining regions1) and CDR3 are essential loops for recognizing the antigen. NB2 could be considered as a potential inhibitor of CD20, though experimental evaluations are needed to confirm it.

Engineered NK Cells Against Cancer and Their Potential Applications Beyond

Cell therapy is an innovative therapeutic concept where viable cells are implanted, infused, or grafted into a patient to treat impaired or malignant tissues. The term was first introduced circa the 19th century and has since resulted in multiple breakthroughs in different fields of medicine, such as neurology, cardiology, and oncology. Lately, cell and gene therapy are merging to provide cell products with additional or enhanced properties. In this context, adoptive transfer of genetically modified cytotoxic lymphocytes has emerged as a novel treatment option for cancer patients. To this day, five cell therapy products have been FDA approved, four of which for CD19-positive malignancies and one for B-cell maturation antigen (BCMA)-positive malignancies. These are personalized immunotherapies where patient T cells are engineered to express chimeric antigen receptors (CARs) with the aim to redirect the cells against tumor-specific antigens. CAR-T cell therapies show impressive objective response rates in clinical trials that, in certain instances, may reach up to 80%. However, the life-threatening side effects associated with T cell toxicity and the manufacturing difficulties of developing personalized therapies hamper their widespread use. Recent literature suggests that Natural Killer (NK) cells, may provide a safer alternative and an 'off-the-shelf' treatment option thanks to their potent antitumor properties and relatively short lifespan. Here, we will discuss the potential of NK cells in CAR-based therapies focusing on the applications of CAR-NK cells in cancer therapy and beyond.

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.