In Silico Engineering Towards Enhancement of Bap–VHH Monoclonal Antibody Binding Affinity

TransABseq: A Two-Stage Approach for Predicting Antigen-Antibody Binding Affinity Changes upon Mutation Based on Protein Sequences

The antigen-antibody interaction represents a critical mechanism in host defense, contributing to pathogen neutralization, tumor surveillance, immunotherapy, and in vitro disease detection. Owing to their exceptional specificity, affinity, and selectivity, antibodies have been extensively utilized in the development of clinical diagnostic, therapeutic, and prophylactic strategies. In this study, we propose TransABseq, a novel computational framework specifically designed to predict the effects of missense mutations on antigen-antibody interactions. The model's innovative two-stage architecture enables comprehensive feature analysis: in the first stage, multiple embeddings of protein language models are processed through a Transformer encoder module and a multiscale convolutional module; in the second stage, the XGBOOST model is used to perform quantitative output based on the deeply fused features. A critical advancement contributing to the effectiveness of TransABseq is the deep feature fusion strategy, which reveals the biochemical properties of proteins. By leveraging the multilayer self-attention mechanism of the Transformer to capture complex global dependencies within sequences and mining features at different hierarchical levels through multiscale convolution, the feature abstraction capability of TransABseq is significantly enhanced. We evaluated TransABseq through three distinct cross-validation strategies on two established benchmarks and a newly reconstructed data set. As a result, TransABseq achieved average PCC values of 0.607, 0.843, and 0.794 and average RMSE values of 1.166, 1.314, and 1.337 kcal/mol in 10-fold cross-validation. Furthermore, its robustness and predictive accuracy were validated on blind test data sets, where TransABseq outperformed existing methods, enabling it to attain a PCC of 0.721 and an RMSE of 0.925 kcal/mol. The relevant data and code have been made publicly available for academic research at: https://github.com/cuifengLI/TransABseq.

Hotspot Mutagenesis Strategy Yielding a Highly Sensitive Nanobody against Parathion for Immunoassay Development

Nanobodies (Nbs) hold great potential as affinity reagents for the immunoassays of small-molecule contaminants. However, there is an urgent need for better approaches to discover higher-affinity Nbs and expand their application in food analysis. Herein, with the antiparathion nanobody (VHH9) as a model, a novel hotspot mutagenesis technique for Nb affinity maturation is reported, which targets specific motifs within Nb DNA sequences and bypasses the need for traditional 3D protein modeling to navigate candidate mutated residues. Specifically, seven residues Ser28, Tyr29, Ser32, Lys102, Phe103, Arg105, and Ala106 encoded by two AGY and two RGYW codons were selected and randomized to construct a saturation mutation library. Enhanced mutants G4, H6, and G2 were successfully isolated and then characterized by indirect competitive enzyme-linked immunosorbent assay (ic-ELISA), with half-maximal inhibition concentrations (IC50) of 4.7, 3.5, and 2.9 ng/mL, respectively, 2.1-, 2.9-, and 3.5-fold lower than that of the original Nbs. Subsequently, a biotin-streptavidin-amplified ELISA (BA-ELISA) was developed for the detection of parathion based on biotinylated H6. The developed BA-ELISA showed an IC50 value as low as 1.5 ng/mL, which was 2.3-fold improvement in sensitivity compared to H6-based ic-ELISA. The average recoveries in vegetable and fruit samples ranged from 87.0% to 113.0%. In summary, this work demonstrated that hotspot mutagenesis is effective for maximizing the efficiency to obtain Nbs with improved sensitivity, and the developed BA-ELISA is a robust tool for the routine screening of parathion.

Rapid transformation of nanobodies affinity based on AlphaFold2's high-accuracy predictions and interaction analysis for enrofloxacin detection in coastal fish

High-affinity antibodies are crucial in biosensors, disease diagnostics, therapeutic drug development, and immunological analysis, making the enhancement of antibody affinity a key research focus within the field. Computer-aided design is recognized as a time-saving and labor-efficient method for nanobodies in vitro affinity maturation. Compared to experimental mutagenesis techniques, it is advantageous due to the elimination of the need for laborious library construction and screening processes. However, these approaches are constrained by structural prediction since inaccuracy in structure could readily result in maturation failures. Herein, a novel nanobodies modification method for in vitro affinity maturation, utilizing the high accuracy prediction of AlphaFold2, was employed to rapidly transform a low affinity nanobody against enrofloxacin (ENR) into one with high affinity. The molecular docking results revealed a 1.5- to 2.5-fold increase in the number of noncovalent interactions of modified nanobodies, accompanied by a reduction in binding free energy ranging from 14.1 to 62.6%. The evaluation results from ELISA and BLI indicated that the affinity of the modified nanobodies had been enhanced by 6.2-91.6 times compared to the template nanobody. Furthermore, the modified nanobodies were employed for the detection of ENR-spiked coastal fish samples. In summary, this research proposed a nanobodies modification method from a new perspective, endowing its great application potential in biosensors, food safety, and environmental monitoring.

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.

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.

Recent Progress in the Discovery and Development of Monoclonal Antibodies against Viral Infections

Monoclonal antibodies (mAbs), the new revolutionary class of medications, are fast becoming tools against various diseases thanks to a unique structure and function that allow them to bind highly specific targets or receptors. These specialized proteins can be produced in large quantities via the hybridoma technique introduced in 1975 or by means of modern technologies. Additional methods have been developed to generate mAbs with new biological properties such as humanized, chimeric, or murine. The inclusion of mAbs in therapeutic regimens is a major medical advance and will hopefully lead to significant improvements in infectious disease management. Since the first therapeutic mAb, muromonab-CD3, was approved by the U.S. Food and Drug Administration (FDA) in 1986, the list of approved mAbs and their clinical indications and applications have been proliferating. New technologies have been developed to modify the structure of mAbs, thereby increasing efficacy and improving delivery routes. Gene delivery technologies, such as non-viral synthetic plasmid DNA and messenger RNA vectors (DMabs or mRNA-encoded mAbs), built to express tailored mAb genes, might help overcome some of the challenges of mAb therapy, including production restrictions, cold-chain storage, transportation requirements, and expensive manufacturing and distribution processes. This paper reviews some of the recent developments in mAb discovery against viral infections and illustrates how mAbs can help to combat viral diseases and outbreaks.

Engineering of single-domain antibodies for next-generation snakebite antivenoms

Given the magnitude of the global snakebite crisis, strategies to ensure the quality of antivenom, as well as the availability and sustainability of its supply are under development by several research groups. Recombinant DNA technology has allowed the engineering of monoclonal antibodies and recombinant fragments as alternatives to conventional antivenoms. Besides having higher therapeutic efficacy, with broad neutralization capacity against local and systemic toxicity, novel antivenoms need to be safe and cost-effective. Due to the biological and physical chemical properties of camelid single-domain antibodies, with high volume of distribution to distal tissue, their modular format, and their versatility, their biotechnological application has grown considerably in recent decades. This article presents the most up-to-date developments concerning camelid single-domain-based antibodies against major toxins from snake venoms, the main venomous animals responsible for reported envenoming cases and related human deaths. A brief discussion on the composition, challenges, and perspectives of antivenoms is presented, as well as the road ahead for next-generation antivenoms based on single-domain antibodies.

Design of an engineered ACE2 as a novel therapeutics against COVID-19

The interaction between the angiotensin-converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of the spike protein from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a pivotal role in virus entry into the host cells. Since recombinant ACE2 protein has been suggested as an anti-SARS-CoV-2 therapeutic agent, this study was conducted to design an ACE2 protein with more desirable properties. In this regard, the amino acids with central roles in enzymatic activity of the ACE2 were substituted. Moreover, saturation mutagenesis at the interaction interface between the ACE2 and RBD was performed to increase their interaction affinity. The best mutations to increase the structural and thermal stability of the ACE2 were also selected based on B factors and mutation effects. The obtained resulted revealed that the Arg273Gln and Thr445Gly mutation have drastically reduced the binding affinity of the angiotensin-II into the active site of ACE2. The Thr27Arg mutation was determined to be the most potent mutation to increase the binding affinity. The Asp427Arg mutation was done to decrease the flexibility of the region with high B factor. The Pro451Met mutation along with the Gly448Trp mutation was predicted to increase the thermodynamic stability and thermostability of the ACE2. The designed therapeutic ACE2 would have no enzymatic activity while it could bear stronger interaction with Spike glycoprotein of the SARS-CoV-2. Moreover, decreased in vivo enzymatic degradation would be anticipated due to increased thermostability. This engineered ACE2 could be exploited as a novel therapeutic agent against COVID-19 after necessary evaluations.

mCSM-AB2: guiding rational antibody design using graph-based signatures

MOTIVATION

A lack of accurate computational tools to guide rational mutagenesis has made affinity maturation a recurrent challenge in antibody (Ab) development. We previously showed that graph-based signatures can be used to predict the effects of mutations on Ab binding affinity.

RESULTS

Here we present an updated and refined version of this approach, mCSM-AB2, capable of accurately modelling the effects of mutations on Ab-antigen binding affinity, through the inclusion of evolutionary and energetic terms. Using a new and expanded database of over 1800 mutations with experimental binding measurements and structural information, mCSM-AB2 achieved a Pearson's correlation of 0.73 and 0.77 across training and blind tests, respectively, outperforming available methods currently used for rational Ab engineering.

AVAILABILITY AND IMPLEMENTATION

mCSM-AB2 is available as a user-friendly and freely accessible web server providing rapid analysis of both individual mutations or the entire binding interface to guide rational antibody affinity maturation at http://biosig.unimelb.edu.au/mcsm_ab2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.