Third generation antibody discovery methods: in silico rational design

Antibody heavy chain CDR3 length-dependent usage of human IGHJ4 and IGHJ6 germline genes

Therapeutic antibody discovery using synthetic diversity has been proved productive, especially for target proteins not suitable for traditional animal immunization-based antibody discovery approaches. Recently, many lines of evidences suggest that the quality of synthetic diversity design limits the development success of synthetic antibody hits. The aim of our study is to understand the quality limitation and to properly address the challenges with a better design. Using VH3–23 as a model framework, we observed and quantitatively mapped CDR-H3 loop length-dependent usage of human IGHJ4 and IGHJ6 germline genes in the natural human immune repertoire. Skewed usage of DH2-JH6 and DH3-JH6 rearrangements was quantitatively determined in a CDR-H3 length-dependent manner in natural human antibodies with long CDR-H3 loops. Structural modeling suggests choices of JH help to stabilize antibody CDR-H3 loop and JH only partially contributes to the paratope. Our observations shed light on the design of next-generation synthetic diversity with improved probability of success.

Evolution of KIPPIS as a versatile platform for evaluating intracellularly functional peptide aptamers

Chimeric proteins have been widely used to evaluate intracellular protein–protein interactions (PPIs) in living cells with various readouts. By combining an interleukin-3-dependent murine cells and chimeric proteins containing a receptor tyrosine kinase c-kit, we previously established a c-kit-based PPI screening (KIPPIS) system to evaluate and select protein binders. In the KIPPIS components, proteins of interest are connected with a chemically inducible helper module and the intracellular domain of the growth-signaling receptor c-kit, which detects PPIs based on cell proliferation as a readout. In this system, proteins of interest can be incorporated into chimeric proteins without any scaffold proteins, which would be advantageous for evaluating interaction between small peptides/domains. To prove this superiority, we apply KIPPIS to 6 peptide aptamer–polypeptide pairs, which are derived from endogenous, synthetic, and viral proteins. Consequently, all of the 6 peptide aptamer–polypeptide interactions are successfully detected by cell proliferation. The detection sensitivity can be modulated in a helper ligand-dependent manner. The assay results of KIPPIS correlate with the activation levels of Src, which is located downstream of c-kit-mediated signal transduction. Control experiments reveal that KIPPIS clearly discriminates interacting aptamers from non-interacting ones. Thus, KIPPIS proves to be a versatile platform for evaluating the binding properties of peptide aptamers.

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10³ variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 10⁴ variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 10⁸ trastuzumab variants and predict the HER2-specific subset (approximately 1 × 10⁶ variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.

Prospects of Neutralizing Nanobodies Against SARS-CoV-2

Since December 2019, the SARS-CoV-2 has erupted on a large scale worldwide and spread rapidly. Passive immunization of antibody-related molecules provides opportunities for prevention and treatment of high-risk patients and children. Nanobodies (Nbs) have many strong physical and chemical properties. They can be atomized, administered by inhalation, and can be directly applied to the infected site, with fast onset, high local drug concentration/high bioavailability, and high patient compliance (no needles). It has very attractive potential in the treatment of respiratory viruses. Rapid and low-cost development of Nbs targeting SARS-CoV-2 can quickly be achieved. Nbs against SARS-CoV-2 mutant strains also can be utilized quickly to prevent the virus from escaping. It provides important technical supports for the treatment of the SARS-CoV-2 and has the potential to become an essential medicine in the toolbox against the SARS-CoV-2.

Animal immunization merges with innovative technologies: A new paradigm shift in antibody discovery

Animal-derived antibody sources, particularly, transgenic mice that are engineered with human immunoglobulin loci, along with advanced antibody generation technology platforms have facilitated the discoveries of human antibody therapeutics. For example, isolation of antigen-specific B cells, microfluidics, and next-generation sequencing have emerged as powerful tools for identifying and developing monoclonal antibodies (mAbs). These technologies enable not only antibody drug discovery but also lead to the understanding of B cell biology, immune mechanisms and immunogenetics of antibodies. In this perspective article, we discuss the scientific merits of animal immunization combined with advanced methods for antibody generation as compared to animal-free alternatives through in-vitro-generated antibody libraries. The knowledge gained from animal-derived antibodies concerning the recombinational diversity, somatic hypermutation patterns, and physiochemical properties is found more valuable and prerequisite for developing in vitro libraries, as well as artificial intelligence/machine learning methods to discover safe and effective mAbs.

The Diagnostic Potential of Amyloidogenic Proteins

Neurodegenerative disorders are a highly prevalent class of diseases, whose pathological mechanisms start before the appearance of any clear symptoms. This fact has prompted scientists to search for biomarkers that could aid early treatment. These currently incurable pathologies share the presence of aberrant aggregates called amyloids in the nervous system, which are composed of specific proteins. In this review, we discuss how these proteins, their conformations and modifications could be exploited as biomarkers for diagnostic purposes. We focus on proteins that are associated with the most prevalent neurodegenerative disorders, including Alzheimer's and Parkinson's diseases, amyotrophic lateral sclerosis, and frontotemporal dementia. We also describe current challenges in detection, the most recent techniques with diagnostic potentials and possible future developments in diagnosis.

Applications of Machine and Deep Learning in Adaptive Immunity

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

Rationally Designed Bicyclic Peptides Prevent the Conversion of Aβ42 Assemblies Into Fibrillar Structures

There is great interest in drug discovery programs targeted at the aggregation of the 42-residue form of the amyloid β peptide (Aβ42), since this molecular process is closely associated with Alzheimer’s disease. The use of bicyclic peptides may offer novel opportunities for the effective modification of Aβ42 aggregation and the inhibition of its cytotoxicity, as these compounds combine the molecular recognition ability of antibodies with a relatively small size of about 2 kD. Here, to pursue this approach, we rationally designed a panel of six bicyclic peptides targeting various epitopes along the sequence of Aβ42 to scan its most amyloidogenic region (residues 13–42). Our kinetic analysis and structural studies revealed that at sub-stoichiometric concentrations the designed bicyclic peptides induce a delay in the condensation of Aβ42 and the subsequent transition to a fibrillar state, while at higher concentrations they inhibit such transition. We thus suggest that designed bicyclic peptides can be employed to inhibit amyloid formation by redirecting the aggregation process toward amorphous assemblies.

Quantifying misfolded protein oligomers as drug targets and biomarkers in Alzheimer and Parkinson diseases

Protein misfolding and aggregation are characteristic of a wide range of neurodegenerative disorders, including Alzheimer and Parkinson diseases. A hallmark of these diseases is the aggregation of otherwise soluble and functional proteins into amyloid aggregates. Although for many decades such amyloid deposits have been thought to be responsible for disease progression, it is now increasingly recognized that the misfolded protein oligomers formed during aggregation are, instead, the main agents causing pathological processes. These oligomers are transient and heterogeneous, which makes it difficult to detect and quantify them, generating confusion about their exact role in disease. The lack of suitable methods to address these challenges has hampered efforts to investigate the molecular mechanisms of oligomer toxicity and to develop oligomer-based diagnostic and therapeutic tools to combat protein misfolding diseases. In this Review, we describe methods to quantify misfolded protein oligomers, with particular emphasis on diagnostic applications as disease biomarkers and on therapeutic applications as target biomarkers. The development of these methods is ongoing, and we discuss the challenges that remain to be addressed to establish measurement tools capable of overcoming existing limitations and to meet present needs.

Systematic Activity Maturation of a Single-Domain Antibody with Non-canonical Amino Acids through Chemical Mutagenesis

Great advances have been made over the last four decades in therapeutic and diagnostic applications of antibodies. The activity maturation of antibody candidates, however, remains a significant challenge. To address this problem, we present a method that enables the systematic enhancement of the activity of a single-domain antibody through the post-translational installation of non-canonical side chains by chemical mutagenesis. We illustrate this approach by performing a structure-activity relationship study beyond the 20 naturally occurring amino acids on a single-domain antibody designed in silico to inhibit the aggregation of the amyloid-β peptide, a process closely linked to Alzheimer's disease. We found that this approach can improve, by five orders of magnitude, the anti-aggregation activity of the starting single-domain antibody, without affecting its stability. These results show that the expansion of the chemical space available to antibodies through chemical mutagenesis can be exploited for the systematic enhancement of the activity of these molecules.

Directed Evolution of Therapeutic Antibodies Targeting Glycosylation in Cancer

Simple Summary

We generated a platform for designing optimized functional therapeutic antibodies against cancer glycans. The target tumor-associated carbohydrate antigen is commonly expressed in colon and pancreatic cancers. We developed a system for selection of potent antibodies by yeast surface display against this carbohydrate antigen, then showed that elite clones have potent affinity, specificity, cancer cell binding, and therapeutic efficacy. These tools have broad utility for manipulating and engineering antibodies against carbohydrate antigens, and provide major innovative avenues of research in the field of cancer therapy and diagnostics.

Abstract

Glycosylation patterns commonly change in cancer, resulting in expression of tumor-associated carbohydrate antigens (TACA). While promising, currently available anti-glycan antibodies are not useful for clinical cancer therapy. Here, we show that potent anti-glycan antibodies can be engineered to acquire cancer therapeutic efficacy. We designed yeast surface display to generate and select for therapeutic antibodies against the TACA SLe^a (CA19−9) in colon and pancreatic cancers. Elite clones showed increased affinity, better specificity, improved binding of human pancreatic and colon cancer cell lines, and increased complement-dependent therapeutic efficacy. Molecular modeling explained the structural basis for improved antibody functionality at the molecular level. These new tools of directed molecular evolution and selection for effective anti-glycan antibodies, provide insights into the mechanisms of cancer therapy targeting glycosylation, and provide major methodological advances that are likely to open up innovative avenues of research in the field of cancer theranostics.

A rationally designed bicyclic peptide remodels Aβ42 aggregation in vitro and reduces its toxicity in a worm model of Alzheimer's disease

Bicyclic peptides have great therapeutic potential since they can bridge the gap between small molecules and antibodies by combining a low molecular weight of about 2 kDa with an antibody-like binding specificity. Here we apply a recently developed in silico rational design strategy to produce a bicyclic peptide to target the C-terminal region (residues 31–42) of the 42-residue form of the amyloid β peptide (Aβ42), a protein fragment whose aggregation into amyloid plaques is linked with Alzheimer’s disease. We show that this bicyclic peptide is able to remodel the aggregation process of Aβ42 in vitro and to reduce its associated toxicity in vivo in a C. elegans worm model expressing Aβ42. These results provide an initial example of a computational approach to design bicyclic peptides to target specific epitopes on disordered proteins.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage–derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Engineering a Histone Reader Protein by Combining Directed Evolution, Sequencing, and Neural Network Based Ordinal Regression

Directed evolution is a powerful approach for engineering proteins with enhanced affinity or specificity for a ligand of interest but typically requires many rounds of screening/library mutagenesis to obtain mutants with desired properties. Furthermore, mutant libraries generally only cover a small fraction of the available sequence space. Here, for the first time, we use ordinal regression to model protein sequence data generated through successive rounds of sorting and amplification of a protein-ligand system. We show that the ordinal regression model trained on only two sorts successfully predicts chromodomain CBX1 mutants that would have stronger binding affinity with the H3K9me3 peptide. Furthermore, we can extract the predictive features using contextual regression, a method to interpret nonlinear models, which successfully guides identification of strong binders not even present in the original library. We have demonstrated the power of this approach by experimentally confirming that we were able to achieve the same improvement in binding affinity previously achieved through a more laborious directed evolution process. This study presents an approach that reduces the number of rounds of selection required to isolate strong binders and facilitates the identification of strong binders not present in the original library.

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties

SUMMARY

Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools.

AVAILABILITY AND IMPLEMENTATION

A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.

Rational design of a conformation-specific antibody for the quantification of Aβ oligomers

The accurate quantification of the amounts of small oligomeric assemblies formed by the amyloid β (Aβ) peptide represents a major challenge in the Alzheimer’s field. There is therefore great interest in the development of methods to specifically detect these oligomers by distinguishing them from larger aggregates. The availability of these methods will enable the development of effective diagnostic and therapeutic interventions for this and other diseases related to protein misfolding and aggregation. We describe here a single-domain antibody able to selectively quantify oligomers of the Aβ peptide in isolation and in complex protein mixtures from animal models of disease.

Protein misfolding and aggregation is the hallmark of numerous human disorders, including Alzheimer’s disease. This process involves the formation of transient and heterogeneous soluble oligomers, some of which are highly cytotoxic. A major challenge for the development of effective diagnostic and therapeutic tools is thus the detection and quantification of these elusive oligomers. Here, to address this problem, we develop a two-step rational design method for the discovery of oligomer-specific antibodies. The first step consists of an “antigen scanning” phase in which an initial panel of antibodies is designed to bind different epitopes covering the entire sequence of a target protein. This procedure enables the determination through in vitro assays of the regions exposed in the oligomers but not in the fibrillar deposits. The second step involves an “epitope mining” phase, in which a second panel of antibodies is designed to specifically target the regions identified during the scanning step. We illustrate this method in the case of the amyloid β (Aβ) peptide, whose oligomers are associated with Alzheimer’s disease. Our results show that this approach enables the accurate detection and quantification of Aβ oligomers in vitro, and in Caenorhabditis elegans and mouse hippocampal tissues.

Chimeric Phage Nanoparticles for Rapid Characterization of Bacterial Pathogens: Detection in Complex Biological Samples and Determination of Antibiotic Sensitivity

Rapid, specific, and sensitive detection of pathogenic bacteria in drink, food, and clinical samples is an important goal for public health. In addition, rapid characterization of antibiotic susceptibility could inform clinical choices and improve antibiotic stewardship. We previously reported a straightforward, inexpensive strategy to detect Gram-negative bacterial pathogens, including Pseudomonas aeruginosa, Vibrio cholerae, and Escherichia coli, taking advantage of the high affinity and specificity of phages for their bacterial hosts. Chimeric phages targeted different bacterial pathogens, and thiolation of the phages induced aggregation of gold nanoparticles (AuNPs), leading to a visible colorimetric response in the presence of at least ∼100 cells of the target bacteria. Here, we apply this strategy to complex biological samples (milk, urine, and swabs from a porcine ex vivo model of P. aeruginosa infection). We also show that this assay can be used to identify the antibiotic susceptibility profile based on detection of bacterial growth in the presence of different antibiotics. The prospect for using phage-conjugated AuNPs to detect bacterial pathogens in clinical samples and guide antibiotic choice is discussed.

The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies

The development of therapeutic monoclonal antibodies (mAbs) can be hindered by their tendency to aggregate throughout their lifetime, which can illicit immunogenic responses and render mAb manufacturing unfeasible. Consequently, there is a need to identify mAbs with desirable thermodynamic stability, solubility, and lack of self-association. These behaviors are assessed using an array of in silico and in vitro assays, as no single assay can predict aggregation and developability. We have developed an extensional and shear flow device (EFD), which subjects proteins to defined hydrodynamic forces which mimic those experienced in bioprocessing. Here, we utilize the EFD to explore the aggregation propensity of 33 IgG1 mAbs, whose variable domains are derived from clinical antibodies. Using submilligram quantities of material per replicate, wide-ranging EFD-induced aggregation (9-81% protein in pellet) was observed for these mAbs, highlighting the EFD as a sensitive method to assess aggregation propensity. By comparing the EFD-induced aggregation data to those obtained previously from 12 other biophysical assays, we show that the EFD provides distinct information compared with current measures of adverse biophysical behavior. Assessing a candidate's liability to hydrodynamic force thus adds novel insight into the rational selection of developable mAbs that complements other assays.

Seeking high-priority mutations enabling successful antibody-breeding: systematic analysis of a mutant that gained over 100-fold enhanced affinity

"Antibody-breeding" has provided therapeutic/diagnostic antibody mutants with greater performance than native antibodies. Typically, random point mutations are introduced into the VH and VL domains of parent antibodies to generate diverse libraries of single-chain Fv fragments (scFvs), from which evolved mutants are selected. We produced an scFv against estradiol-17β with 11 amino acid substitutions and a >100-fold improved affinity constant (Ka = 1.19 × 1010 M-1) over the parent scFv, enabling immunoassays with >30-fold higher sensitivity. We systematically analyzed contributions of these substitutions to the affinity enhancement. Comparing various partial scFv revertants based on their Kas indicated that a revertant with four substitutions (VH-L100gQ, VL-I29V, -L36M, -S77G) exhibited somewhat higher affinity (Ka = 1.46 × 1010 M-1). Finally, the VH-L100gQ substitution, occurring in VH complementarity-determining region (CDR) 3, was found to be the highest-priority for improving the affinity, and VL-I29V and/or VL-L36M cooperated significantly. These findings encouraged us to reconsider the potential of VH-CDR3-targeting mutagenesis, which has been frequently attempted. The substitution(s) wherein might enable a "high rate of return" in terms of selecting mutants with dramatically enhanced affinities. The "high risk" of generating a tremendous excess of "junk mutants" can be overcome with the efficient selection systems that we developed.

A Sequential Multidimensional Analysis Algorithm for Aptamer Identification based on Structure Analysis and Machine Learning

Molecular recognition ligands are of great significance in many fields, but our ability to develop new recognition molecules remains to be expanded. Here, we developed a Sequential Multidimensional Analysis algoRiThm for aptamer discovery (SMART-Aptamer) from high-throughput sequencing (HTS) data of SELEX libraries based on multilevel structure analysis and unsupervised machine learning to discover nucleic acid recognition ligands with high accuracy and efficiency. We validated SMART-Aptamer with three sets of HTS data from screening pools against hESCs, EpCAM, and CSV. High affinity aptamers for all three targets were successfully obtained, and the results revealed that SMART-Aptamer is able to pick out high affinity aptamers with low false positive and negative rates. With the advantages of accuracy, efficiency, and robustness, SMART-Aptamer represents a paradigm-shift strategy for the discovery of binding ligands for a variety of biomedical applications.

Phage Display Derived Monoclonal Antibodies: From Bench to Bedside

Monoclonal antibodies (mAbs) have become one of the most important classes of biopharmaceutical products, and they continue to dominate the universe of biopharmaceutical markets in terms of approval and sales. They are the most profitable single product class, where they represent six of the top ten selling drugs. At the beginning of the 1990s, an in vitro antibody selection technology known as antibody phage display was developed by John McCafferty and Sir. Gregory Winter that enabled the discovery of human antibodies for diverse applications, particularly antibody-based drugs. They created combinatorial antibody libraries on filamentous phage to be utilized for generating antigen specific antibodies in a matter of weeks. Since then, more than 70 phage-derived antibodies entered clinical studies and 14 of them have been approved. These antibodies are indicated for cancer, and non-cancer medical conditions, such as inflammatory, optical, infectious, or immunological diseases. This review will illustrate the utility of phage display as a powerful platform for therapeutic antibodies discovery and describe in detail all the approved mAbs derived from phage display.

Nanobody stability engineering by employing the ΔTm shift; a comparison with apparent rate constants of heat-induced aggregation

The antigen-binding domains of camelid heavy-chain antibodies, also called nanobodies, gained strong attention because of their unique functional and biophysical properties. They gave rise to an entire spectrum of applications in biotechnology, research and medicine. Despite several reports about reversibly refolding nanobodies, protein aggregation plays a major role in nanobody thermoresistance, asking for strategies to engineer their refolding behavior. Here, we use measurements of nanobody aggregation kinetics to validate structural features in the nanobody fold that are suppressing heat-induced nanobody aggregation. Furthermore, the kinetic measurements yielded a detailed insight into the concept of the ΔTm shift, a metric for protein aggregation propensities obtained from differential scanning fluorimetry measurements. By relating the equilibrium measurements of the ΔTm shift to the kinetic measurements of heat-induced nanobody aggregation, a distinct relationship could be identified that allows a prediction of nanobody aggregation rates from a simple equilibrium measurement of ΔTm.

A consensus protocol for the in silico optimisation of antibody fragments

We present an in silico mutagenetic protocol for improving the binding affinity of single domain antibodies (or nanobodies, VHHs). The method iteratively attempts random mutations in the interacting region of the protein and evaluates the resulting binding affinity towards the target by scoring, with a collection of scoring functions, short explicit solvent molecular dynamics trajectories of the binder-target complexes. The acceptance/rejection of each attempted mutation is carried out by a consensus decision-making algorithm, which considers all individual assessments derived from each scoring function. The method was benchmarked by evolving a single complementary determining region (CDR) of an anti-HER2 VHH hit obtained by direct panning of a phage display library. The optimised VHH mutant showed significantly enhanced experimental affinity with respect to the original VHH it matured from. The protocol can be employed as it is for the optimization of peptides, antibody fragments, and (given enough computational power) larger antibodies.

Molecular Crowding Evolution for Enabling Discovery of Enthalpy-Driven Aptamers for Robust Biomedical Applications

An enthalpy-driven ligand is an ideal probe for practical applications because of the formation of abundant specific bonds between the ligand and target, compared to an entropy-driven ligand with a similar Gibbs free energy change. However, there has been a lack of direct discovery strategy for identifying enthalpy-driven ligands. In this work, a molecular crowding SELEX (systematic evolution of ligands by exponential enrichment) strategy for discovering enthalpy-driven aptamers was developed to improve the affinity and selectivity of aptamers in complex samples. Three aptamer sequences were successfully evolved against a tumor biomarker protein, and all proved to be enthalpy-driven by thermodynamics analysis, establishing the feasibility of molecular crowding SELEX for effective discovery of enthalpy-driven aptamers. Further comparison of aptamers evolved from conventional SELEX in buffer and molecular crowding SELEX (SYL-H2C) revealed much higher affinity of SYL-H2C. With its improved thermodynamic properties, the enthalpy-driven SYL-H2C aptamer was able to detect circulating tumor cells in real cancer patient blood samples with excellent detection accuracy (10/10). The proposed molecular crowding screening strategy offers a promising direction for discovering robust binding probes for a great variety of biomedical applications.

De novo discovery of antibody drugs - great promise demands scrutiny

We live in an era of rapidly advancing computing capacity and algorithmic sophistication. "Big data" and "artificial intelligence"find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts.

Connecting the sequence dots: shedding light on the genesis of antibodies reported to be designed in silico

Two recent publications out of the same research laboratory report on structure-based in silico design of antibodies against viral targets without sequence disclosure. Cross-referencing the published data to patent databases, we established the sequence identity of said computationally designed antibodies. In both cases, the antibodies align with high sequence identity to previously reported antibodies of the same specificity. This clear underlying sequence relationship, which is far closer than the antibody templates reported to seed the computational design, suggests an alternative origin of the computationally designed antibodies. The lack of both reproducible computational algorithms and of output sequences in the initial publications obscures the relationship to previously reported antibodies, and sows doubt as to the genesis narrative described therein.

AbRSA: A robust tool for antibody numbering

The remarkable progress in cancer immunotherapy in recent years has led to the heat of great development for therapeutic antibodies. Antibody numbering, which standardizes a residue index at each position of an antibody variable domain, is an important step in immunoinformatic analysis. It provides an equivalent index for the comparison of sequences or structures, which is particularly valuable for antibody modeling and engineering. However, due to the extremely high diversity of antibody sequences, antibody-numbering tools cannot work in all cases. This article introduces a new antibody-numbering tool named AbRSA, which integrates heuristic knowledge of region-specific features into sequence mapping to enhance the robustness. The benchmarks demonstrate that, AbRSA exhibits robust performance in numbering sequences with diverse lengths and patterns compared with the state-of-the-art tools. AbRSA offers a user-friendly interface for antibody numbering, complementarity-determining region delimitation, and 3D structure rendering. It is freely available at http://cao.labshare.cn/AbRSA.