Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification

RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery

Discovery of therapeutic antibodies against infectious disease pathogens presents distinct challenges. Ideal candidates must possess not only the properties required for any therapeutic antibody (e.g. specificity, low immunogenicity) but also high affinity to many mutants of the target antigen. Here we present RESP2, an enhanced version of our RESP pipeline, designed for the discovery of antibodies against one or multiple antigens with simultaneously optimized developability properties. We first evaluate this pipeline in silico using the Absolut! database of scores for antibodies docked to target antigens. We show that RESP2 consistently identifies sequences that bind more tightly to a group of target antigens than any sequence present in the training set with success rates >= 85%. Popular generative AI techniques evaluated on the same datasets achieve success rates of 1.5% or less by comparison. Next we use the receptor binding domain (RBD) of the COVID-19 spike protein as a case study, and discover a highly human antibody with broad (mid to high-affinity) binding to at least 8 different variants of the RBD. These results illustrate the advantages of this pipeline for antibody discovery against a challenging target. A Python package that enables users to utilize the RESP pipeline on their own targets is available at https://github.com/Wang-lab-UCSD/RESP2, together with code needed to reproduce the experiments in this paper.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Untangling immunoglobulin genotype-function associations

Immunoglobulin (IG) genes, encoding B cell receptors (BCRs), are fundamental components of the mammalian immune system, which evolved to recognize the diverse antigenic universe present in nature. To handle these myriad inputs, BCRs are generated through combinatorial recombination of a set of highly polymorphic germline genes, resulting in a vast repertoire of antigen receptors that initiate responses to pathogens and regulate commensals. Following antigen recognition and B cell activation, memory B cells and plasma cells form, allowing for the development of anamnestic antibody (Ab) responses. How inherited variation in IG genes impacts host traits, disease susceptibility, and Ab recall responses is a topic of great interest. Here, we consider approaches to translate emerging knowledge about IG genetic diversity and expressed repertoires to inform our understanding of Ab function in health and disease etiology. As our understanding of IG genetics grows, so will our need for tools to decipher preferences for IG gene or allele usage in different contexts, to better understand antibody responses at the population level.

The RESP AI model accelerates the identification of tight-binding antibodies

High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the KD of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development.

Subtle Longitudinal Alterations in Env Sequence Potentiate Differences in Sensitivity to Broadly Neutralizing Antibodies following Acute HIV-1 Subtype C Infection

Broadly neutralizing antibodies (bNAbs) for HIV-1 prevention or cure strategies must inhibit transmitted/founder and reservoir viruses. Establishing sensitivity of circulating viruses to bNAbs and genetic patterns affecting neutralization variability may guide rational bNAbs selection for clinical development. We analyzed 326 single env genomes from nine individuals followed longitudinally following acute HIV-1 infection, with samples collected at ~1 week after the first detection of plasma viremia; 300 to 1,709 days postinfection but prior to initiating antiretroviral therapy (ART) (median = 724 days); and ~1 year post ART initiation. Sequences were assessed for phylogenetic relatedness, potential N- and O-linked glycosylation, and variable loop lengths (V1 to V5). A total of 43 env amplicons (median = 3 per patient per time point) were cloned into an expression vector and the TZM-bl assay was used to assess the neutralization profiles of 15 bNAbs targeting the CD4 binding site, V1/V2 region, V3 supersite, MPER, gp120/gp41 interface, and fusion peptide. At 1 μg/mL, the neutralization breadths were as follows: VRC07-LS and N6.LS (100%), VRC01 (86%), PGT151 (81%), 10-1074 and PGT121 (80%), and less than 70% for 10E8, 3BNC117, CAP256.VRC26, 4E10, PGDM1400, and N123-VRC34.01. Features associated with low sensitivity to V1/V2 and V3 bNAbs were higher potential glycosylation sites and/or relatively longer V1 and V4 domains, including known "signature" mutations. The study shows significant variability in the breadth and potency of bNAbs against circulating HIV-1 subtype C envelopes. VRC07-LS, N6.LS, VRC01, PGT151, 10-1074, and PGT121 display broad activity against subtype C variants, and major determinants of sensitivity to most bNAbs were within the V1/V4 domains. IMPORTANCE Broadly neutralizing antibodies (bNAbs) have potential clinical utility in HIV-1 prevention and cure strategies. However, bNAbs target diverse epitopes on the HIV-1 envelope and the virus may evolve to evade immune responses. It is therefore important to identify antibodies with broad activity in high prevalence settings, as well as the genetic patterns that may lead to neutralization escape. We investigated 15 bNAbs with diverse biophysical properties that target six epitopes of the HIV-1 Env glycoprotein for their ability to inhibit viruses that initiated infection, viruses circulating in plasma at chronic infection before antiretroviral treatment (ART), or viruses that were archived in the reservoir during ART in subtype C infected individuals in South Africa, a high burden country. We identify the antibodies most likely to be effective for clinical use in this setting and describe mutational patterns associated with neutralization escape from these antibodies.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

The applications of machine learning in HIV neutralizing antibodies research-A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Prediction poverty levels of needy college students using RF-PCA model

Nowadays, poverty-stricken college students have become a special group among college students and occupied a higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this study, a novel model, which incorporated Random Forest with Principle Components Analysis (RF-PCA), is proposed to predict poverty levels of college students. To establish this model, we collect some useful information is to construct the datasets which include 4 classes of poverty levels and 21 features of poverty-stricken college students. Furthermore, the feature dimension reduction consists of two steps: the first step is to select the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF) model; the second step is to extract 11 dimensions by means of Principle Components Analysis (PCA). Subsequently, confusion metrics and receiver operating characteristic (ROC) curves are utilized to evaluate the promising performance of the proposed model. Especially the accuracy of the model achieves 78.61% . Finally, compared with seven states of the art classification algorithms, the proposed model achieves a higher prediction accuracy, which indicates that the results provide great potential to identify the poverty levels of college students.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

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.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.