Hallucinating structure-conditioned antibody libraries for target-specific binders

How well do contextual protein encodings learn structure, function, and evolutionary context?

In proteins, the optimal residue at any position is determined by its structural, evolutionary, and functional contexts-much like how a word may be inferred from its context in language. We trained masked label prediction models to learn representations of amino acid residues in different contexts. We focus questions on evolution and structural flexibility and whether and how contextual encodings derived through pretraining and fine-tuning may improve representations for specialized contexts. Sequences sampled from our learned representations fold into template structure and reflect sequence variations seen in related proteins. For flexible proteins, sampled sequences traverse the full conformational space of the native sequence, suggesting that plasticity is encoded in the template structure. For protein-protein interfaces, generated sequences replicate wild-type binding energies across diverse interfaces and binding strengths in silico. For the antibody-antigen interface, fine-tuning recapitulate conserved sequence patterns, while pretraining on general contexts improves sequence recovery for the hypervariable H3 loop. A record of this paper's transparent peer review process is included in the supplemental information.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Rapid affinity optimization of an anti-TREM2 clinical lead antibody by cross-lineage immune repertoire mining

We describe a process for rapid antibody affinity optimization by repertoire mining to identify clones across B cell clonal lineages based on convergent immune responses where antigen-specific clones with the same heavy (VH) and light chain germline segment pairs, or parallel lineages, bind a single epitope on the antigen. We use this convergence framework to mine unique and distinct VH lineages from rat anti-triggering receptor on myeloid cells 2 (TREM2) antibody repertoire datasets with high diversity in the third complementarity-determining loop region (CDR H3) to further affinity-optimize a high-affinity agonistic anti-TREM2 antibody while retaining critical functional properties. Structural analyses confirm a nearly identical binding mode of anti-TREM2 variants with subtle but significant structural differences in the binding interface. Parallel lineage repertoire mining is uniquely tailored to rationally explore the large CDR H3 sequence space in antibody repertoires and can be easily and generally applied to antibodies discovered in vivo.

Antibody design using deep learning: from sequence and structure design to affinity maturation

Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

Strategies to improve the therapeutic efficacy of mesenchymal stem cell-derived extracellular vesicle (MSC-EV): a promising cell-free therapy for liver disease

Liver disease has emerged as a significant worldwide health challenge due to its diverse causative factors and therapeutic complexities. The majority of liver diseases ultimately progress to end-stage liver disease and liver transplantation remains the only effective therapy with the limitations of donor organ shortage, lifelong immunosuppressants and expensive treatment costs. Numerous pre-clinical studies have revealed that extracellular vesicles released by mesenchymal stem cells (MSC-EV) exhibited considerable potential in treating liver diseases. Although natural MSC-EV has many potential advantages, some characteristics of MSC-EV, such as heterogeneity, uneven therapeutic effect, and rapid clearance in vivo constrain its clinical translation. In recent years, researchers have explored plenty of ways to improve the therapeutic efficacy and rotation rate of MSC-EV in the treatment of liver disease. In this review, we summarized current strategies to enhance the therapeutic potency of MSC-EV, mainly including optimization culture conditions in MSC or modifications of MSC-EV, aiming to facilitate the development and clinical application of MSC-EV in treating liver disease.

AI Models for Protein Design are Driving Antibody Engineering

Therapeutic antibody engineering seeks to identify antibody sequences with specific binding to a target and optimized drug-like properties. When guided by deep learning, antibody generation methods can draw on prior knowledge and experimental efforts to improve this process. By leveraging the increasing quantity and quality of predicted structures of antibodies and target antigens, powerful structure-based generative models are emerging. In this review, we tie the advancements in deep learning-based protein structure prediction and design to the study of antibody therapeutics.

Cytoplasm Hydrogelation-Mediated Cardiomyocyte Sponge Alleviated Coxsackievirus B3 Infection

Viral myocarditis (VMC), commonly caused by coxsackievirus B3 (CVB3) infection, lacks specific treatments and leads to serious heart conditions. Current treatments, such as IFNα and ribavirin, show limited effectiveness. Herein, rather than inhibiting virus replication, this study introduces a novel cardiomyocyte sponge, intracellular gelated cardiomyocytes (GCs), to trap and neutralize CVB3 via a receptor-ligand interaction, such as CAR and CD55. By maintaining cellular morphology, GCs serve as sponges for CVB3, inhibiting infection. In vitro results revealed that GCs could inhibit CVB3 infection on HeLa cells. In vivo, GCs exhibited a strong immune escape ability and effectively inhibited CVB3-induced viral myocarditis with a high safety profile. The most significant implication of this study is to develop a universal antivirus infection strategy via intracellular gelation of the host cell, which can be employed not only for treating defined pathogenic viruses but also for a rapid response to infection outbreaks caused by mutable and unknown viruses.