Antibody Discovery

Antibody discovery is the process of identifying and characterizing novel antibodies for therapeutic, diagnostic, or research purposes. This workflow guides users through the entire process, from processing raw sequencing data from various library types — such as natural immune repertoires, scFv display libraries, or VHH libraries — to selecting final lead candidates. Key steps include sequence annotation, clustering of similar antibodies, enrichment analysis to identify potent binders from selection campaigns, and assessment of sequence liabilities to ensure good developability. By integrating functional assay data, the platform enables a comprehensive, data-driven approach to select the most promising antibody candidates for further validation.

📄️ Types of Antibody Libraries

The first step in any antibody discovery project is understanding your source material and sequencing strategy. Whether you are mining natural immune responses from an immunized animal or screening a synthetic library, the structure of your antibody constructs and the way they are sequenced fundamentally shape the data you will analyze.

📄️ How to Import Data

Before you can begin annotating, clustering, or analyzing your antibody libraries, you need to import your sequencing data into the project. This could be raw FASTQ files from a single-cell V(D)J run, a VHH sequencing experiment, or from the panning rounds of an scFv display library.

📄️ Annotating Natural Antibody Libraries

After importing your raw sequencing data, the next critical step is annotation. This process transforms your FASTQ files into structured clonotype tables by:

📄️ Annotating scFv Libraries

Single-chain variable fragments (scFv) are engineered proteins that fuse the variable regions of heavy (VH) and light (VL) antibody chains into a single polypeptide, connected by a flexible peptide linker. Analyzing scFv sequencing data is crucial for antibody discovery and engineering.

📄️ Annotating Bulk Synthetic Libraries

Synthetic antibody libraries, often used in display technologies like phage display, require a specialized bioinformatics approach for annotation. Unlike natural libraries that are aligned against a public reference gene database (like IMGT), synthetic libraries must be aligned against a known, custom reference sequence of the library's construct.

📄️ Antibody Clustering

The Clonotype Clustering block groups similar antibody sequences into clusters based on user-defined criteria. This process is essential for identifying and analyzing families of related sequences that may share binding properties. Under the hood, this block leverages the powerful and fast MMseqs2 tool for sequence clustering.

📄️ Enrichment Analysis

In antibody discovery campaigns using display technologies (like phage, yeast, or ribosome display), the primary goal is to isolate high-affinity binders from a vast library through successive rounds of selection or "panning." The Clonotype Enrichment analysis block is a powerful tool designed to identify and quantify the antibody clonotypes (or clusters of similar clonotypes) that become more abundant throughout this selection process.

📄️ Antibody Sequence Liabilities Assessment

In antibody discovery, identifying promising candidates goes beyond just their binding affinity. It's crucial to also assess their potential for manufacturability, stability, and safety. The developability of an antibody is a critical factor that determines its likelihood of becoming a successful therapeutic. This refers to a set of physicochemical properties that make an antibody amenable to being manufactured at a large scale, formulated for stability, and safe for administration to patients.

📄️ Functional Assay Data Integration

Bridging the gap between high-throughput next-generation sequencing (NGS) and functional validation is a critical step in antibody discovery. This guide demonstrates how to use the Immune Assay Data block in Platforma to import results from functional assays (e.g., ELISA, SPR, cell-based assays) and associate this data with your antibody clonotypes identified from NGS.

📄️ Antibody Lead Selection

The Antibody/TCR Lead Selection block is a powerful downstream tool designed to automate and streamline the process of identifying the most promising antibody candidates from your datasets. It serves as a capstone analysis, integrating results from upstream enrichment, clustering, liability assessments, and somatic hypermutation (SHM) analysis to help you filter, rank, and select a final list of diverse, high-quality leads for subsequent wet-lab validation.