Bassi, S. | Search bioRxiv

bioRxiv2025

gffutilsAI: An AI-Agent for Interactive Genomic Feature Exploration in GFF files

AO_SCPLOWBSTRACTC_SCPLOWThe General Feature Format (GFF) is widely used to represent genomic annotations, but its hierarchical, multi-attribute structure makes manual querying and analysis challenging. Existing libraries such as gffutils provide programmatic interfaces, yet they require coding proficiency. gffutilsAI is a novel AI-powered command-line agent that enables researchers to perform interactive, natural-language-driven exploration of GFF files. Built on top of the gffutils library and the Strands AI agent framework, gffutilsAI integrates local and cloud-based large language models (LLMs) such as Llama 3.1, GPT-5, and Claude 3.5 to translate human queries into executable actions. The tool supports coordinate-based queries, attribute and GO searches, hierarchical traversal, statistical summaries, and CSV export, offering a new paradigm for accessible conversational genomics.

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bioRxiv2025

Fine-tuned Protein Language Model Identifies Antigen-specific B Cell Receptors from Immune Repertoires

Scalable identification of antigen-specific antibodies from whole immune repertoire V(D)J sequences is a central challenge in biomedical engineering. We show that protein language models (PLMs) fine-tuned on antibody heavy-chain sequences can directly predict antigen specificity from unselected immune repertoires. We assessed our model, Antigen Specificity Predictor (ASPred), against SARS-CoV-2, influenza, and HIV-AIDS antigens, observing comparable predictive performance. In the whole immune repertoire V(D)J sequences of mice immunized with the SARS-CoV-2 spike proteins receptor-binding domain (RBD), ASPred identified antibody sequences specific to RBD. Several candidate sequences were validated, including one as a heavy chain-only nanobody with 20.7 nM dissociation constant. Molecular dynamics simulations supported the predicted interactions at coarse-grained and atomic levels. Benchmarking against Barcode-Enabled Antigen Mapping (BEAM) of B cell receptor sequence data had highly significant overlaps with ASPred predictions, suggesting scalability. The predicted SARS-CoV-2 binders differed substantially from training sequences, demonstrating generalization beyond sequence memorization. Together, we establish that heavy chain antibody sequences encode sufficient information for PLMs to infer specificity, offering a scalable framework for antibody discovery with broad applications.

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