Biology subjects

Rodella, C.

Publications and source records attributed to Rodella, C..

3 recordsLinked to original sources

bioRxiv2025

H3BERTa: A CDR-H3 specific language model for antibody repertoire analysis

AbstractAntibodies are central to immune defense and therapeutic design, yet predicting which sequences confer functional activity remains challenging. Deep learning models trained on full variable regions often struggle due to sparse experimental data, signal dilution from conserved framework residues, and the extreme diversity of hypervariable loops. The heavy-chain complementarity-determining region 3 (CDR-H3) is the most variable segment shaping antigen specificity and driving immune diversity. Here, we present H3BERTa, a transformer-based language model trained solely on CDR-H3 sequences, to test whether this short region alone encodes enough biologically meaningful information. H3BERTa embeddings recapitulate biologically relevant sequence features, including J-gene usage and inferred B-cell maturation state. We further show that pseudo-perplexity profiles can be used to analyze repertoires, distinguishing healthy from HIV-1-derived sequences and suggesting measurable immune response signatures. Finally, these embeddings can support classifiers for broadly neutralizing antibodies (bnAbs) using limited labeled sequences, demonstrating their potential for accelerating antibody discovery. Together, our results indicate that the CDR-H3 region alone encodes a rich immunological signature, which H3BERTa robustly captures, providing a focused computational tool for analyzing repertoire diversity and informing antibody engineering.

immunology↗

bioRxiv2025

Deep generative modeling reveals maturation-linked pairing signatures in human antibodies

Understanding how antibody heavy and light chains pair is critical for decoding immune repertoire architecture and designing therapeutic antibodies. However, most antibody sequence databases lack paired chain information. To address this gap, we developed a two-stage deep learning framework. First, we pre-trained separate transformer-based language models on large corpora of unpaired heavy and light chain sequences to capture patterns of gene usage and somatic hypermutation. These models were then integrated via lightweight adapters into a sequence-to-sequence model trained in a machine translation setting, enabling light chain generation conditioned on heavy chain input. Although native light chain recovery was moderate, the model consistently captured functionally meaningful constraints: generated sequences exhibited high germline identity, improved structural quality of predicted folds, and broader coverage of framework and CDR regions. Immunologically, heavy chains from memory B cells preferentially generated light chains with more restricted V gene usage, reflecting maturation-dependent selection. Additionally, generated{kappa} light chains displayed a trimodal similarity distribution, suggesting distinct functional pairing modes ranging from promiscuous to highly specific. This work shows that sequence-to-sequence modeling can uncover inter-chain dependencies and generate structurally and immunologically plausible antibody pairs, providing a foundation for computational repertoire analysis and therapeutic design. HighlightsO_LIA deep generative modeling framework enables conditional generation of light chains from heavy chains, leveraging unpaired data. C_LIO_LIConditioning enhances the structural quality and germline coherence of predicted antibodies. C_LIO_LIMemory B cell-derived heavy chains preferentially generate light chains with restricted V gene usage, consistent with maturation-dependent selection. C_LIO_LIGenerated{kappa} light chains show a trimodal similarity distribution, suggesting discrete pairing modes ranging from promiscuous to highly specific. C_LI

bioinformatics↗

bioRxiv2024

TemBERTure: Advancing protein thermostability prediction with Deep Learning and attention mechanisms

Understanding protein thermostability is essential for various biotechnological and biological applications. However, traditional experimental methods for assessing this property are time-consuming, expensive, and error-prone. Recently, the application of Deep Learning techniques from Natural Language Processing (NLP) was extended to the field of biology, with an emphasis on protein modeling. From a linguistic perspective, the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar. This study explores the potential of Deep Learning models trained on protein sequences to predict protein thermostability which provide improvements with respect to current approaches. We implemented TemBERTure, a Deep Learning framework to classify the thermal class (non-thermophilic or thermophilic) and predict and melting temperature of a protein, based on its primary sequence. Our findings highlight the critical role that data diversity plays on training robust models. Models trained on datasets with a wider range of sequences from various organisms exhibited superior performance compared to those with limited diversity. This emphasizes the need for a comprehensive data curation strategy that ensures a balanced representation of diverse species in the training data, to avoid the risk that the model focuses on recognizing the evolutionary lineage of the sequence rather than the intrinsic thermostability features. In order to gain more nuanced insights into protein thermostability, we propose leveraging attention scores within Deep Learning models to gain more nuanced insights into protein thermostability. We show that analyzing these scores alongside the 3D protein structure could offer a better understanding of the complex interplay between amino acid properties, their positioning, and the surrounding microenvironment, all crucial factors influencing protein thermostability. This work sheds light on the limitations of current protein thermostability prediction methods and introduces new avenues for exploration. By emphasizing data diversity and utilizing refined attention scores, future research can pave the way for more accurate and informative methods for predicting protein thermostability. Availability and ImplementationTemBERTure model and the data are available at https://github.com/ibmm-unibe-ch/TemBERTure

bioinformatics↗