Biology subjects

Barrett, T. D.

Publications and source records attributed to Barrett, T. D..

3 recordsLinked to original sources

bioRxiv2025

AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks

Antibody engineering is marked by diverse data and desiderata, making it a prime candidate for multi-objective design, but is commonly tackled as a series of sequential optimisation tasks. Here, we present AbBFN2, a generative antibody foundation model trained on paired antibody sequences as well as genetic and biophysical metadata. This is achieved using the Bayesian Flow Network paradigm, which allows unified modelling of diverse data sources and flexible conditional generation at inference time. By virtue of its rich set of features and architectural flexibility, AbBFN2 can be adapted to a number of tasks commonly tackled by individual models, consolidating traditional computational pipelines into a single step. We demonstrate the adaptability of AbBFN2 using sequence inpainting, humanisation, biophysical property optimisation, and conditional de novo library generation of antibodies with rare attributes as example tasks. By removing the need for task-specific training, we hope that AbBFN2 will accelerate machine learning-based antibody design and development workflows.

bioinformatics↗

bioRxiv2024

Protein Sequence Modelling with Bayesian Flow Networks

Exploring the vast and largely uncharted territory of amino acid sequences is crucial for understanding complex protein functions and the engineering of novel therapeutic proteins. Whilst generative machine learning has advanced protein sequence modelling, no existing approach is proficient for both unconditional and conditional generation. In this work, we propose that Bayesian Flow Networks (BFNs), a recently introduced framework for generative modelling, can address these challenges. We present ProtBFN, a 650M parameter model trained on protein sequences curated from UniProtKB, which generates natural-like, diverse, structurally coherent, and novel protein sequences, significantly outperforming leading autoregressive and discrete diffusion models. Further, we fine-tune ProtBFN on heavy chains from the Observed Antibody Space (OAS) to obtain an antibody-specific model, AbBFN, which we use to evaluate zero-shot conditional generation capabilities. AbBFN is found to be competitive with, or better than, antibody-specific BERT-style models, when applied to predicting individual framework or complimentary determining regions (CDR).

bioinformatics↗

bioRxiv2022

So ManyFolds, So Little Time: Efficient Protein Structure Prediction With pLMs and MSAs

In recent years, machine learning approaches for de novo protein structure prediction have made significant progress, culminating in AlphaFold which approaches experimental accuracies in certain settings and heralds the possibility of rapid in silico protein modelling and design. However, such applications can be challenging in practice due to the significant compute required for training and inference of such models, and their strong reliance on the evolutionary information contained in multiple sequence alignments (MSAs), which may not be available for certain targets of interest. Here, we first present a streamlined AlphaFold architecture and training pipeline that still provides good performance with significantly reduced computational burden. Aligned with recent approaches such as OmegaFold and ESMFold, our model is initially trained to predict structure from sequences alone by leveraging embeddings from the pretrained ESM-2 protein language model (pLM). We then compare this approach to an equivalent model trained on MSA-profile information only, and find that the latter still provides a performance boost - suggesting that even state-of-the-art pLMs cannot yet easily replace the evolutionary information of homologous sequences. Finally, we train a model that can make predictions from either the combination, or only one, of pLM and MSA inputs. Ultimately, we obtain accuracies in any of these three input modes similar to models trained uniquely in that setting, whilst also demonstrating that these modalities are complimentary, each regularly outperforming the other.

bioinformatics↗