Biology subjects

Stocco, F.

Publications and source records attributed to Stocco, F..

3 recordsLinked to original sources

bioRxiv2026

Translating chemical reactions into enzyme sequences enables formate dehydrogenase engineering

Enzyme engineering has broad applications across chemistry and biotechnology but remains costly and susceptible to low success rates. Generative artificial intelligence can substantially expand the sequence space accessible to enzyme engineering; however, directing generation towards enzymes that catalyse a user-defined chemical reaction remains challenging. Inspired by machine translation, we formulate enzyme generation as translation between two molecular languages: chemical reactions and amino acid sequences. Here, we introduce REXzyme, a translation model that generates enzymes conditioned on user-defined reactions. First, we evaluate its designs using multiple in silico metrics and characterize the strengths and limitations of its generalisation capacity. Then, we steer REXzyme towards the generation of enhanced formate dehydrogenases for their reverse natural reaction, i.e., the reduction of carbon dioxide. All 20 generated enzymes were expressed and purified as soluble proteins and exhibited CO2-reduction activity, with the most active design reaching 2.5-fold the specific activity of the reference enzyme. Notably, all substitutions in the designed variants were performed on distal positions outside the catalytic site. Our results show that reaction-to-sequence translation provides a route for efficiently engineering enhanced biocatalysts.

biochemistry↗

bioRxiv2026

ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models

Generative protein language models (pLMs) enable exploration of vast sequence spaces for protein design, but reliably controlling generation toward desired functional families remains challenging. While protein generation has broadly followed trends in NLP, two directions remain underexplored: alignment methods that optimize model behavior toward design objectives, and prompting-based control at inference time without fine-tuning. We introduce ProtGPT3, an open-source family of protein language models spanning 112M to 10B parameters and integrated with the Hugging Face ecosystem. The suite includes both single-sequence and multiple sequence alignment (MSA)-promptable models, enabling flexible conditioning for generation. Across model scales and protein families, we systematically compare supervised fine-tuning and few-shot prompting using homologous sequences. Analogous to how large language models (LLMs) are routinely aligned with user intent, we study post-training alignment in single-sequence models using sequence-complexity and structure-confidence metrics across the proteome. We find that alignment reduces low-complexity generations while preserving sequence diversity. Furthermore, we show that few-shot prompting is a competitive and more scalable alternative to supervised fine-tuning for controlled generation. In a low-data defluorinase case study, ProtGPT3-MSA achieved higher computational success rates than fine-tuned baselines and produced designs that were soluble and expressed following experimental validation. Finally, we explore the potential of inference-time compute in MSA models by introducing a homolog-based Feynman-Kac inference procedure for steering protein generation toward desired targets. All models are publicly available at https://huggingface.co/collections/AI4PD/protgpt3-family.

bioengineering↗

bioRxiv2025

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition

In this report, we summarize and analyze the 2024 Adaptyv protein design competition. Participants used computational and Machine Learning (ML) methods of their choice to design proteins that bind the Epidermal Growth Factor Receptor (EGFR), a key drug target involved in cell growth, differentiation, and cancer development. Over 1,800 designs were submitted across two rounds. Of these, 601 proteins were selected and characterized for expression and binding affinity to EGFR, with competitors both optimizing existing binders (KD = 1.21 nM) and creating de novo binders (KD = 82 nM). All selected designs were experimentally validated using Adaptyvs automated Bio-Layer Interferometry (BLI) pipeline. This competition illustrates the potential of crowdsourcing to drive creativity and innovation in protein design. However, it also exposed key challenges, such as the lack of standardized benchmarks, experimental design targets, and robust computational metrics for method comparison. We anticipate that future competitions will address these gaps and further motivate progress in computational protein design.

bioengineering↗