Integrating Protein Language Models and All-Atom Generative Design for Predicting Mutation-Induced Stability Changes
Keywords:
protein language models; all-atom generative design; mutation-induced stability changes; protein engineering; AI governance; robust deployment; structural bioinformatics; socio-technical systemsAbstract
Predicting mutation-induced stability changes is a foundational challenge for protein engineering, therapeutic development, biomanufacturing, and synthetic biology. Recent advances in protein language models have made it possible to infer evolutionary and functional constraints directly from sequence data, while all-atom generative design has begun to model molecular geometry, side-chain packing, and physicochemical interactions with increasing fidelity. This paper examines how these two research programs can be integrated into a system-level architecture for predicting mutation-induced stability changes. Rather than treating sequence-based prediction and structure-based generation as competing methods, the paper argues that they are complementary layers of a broader socio-technical infrastructure that must address data curation, model interoperability, uncertainty communication, deployment constraints, sustainability, robustness, and governance. The analysis emphasizes architectural trade-offs between broad sequence pretraining and high-resolution all-atom modeling, between zero-shot generalization and experimental calibration, and between centralized foundation-model services and distributed domain-specific fine-tuning. It also considers the policy implications of systems capable of proposing or evaluating mutations that could alter protein stability, function, or host range. The paper concludes that progress will depend not only on improved predictive accuracy but also on auditable data pipelines, reproducible evaluation regimes, equitable access, environmental accountability, and responsible translational pathways. A robust integrated system must therefore be understood as an institutional and infrastructural achievement as much as a computational one.
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