Large-Scale Protein Modeling Platforms

Abstract

Protein modelling has been transformed by deep learning: AlphaFold2 predicts monomeric protein structures with experimental accuracy, ESM-2 generates rich sequence representations from 250 million proteins, and RFdiffusion designs novel protein structures from scratch. Yet deploying these capabilities at the scale required for proteome-wide analysis, drug discovery pipelines, and synthetic biology workflows demands computational platforms that orchestrate model inference, manage protein databases, and integrate predictions across tasks. We present the Protein Modeling Platform Assessment Framework (PMPAF), evaluating five platform architectures -- structure prediction servers, protein language model APIs, multi-task protein foundation platforms, cloud-native scalable inference engines, and integrated design-predict-validate workflows -- across four large-scale protein modelling tasks (proteome-wide structure prediction, variant effect screening, protein-protein interaction mapping, and de novo protein design pipelines). Our Platform Performance Score (PPS) measures prediction throughput, accuracy across tasks, infrastructure scalability, user accessibility, and workflow integration capability. Cloud-native inference engines achieve the highest PPS (0.928) through auto-scaling GPU clusters that process 100,000+ proteins per day, while integrated design-predict-validate workflows achieve the highest end-to-end utility (0.960) by connecting computational prediction to experimental validation in a closed-loop platform.