Generative design of intrinsically disordered proteins based on conditioned protein language models: Data is the limit

Abstract

Intrinsically disordered proteins and regions (IDRs) are central to a multitude of biological processes. Despite extensive studies of their structural and physicochemical properties, the rational design of IDRs with defined conformational behavior remains challenging due to their ensemble nature. Here we present a generative framework for designing disordered protein sequences conditioned on target conformational ensemble descriptors using protein language models (pLMs). We formulate IDR design as the task of generating amino acid sequences predicted to realize specified biophysical properties and implement a Transformer encoder-decoder architecture that maps numerical descriptors to protein sequences. By training models on datasets spanning two orders of magnitude in size, we show that accurate control of conformational and physicochemical properties is achieved only at large data scale. These results demonstrate the feasibility of conditioning generative models on ensemble-level descriptors for IDR design. More broadly, these results support a data-centric paradigm for protein engineering, in which data availability emerges as a key limiting factor for the accurate design of IDRs.