🤖 AI Summary
Deep learning in protein design faces two core challenges: inadequate modeling of sequence–structure–function relationships and poor out-of-distribution generalization. To address these, we propose the first architecture-centric taxonomy of protein design models, systematically categorizing methodological evolution—from unimodal structure prediction (e.g., AlphaFold, ESMFold) and generative sequence design (e.g., ProteinMPNN, RFdiffusion) to joint sequence–structure–function co-design (e.g., ESM3). We identify generalizable fitness landscape modeling as the critical pathway to overcoming generalization bottlenecks. By synthesizing advances in attention mechanisms, diffusion modeling, multimodal representation learning, and geometric deep learning, we delineate the capability boundaries of state-of-the-art models and establish joint co-design as the optimal paradigm. This framework provides a systematic methodology for transcending natural evolutionary constraints and enabling rational design of novel functional proteins.
📝 Abstract
Deep learning has transformed protein design, enabling accurate structure prediction, sequence optimization, and de novo protein generation. Advances in single-chain protein structure prediction via AlphaFold2, RoseTTAFold, ESMFold, and others have achieved near-experimental accuracy, inspiring successive work extended to biomolecular complexes via AlphaFold Multimer, RoseTTAFold All-Atom, AlphaFold 3, Chai-1, Boltz-1 and others. Generative models such as ProtGPT2, ProteinMPNN, and RFdiffusion have enabled sequence and backbone design beyond natural evolution-based limitations. More recently, joint sequence-structure co-design models, including ESM3, have integrated both modalities into a unified framework, resulting in improved designability. Despite these advances, challenges still exist pertaining to modeling sequence-structure-function relationships and ensuring robust generalization beyond the regions of protein space spanned by the training data. Future advances will likely focus on joint sequence-structure-function co-design frameworks that are able to model the fitness landscape more effectively than models that treat these modalities independently. Current capabilities, coupled with the dizzying rate of progress, suggest that the field will soon enable rapid, rational design of proteins with tailored structures and functions that transcend the limitations imposed by natural evolution. In this review, we discuss the current capabilities of deep learning methods for protein design, focusing on some of the most revolutionary and capable models with respect to their functionality and the applications that they enable, leading up to the current challenges of the field and the optimal path forward.