Widatalla, T. | bioRxiv Science

bioRxiv2025

Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN

Leading deep learning-based methods for fixed-backbone protein sequence design do not model protein sidechain conformation during sequence generation despite the large role the three-dimensional arrangement of sidechain atoms play in protein conformation, stability, and overall protein function. Instead, these models implicitly reason about crucial sidechain interactions based on backbone geometry and known amino acid sequence labels. To address this, we present FAMPNN (Full-Atom MPNN), a sequence design method that explicitly models both sequence identity and sidechain conformation for each residue, where the per-token distribution of a residues discrete amino acid identity and its continuous sidechain conformation are learned with a combined categorical cross-entropy and diffusion loss objective. We demonstrate that learning these distributions jointly is a highly synergistic task that both improves sequence recovery while achieving state-of-the-art sidechain packing. Furthermore, benefits from explicit full-atom modeling generalize from sequence recovery to practical protein design applications, such as zero-shot prediction of experimental binding and stability measurements.

bioengineering↗

bioRxiv2024

Aligning protein generative models with experimental fitness via Direct Preference Optimization

Generative models trained on unlabeled protein datasets have demonstrated a remarkable ability to predict some biological functions without any task-specific training data. However, this capability does not extend to all relevant functions and, in many cases, the unsupervised model still underperforms task-specific, supervised baselines. We hypothesize that this is due to a fundamental "alignment gap" in which the rules learned during unsupervised training are not guaranteed to be related to the function of interest. Here, we demonstrate how to provide protein generative models with useful task-specific information without losing the rich, general knowledge learned during pretraining. Using an optimization task called Direct Preference Optimization (DPO), we align a structure-conditioned language model to generate stable protein sequences by encouraging the model to prefer stabilizing over destabilizing variants given a protein backbone structure. Our resulting model, ProteinDPO, is the first structure-conditioned language model preference-optimized to experimental data. ProteinDPO achieves competitive stability prediction and consistently outperforms both unsupervised and finetuned versions of the model. Notably, the aligned model also performs well in domains beyond its training data to enable absolute stability prediction of large proteins and binding affinity prediction of multi-chain complexes, while also enabling single-step stabilization of diverse backbones. These results indicate that ProteinDPO has learned generalizable information from its biophysical alignment data.

biophysics↗