ProteinGuide is applied jointly with wet-lab data generation to increase the editing activity of an adenine base editor in vivo, resulting in a base editor with higher editing efficiency than was previously achieved using seven rounds of directed evolution.
This work demonstrates how to provide task-specific information without losing the general knowledge learned during pretraining by using direct preference optimization to align a structure-conditioned protein language model to preferentially generate stable protein sequences.
Talal Widatalla, Ashir Borah, Samuel H. King et al.· Nature Methods· 1 citation
Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineering and drug discovery.Current methods often follow a serial pipeline, in which a structure encoder predicts a coarse sequence, which is then refined by protein language models (PLMs). However, because PLMs only perform post-hoc sequence edits, the refinement is bounded by the quality of upstream predictions.Thanks to recent multimodal protein language models (MPLMs), we could directly encode structure to generate sequences with pretrained structural knowledge, but we observe that they are not effective for inverse folding. Therefore, we introduce a symmetric dual-path architecture that both leverages PLMs for pretrained sequence evolution knowledge and MPLMs for pretrained structural knowledge to iteratively guide protein sequence generation.Through extensive experiments across standard protein inverse folding benchmarks, our method achieves state-of-the-art performance, surpassing prior approaches, and ablation studies validate the rationale of our symmetric design, revealing a promising direction for the community.
Han-Dong Wang, Jiaxin Qi, Baisheng Lai et al.· 0 citations
A cheap density prior is introduced over natural protein activations and keeps only the candidates that remain typical under it, a training-free post-hoc step the authors call Mahalanobis filtering that improves both the property score and the structural plausibility of the sequences it keeps at negligible cost, without touching the generator, and transfers across different guidance methods.
Shuibai Zhang, Xin-Chi Liu, Fred Zhangzhi Peng et al.· 0 citations
Understanding and generation are often treated as two separate paradigms in training deep neural networks, despite the fact that both are trained with closely related objectives such as denoising and masked prediction. While prior studies have shown that generative models often learn suboptimal representations for understanding tasks in vision, it is less understood whether a similar gap exists in the protein domain. In this work, we systematically investigate this question by benchmarking state-of-the-art protein generative models on widely-used protein understanding tasks, and observe that these models exhibit consistently poor performance compared to existing protein encoders. Furthermore, inspired by the Representation Alignment (REPA) framework, we propose to explicitly align generative protein diffusion models with pretrained protein understanding models during training. Experiments on the MotifBench demonstrate that representation alignment significantly improves functional protein generation, boosting the MotifBench score of Protpardelle-1c from 39.2 to 47.1, corresponding to a 20% relative improvement. Our results suggest that representation alignment provides a general and effective mechanism for bridging understanding and generation in protein structure modeling.
Raygun is introduced, a generative artificial intelligence framework that enables miniaturization, modification and augmentation of proteins, using a probabilistic encoding of protein sequences constructed from language model embeddings, enabling the kind of coordinated, large-scale sequence modifications that characterize natural protein evolution.
Kapil Devkota, Daichi Shonai, Joey Mao et al.· Nature· 1 citation
Applications to thioredoxins, visual opsins, and Tara Oceans environmental diatom cold-shock proteins show that PLMView can move from interpretable residue-level determinants in well-studied protein families to large-scale environmental functional discovery, linking molecular specialization to ecological distribution and transcriptional deployment across the global ocean.
