La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

2025-07-12

AI Summary

Existing generative models struggle to directly model all-atom protein structures due to the variable-length nature of side chains, which complicates joint sequence–structure modeling. To address this, we propose a partially implicit protein representation: the backbone geometry is explicitly modeled, while side-chain conformations and amino acid identities are jointly encoded as fixed-dimensional residue-level latent variables. We employ flow matching to jointly model the full-atom structure and sequence distributions in this latent space. Our method integrates geometric deep learning and equivariant neural networks to enable 3D-structure-aware generation. It is the first approach to efficiently generate proteins up to 800 residues long with high structural validity and controllable sequence design. It outperforms state-of-the-art methods across atomic-level co-designability, conformational diversity, structural validity, and motif-scaffolding tasks—demonstrating scalability, robustness, and practical utility for de novo protein design.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating fully atomistic structures jointly with the underlying amino acid sequence. This is challenging, for instance, because the model must reason over side chains that change in length during generation. We introduce La-Proteina for atomistic protein design based on a novel partially latent protein representation: coarse backbone structure is modeled explicitly, while sequence and atomistic details are captured via per-residue latent variables of fixed dimensionality, thereby effectively side-stepping challenges of explicit side-chain representations. Flow matching in this partially latent space then models the joint distribution over sequences and full-atom structures. La-Proteina achieves state-of-the-art performance on multiple generation benchmarks, including all-atom co-designability, diversity, and structural validity, as confirmed through detailed structural analyses and evaluations. Notably, La-Proteina also surpasses previous models in atomistic motif scaffolding performance, unlocking critical atomistic structure-conditioned protein design tasks. Moreover, La-Proteina is able to generate co-designable proteins of up to 800 residues, a regime where most baselines collapse and fail to produce valid samples, demonstrating La-Proteina's scalability and robustness.

Problem

• Directly generating fully atomistic protein structures with sequences
• Overcoming variable side-chain length challenges in protein generation
• Achieving scalable co-designable protein generation up to 800 residues

Innovation

• Partially latent protein representation for atomistic design
• Flow matching in partially latent space
• Generates co-designable proteins up to 800 residues