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arXiv · 2411.18568

Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies

Tianyuan Zheng·Alessandro Rondina·Gos Micklem·Pietro Liò
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Abstract

Deep generative models show promise for $\textit{de novo}$ protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally relevant, and dynamically stable remains challenging. We present a deep generative modeling pipeline for early $\textit{de novo}$ design of monomeric proteins, based on Score Matching and Flow Matching. We apply this pipeline to four diverse protein families with an adaptable evaluation protocol. Generated structures display realistic, clash-free conformations enriched with family-specific features, while the designed sequences preserve essential functional residues while retaining variability. Molecular dynamics and binding simulations show dynamic stability, with wild-type-like binding pockets that interact favorably with family-specific ligands. These results provide practical guidelines for integrating generative models into protein design workflows.

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Tianyuan Zheng, Alessandro Rondina, Gos Micklem, Pietro Liò. 2024-11-27. Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies. https://arxiv.org/abs/2411.18568

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