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

TL;DR

This paper presents a deep generative modeling pipeline for de novo protein design, demonstrating its ability to produce realistic, stable, and functionally relevant protein structures across four diverse families, with practical guidelines for workflow integration.

Contribution

The paper introduces a novel deep generative pipeline based on Score Matching and Flow Matching for early de novo protein design, validated through four case studies.

Findings

Generated structures are realistic and clash-free.

Designed sequences retain functional residues and variability.

Simulations confirm dynamic stability and ligand interactions.

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.