ProteinMPNN
基于消息传递神经网络的蛋白质序列设计方法，从给定的蛋白质骨架结构出发 设计满足该结构的氨基酸序列。该方法在序列恢复率和实验成功率上大幅优于 传统的 Rosetta 设计方法。
| Property | Value |
|---|---|
| Purpose | 基于图神经网络的蛋白质序列设计 |
| Time Complexity | O(n^2 * d) |
| Space Complexity | O(n^2) |
| Year | 2022 |
| Difficulty | Advanced |
| Languages | Python |
| Category | Protein Language Model |
Complexity Analysis
- Time Complexity:
O(n^2 * d) - Space Complexity:
O(n^2)
Performance Insight: The time complexity of this algorithm is polynomial. High space complexity; consider Hirschberg-style space-optimized variants for very long sequences.
Note: Complexity analysis is based on theoretical models. Actual runtime is affected by input scale, hardware, and implementation optimizations. Benchmark for your specific workload.
Literature & Implementation
Related Tools
Rosetta · RFdiffusion · ESM
Tags
protein-design inverse-folding graph-neural-network sequence-design