Inverse folding methods outperform sequence design by hallucination

Summary

Inverse folding methods outperform hallucination at designing proteins that can be refolded in silico (1,2).

Figures

	ESMFold TM	ESMFold pLDDT	OmegaFold TM	OmegaFold pLDDT	AlphaFold2 TM	AlphaFold2 pLDDT	Recovery%
Uniform	0.05	27.68	0.05	31.53	0.06	33.68	5.00
Natural frequencies	0.07	30.53	0.07	35.59	0.06	35.02	5.84
StructTrans	0.72	68.85	0.64	70.35	0.79	80.66	35.89
GVP	0.73	69.67	0.67	74.33	0.83	84.29	39.46
ProteinMPNN	0.80	76.53	0.76	80.75	0.87	87.89	41.44
PiFold	0.71	67.55	0.64	70.21	0.82	82.54	44.86
ByProt	0.73	72.12	0.70	77.58	0.85	87.26	51.23
AF-Design	0.53	61.37	0.53	72.04	0.52	75.29	15.95
ESM-Design	0.38	59.65	0.38	62.66	0.37	60.02	17.33
Wildtype	0.80	74.91	0.75	78.39	0.90	89.87	100
Table from (1)

Ref (2)

1.

Wang C, Zhong B, Zhang Z, Chaudhary N, Misra S, Tang J. PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design. 2023; Available from: https://arxiv.org/abs/2312.00080

2.

Frank C, Khoshouei A, Fuβ L, Schiwietz D, Putz D, Weber L, et al. Scalable protein design using optimization in a relaxed sequence space. Science. 2024;386(6720):439–45. Available from: https://doi.org/10.1126/science.adq1741