The paper evaluates several released protein design models on the task of reducing the GRAVY (hydropathy) index of the FDA-approved antibody siltuximab, constrained to edit distance at most 5 from the original sequence. Lambo-2, a classifier-guided discrete diffusion model, achieves the largest shift in the GRAVY distribution among all models tested. WJS and the authors' CB-PLM produce designs with the least disruption to protein naturalness (as measured by TAP scores), whereas ESM2 and a non-deep-learning hydrophilic resample baseline produce the most disrupted designs. The authors note that their general-purpose CB-PLM, trained on over 700 concepts, delivers results comparable to single-concept-optimized state-of-the-art models.
Evidence
correlational
Key metric
Edit distance constraint of 5 from siltuximab; qualitative ranking: Lambo-2 best GRAVY shift, CB-PLM second; WJS and CB-PLM least TAP disruption, ESM2 and hydrophilic resample most TAP disruption
Caveat
Results are for a single protein (siltuximab) and a single property (GRAVY); specific GRAVY values and TAP scores are reported only in figures, not in the text. The comparison involves models trained on different datasets and with different objectives.