The paper investigates how guided protein language models can collapse onto off‑manifold representations when heavily steered to optimize a property. This collapse causes generated sequences to become low‑complexity and statistically similar to random amino‑acid input, yet the property oracle may still rate them highly. The authors propose a cheap, training‑free Mahalanobis filtering step that removes such off‑manifold candidates, improving both property scores and structural plausibility without altering the generator.
arXiv:2606. 18961v1 Announce Type: new Abstract: Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets.
By Lanqing Li, Shentong Mo, Yang Yu, Pheng-Ann Heng
Large protein language models provide a powerful framework for learning sequence-function relationships and guiding experimental design. However, architectural improvements are underexplored, especial...
arXiv:2606. 08100v1 Announce Type: new Abstract: Multimodal $\Delta\Delta G$ predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations.
By A Shivram, Aneesh S. Chivukula, Manik Gupta, Sourav Chowdhury
ProtModernBERT is a modernized BERT-style protein language model that reduces training cost by incorporating architectural refinements, achieving performance comparable to larger models while cutting pre‑training FLOPs by 80‑90%. The authors benchmark ProtModernBERT against ProtBert, Amplify, and ESM2 on supervised structural tasks and a zero‑shot functional benchmark, showing it matches or closely tracks Amplify‑120M and ProtBert. Their cluster‑based validation and random‑init experiments reveal that modern architectural components provide stronger inductive biases than parameter count in low‑compute regimes.
By Alberto Mart\'inez, Natalia Flechas Manrique, Lloren\c{c} Espinosa-Portal\'es, Luc Andrea
SymFold introduces a symmetric dual‑path architecture that combines protein language models (PLMs) and multimodal protein language models (MPLMs) to iteratively guide protein sequence generation for inverse folding. By leveraging pretrained sequence evolution knowledge from PLMs and structural knowledge from MPLMs, the method improves upon the traditional serial pipeline where structure encoders produce coarse sequences refined by PLMs. Experiments on standard inverse‑folding benchmarks show state‑of‑the‑art performance, and ablation studies confirm the effectiveness of the symmetric design.
By Handong Wang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang