The paper investigates a problem in guided protein language models where strong guidance causes the model’s internal representations to collapse onto a region indistinguishable from random amino‑acid input, leading to low‑complexity sequences that still score well on the targeted property. The authors identify this off‑manifold collapse as a detectable signature and propose a post‑hoc filtering technique—Mahalanobis filtering—that removes atypical candidates based on a density prior over natural activations. This simple, training‑free step improves both property scores and structural plausibility across different guidance methods without altering the generator.
By Shuibai Zhang, Xinchi Liu, Fred Zhangzhi Peng, Zhihan Yang, Shutong Wu, Yingzi Ma, Jiawei Zhang
Large protein language models provide a powerful framework for learning sequence-function relationships and guiding experimental design. However, architectural improvements are underexplored, especial...
arXiv:2609.37675v1 Announce Type: new
Abstract: Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and str...
By Biswajit Banerjee, Claudia Alvarez Carreno, Anton S. Petrov
The paper introduces Prompteina, an all‑atom protein structure generation model that accepts natural‑language descriptions of desired structural and functional properties, similar to text‑to‑image systems. It is trained on a large paired dataset of 1.8 million protein structures and over 50 million captions, and fine‑tuned from the Proteina‑Complexa model to align residues with textual cues. Prompteina outperforms existing baselines on most structural metrics, improves binder design performance, and experimentally validates nine new binders for the GM2A target.
By Joseph Lee, Yanjing Li, Bo Qiang, Gregory Lau, Allyson Ettinger, Noah A. Smith, Rory Donovan-Maiye, David Baker, Hanna Hajishirzi, Pang Wei Koh
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
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