Skip to content
#protein folding Open access

Programmable design of functional proteins from natural language

Sep 2026 · bioRxiv · 31 citations · ⚡ 3 influential · 81 references
Biology

TL;DR

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.

Read PDF

Similar papers

Book Open access Aug 2026

The Words of Proteins: Motif-Level Language Modeling for Interpretable Protein Generation

What are the fundamental units of protein sequences? Most protein language models treat amino acids as tokens, yet biological functions are not encoded at the single-residue level. Instead, they emerge from combinations of residues that form functional units that corresponds to conserved sequence motifs. Just like how...

Boon How Low, Wen Y. Goh, Bo-Yang Li et al. · 0 citations
#protein folding Open access Sep 2026

De novo design of ligand binding proteins using large language models alone

Testing the ability of common large language models to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated desi...

Nam Hyeong Kim, A. K. Hatstat, Hyunil Jo et al. · 0 citations
Open access Sep 2026

RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction

RFO formulates binder improvement as a residue-wise mutational search problem, sampling candidate substitutions alternately based on gradient-guided sequence optimization using all-atom structure prediction models and a cycling-based sequence redesign strategy that alternates structure generation with an orthogonal pre...

Odin Zhang, Jia-Qi Wang, T. Thompson et al. · 0 citations
Open access Aug 2026

Efficient exploration of sequence space enables rapid generation of functional genome editors

An approach to create novel, functional proteins through the integration of deep mutational scanning, structural analysis, and evolutionary mining within prompts for a generative protein language model (PLM) is described and the utility of this approach is demonstrated with the generation of novel compact RNA-guided nu...

Nicholas W. Hughes, Sourab Kulkarni, Grant Goldman et al. · 0 citations
Book Open access Aug 2026

Natural Language-Powered Functional Protein Sequence and Structure Co-Design with Multi-Modal Knowledge Fusion

De novo protein design is pivotal for revolutionizing protein engineering and advancing life sciences. Protein co-design aims to simultaneously create a novel protein sequence and structure with tailored functions, addressing the insufficient consistency between sequence and structure of two-stage design. Current AI-as...

Ming Yang, Xin Zheng, Yi Li et al. · 0 citations
#protein folding Review Open access Aug 2026

Minimal-alphabet proteins by evolution, engineering, and design.

How solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering is discussed.

Filip Buchel, V. G. Giacobelli, K. Hlouchová · 0 citations

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.