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AI-designed synthetic protein assemblies enable gene therapy delivery systems far superior to natural capsids, as reported in a Nature paper.
The paper presents TourSynbio-Search, an LLM-driven agent framework for unified protein engineering search across literature and biological databases, powered by the TourSynbio-7B multimodal model with dual PaperSearch and ProteinSearch components.
Introduces AutoProteinEngine (AutoPE), an LLM-driven agent framework that enables biologists without deep learning expertise to perform multimodal AutoML for protein engineering via natural language, showing improvements over zero-shot and manual fine-tuning approaches.
Researchers at Tsinghua University discovered that serine and aromatic residues promote biomolecular condensate mixing while charged amino acids drive separation, enabling prediction and engineering of condensate miscibility.
This Nature research paper presents an AI method that redesigns starting points and outcomes to enhance protein evolution.
Researchers used AlphaFold AI to identify and redesign parts of gene-editing proteins (Cas9) responsible for off-target effects, significantly improving safety by reducing unintended edits.
AbICL proposes an in-context learning framework for antigen-specific antibody affinity ranking, combining a pretrained structural encoder with a context ranking head to leverage labeled demonstrations for test-time adaptation without gradient updates.
This paper investigates whether tabular in-context learning models, pretrained on synthetic causal tables, can generalize to predict biomolecular properties from limited labeled data. The authors find that these models are competitive for protein fitness regression but that representation choice is crucial for small-molecule classification.
Arc Institute announces Germinal, a generative AI system for de novo antibody design published in Nature Biotechnology. It designs epitope-targeted antibodies with nanomolar affinity testing only tens of designs per target, making custom antibody design more accessible.
Pepti-Agent is a closed-loop AI framework for therapeutic peptide design that uses MCP tools and an LLM controller to iteratively refine sequences based on multi-property profiles, addressing constraints like solubility, hemolysis, and non-fouling.
Researchers at Michigan State University used AlphaFold to predict enzyme structures and engineer heat-resistant crops by stabilizing photosynthesis enzymes.
OpenAI collaborated with Retro Biosciences to develop GPT-4b micro, a specialized version of GPT-4o for protein engineering, achieving 50-fold higher expression of stem cell reprogramming markers with enhanced DNA damage repair capabilities. The findings have been validated across multiple donors and cell types, demonstrating AI's potential to accelerate life sciences research.