jbdamask / wkid-smaaht

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Add RSS RAG based on user interest #44

Open jbdamask opened 11 months ago

jbdamask commented 11 months ago

This is an interesting one as it gets to personalized LLM-agents (ok, not really an agent)

The thought was that people can register their interests (which appear in the prompt here as A-D) and when a paper passes a significance threshold they would be notified.

Prompt:

You are a research associate screening new scientific publications for possible review by a team researching antifungal and antimicrobial peptides for use in plants. You will be given the abstract of a paper and should rate it on several measures, using a scale of 1 to 5. Aspects to rate: A. Novelty of the discoveries or experimental results in the paper. B. Applicability of the paper to discovery of antifungal or antimicrobial peptides for use in plants. C. Applicability of the paper to high-volume expression of peptides using a microbial chassis. D. Significance of the use of machine learning techniques in achieving the paper's results. Give a one-sentence summary of the paper and the ratings in the following format: SUMMARY: [One sentence summary] RATINGS: {A: 3, B:2, C:5, D:1} RATIONALE: [Explain the ratings]

Abstract: Antimicrobial peptides (AMPs) have potent and durable antimicrobial activity to a wide range of fungi and bacteria. The growing problem of drug-resistant pathogenic microorganisms, together with the lack of new effective compounds, has stimulated interest in developing AMPs as anti-infective molecules. PAF102 is an AMP that was rationally designed for improved antifungal properties. This cell penetrating peptide has potent and specific activity against major fungal pathogens. Cecropin A is a natural AMP with strong and fast lytic activity against bacterial and fungal pathogens, including multidrug resistant pathogens. Both peptides, PAF102 and Cecropin A, are alternative antibiotic compounds. However, their exploitation requires fast, cost-efficient production systems. Here, we developed an innovative system to produce AMPs in Pichia pastoris using the oleosin fusion technology. Oleosins are plant-specific proteins with a structural role in lipid droplet formation and stabilization, which are used as carriers for recombinant proteins to lipid droplets in plant-based production systems. This study reports the efficient production of PAF102 in P. pastoris when fused to the rice plant Oleosin 18, whereas no accumulation of Cecropin A was detected. The Ole18-PAF102 fusion protein targets the lipid droplets of the heterologous system where it accumulates to high levels. Interestingly, the production of this fusion protein induces the formation of lipid droplets in yeast cells, which can be additionally enhanced by the coexpression of a diacylglycerol transferase gene that allows a three-fold increase in the production of the fusion protein. Using this high producer strain, PAF102 reaches commercially relevant yields of up to 180 mg/l of yeast culture. Moreover, the accumulation of PAF102 in the yeast lipid droplets facilitates its downstream extraction and recovery by flotation on density gradients, with the recovered PAF102 being biologically active against pathogenic fungi. Our results demonstrate that plant oleosin fusion technology can be transferred to the well-established P. pastoris cell factory to produce the PAF102 antifungal peptide, and potentially other AMPs, for multiple applications in crop protection, food preservation and animal and human therapies.

ChatGPT output:

SUMMARY: The paper presents an innovative system for efficient production of the antifungal peptide PAF102 in Pichia pastoris yeast cells using oleosin fusion technology from plants. RATINGS: {A: 4, B: 4, C: 5, D: 1} RATIONALE:

A: The paper is novel in its application of plant oleosin fusion technology for AMP production in yeast, a different kingdom. B: The focus on antifungal peptides like PAF102 is directly applicable to discovery of antifungal agents for plants. C: The system shows high yield (up to 180 mg/l) and is directly related to high-volume expression of peptides. D: No mention of machine learning techniques in the paper's methodology or results. Confidential Communication This electronic message, including any documents attached to it may contain confidential information and is to be accessed and used only by the intended recipient. If you have received this electronic message in error, please immediately notify the sender and delete the electronic message and all attachments. Any unauthorized disclosure, copying, distribution, or other use of this electronic message, any attachments, or the information contained therein is strictly prohibited, and the sender reserves all rights and claims with respect thereto. Confidential Communication This electronic message, including any documents attached to it may contain confidential information and is to be accessed and used only by the intended recipient. If you have received this electronic message in error, please immediately notify the sender and delete the electronic message and all attachments. Any unauthorized disclosure, copying, distribution, or other use of this electronic message, any attachments, or the information contained therein is strictly prohibited, and the sender reserves all rights and claims with respect thereto.