The hottest Protein engineering Substack posts right now

And their main takeaways
Category
Top Technology Topics
Lever 19 implied HN points 16 Oct 24
  1. Bruce Wittmann's journey in science started from pre-med and led him to research at notable institutes like Caltech.
  2. He worked on machine learning to improve protein engineering, building tools that can help many people in the field.
  3. His collaboration with renowned scientists and contributions to published research highlight the exciting potential in protein design and computational biology.
LatchBio 12 implied HN points 13 Nov 24
  1. Latch Bio offers a new Protein Engineering Toolkit with over 16 tools that help create and analyze proteins. This means scientists can now design better drugs and enzymes more easily.
  2. The new software called Latch Plots makes it easier for scientists to visualize biological data. It allows them to create dynamic graphs and analyze data from various sources without much hassle.
  3. Using GPU technology in bioinformatics speeds up data processing significantly. This upgrade allows researchers to analyze large datasets quickly, which is essential for drug discovery and many research projects.
Splitting Infinity 39 implied HN points 30 Oct 23
  1. Yeast, especially in precision fermentation, can be genetically modified to produce a wide range of chemicals, biologics, and medicines by augmenting their genes.
  2. The main challenge in precision fermentation is reducing costs, particularly in the purification process where proteins are separated from complex solutions.
  3. Novel techniques like self-cleaving tags and self-aggregating proteins offer promising solutions for purifying proteins in a cost-effective and efficient manner, potentially eliminating the need for expensive purification methods like column chromatography.
The Merge 0 implied HN points 02 May 23
  1. Boosted Prompt Ensembles can enhance large language models' performance for reasoning
  2. Large language models like ChatGPT can excel in relevance ranking for Information Retrieval tasks
  3. Autonomous driving systems can be trained efficiently using deep RL without simulation or expert demonstrations
AI Prospects: Toward Global Goal Convergence 0 implied HN points 31 Mar 24
  1. AI, particularly deep learning, has enabled breakthroughs in protein engineering, paving the way for advanced nanotechnologies.
  2. Transformative nanotechnologies will bring about atomically-precise fabrication, scalable products, high-throughput processing, and wide-ranging applications in various fields like medicine, spaceflight, carbon capture, and computation.
  3. AI is key in driving progress towards transformative nanotechnologies, with physically manifested digital revolutions that will revolutionize how we create things in the material world.
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