The hottest Protein engineering Substack posts right now

AI models now let researchers design antibody binders on the computer, greatly reducing the experimental search effort needed to find promising candidates.
There is a practical five-step pipeline — pick a target, prepare or predict its structure, run design tools, filter candidates, and validate in the lab — which uses public tools but typically costs thousands of dollars.
Design success is highly target-dependent and improving affinity, specificity, and drug-like properties remains difficult and costly, but AI makes it realistic to engineer more complex, multi-property binders going forward.

Flowers by Design: engineer new flower traits across many species to make beautiful, bespoke plants and to uncover general principles of plant development that can translate to food crops.
Biosensor for Anything: build a platform of protein or cell-based sensors plus large datasets and predictive models so we can cheaply and reliably detect many molecules and signals in real-world samples.
Proteins for Pennies: develop a fast, low-cost protein fabricator or "printer" to make any protein for pennies, cutting testing costs and enabling cheaper therapeutics and faster AI-driven design.

Bruce Wittmann's journey in science started from pre-med and led him to research at notable institutes like Caltech.
He worked on machine learning to improve protein engineering, building tools that can help many people in the field.
His collaboration with renowned scientists and contributions to published research highlight the exciting potential in protein design and computational biology.

Proteins can be engineered to act as “universal fabricators” that assemble materials with molecular precision, opening the door to new classes of electronic, energy, and structural materials beyond today’s manufacturing methods.
Small, interdisciplinary Frontier Research Contractor (BBN/FRC) teams—combining protein engineers, soft-matter experts, mineralization specialists, and process engineers—are the right organizational form to iterate quickly from sequence to macroscopic, functional assemblies.
Building this vision requires infrastructure partners that scale protein production and rapid metrology, and those supplier FRCs can be commercially viable by serving multiple industries while accelerating the core materials programs.

Yeast, especially in precision fermentation, can be genetically modified to produce a wide range of chemicals, biologics, and medicines by augmenting their genes.
The main challenge in precision fermentation is reducing costs, particularly in the purification process where proteins are separated from complex solutions.
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.

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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.
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.
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.

Boosted Prompt Ensembles can enhance large language models' performance for reasoning
Large language models like ChatGPT can excel in relevance ranking for Information Retrieval tasks
Autonomous driving systems can be trained efficiently using deep RL without simulation or expert demonstrations

AI, particularly deep learning, has enabled breakthroughs in protein engineering, paving the way for advanced nanotechnologies.
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.
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.