Protein discovery and synthesis accelerated by AI

[funkervogt]

Overall, Baker’s team estimates that they have about a 15% success rate with such designs, which is far, far above where things were just two or three years ago. And that rate may have already improved. The bottleneck is making and testing the proteins themselves; these techniques are spitting out so many plausible hits that it’s hard to keep up.

https://www.science.org/content/blog-po ... ign-ai-way

[caltrek]

AI Tool Predicts Function of Unknown Proteins
February 14, 2024

Introduction:

(Eurekalert) A new artificial intelligence (AI) tool that draws logical inferences about the function of unknown proteins promises to help scientists unravel the inner workings of the cell.

Developed by KAUST bioinformatics researcher Maxat Kulmanov and colleagues, the tool outperforms existing analytical methods for forecasting protein functions and is even able to analyze proteins with no clear matches in existing datasets.

The model, termed DeepGO-SE, takes advantage of large language models similar to those used by generative AI tools such as Chat-GPT. It then employs logical entailment to draw meaningful conclusions about molecular functions based on general biological principles about the way proteins work.

It essentially empowers computers to logically process outcomes by constructing models of part of the world — in this case, protein function — and inferring the most plausible scenario based on common sense and reasoning about what should happen in these world models.

“This method has many applications,” says Robert Hoehndorf, head of the KAUST Bio-Ontology Research Group, who supervised this research, “especially when it is necessary to reason over data and hypotheses generated by a neural network or another machine learning model,” he adds.

Read more of the Eurekalert article here: https://www.eurekalert.org/news-releases/1034251

For a technical description of DeepGO-SE as published in Nature : https://www.nature.com/articles/s42256-024-00795-w