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Weekly Reading: Five tasks that still challenge protein designers (Nature 635, 246-248 (2024))

Original article is here ( Five protein-design questions that still challenge AI)

This article brings an insightful snapshot for anyone interested in protein design, expecially non-expert (such as me!) consideing starting their own mini-project in this field. As an enzymologist, one of the striking aspects of the article is the emphasis on protein conformation. We often overlook the fact that proteins and enzymes are not static - they are indeed dynamic molecules. The structures we see are merely snapshots of one moment in time, but they does not necessarily capture the full extent of the protein's motion. 

If protein dynamics are a 4-dimensional problem, perhaps designers are only starting to scratch the surface, seeking to understand this complexity in a simplified form. (I mean how many enzyme people run MD by themselves?) This raises a question: can we truly extract or capture protein dynamics just from sequence information alone? This seems like a huge challenge, expecially when considering how much motion, dynamics, and flexibility contribute to a protein/enzyme's function.

The article also made me relfect on the role of traditional wet-lab scientists in this rapidly evolving field. As AlQuaraishi mentioned, better nad more data will lead to better protein designs. So, will wet-lab scientists only serve as data gatherers for computational models, providing the missing pieces? What is the future of wet-lab haha 

Another aspect the article doesn’t fully address is the (in silico and in vivo) screening process for protein designs. While generative AI tools like AlphaFold are invaluable for predicting protein folding, how do we screen the designs in a way that ensures they will perform as intended? Developing general, high-throughput assays has been remained a significant challenge in the field. Success of directed evolution most of time relies on the successful desiging of screening platform. If we could develop more in-silico methods for screening designs—beyond simply assessing foldability—we might take a major leap forward in the protein engineering process.

Finally, I’m left wondering about the models used in protein design. Most of the algorithms driving these tools seem to be based on large language models (I think—correct me if I’m wrong!). The underlying architecture of these algorithms—how it plays a role in protein design—is still something I can’t fully answer as a wet-lab scientist. But it adds another layer of complexity to the entire design process, making it even more fascinating (and daunting).

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