Biotech & Pharma·4 min read

Can protein expression be ‘solved’?

Biotech & PharmaArtificial IntelligenceBiology

That’s the title of a great Trends in Biotechnology article led by @the align foundation’s @erika debenedictis. I loved the preprint, I am thrilled to see it published. The Align Foundation is a @schmidt futures and @griffin catalyst-funded research non-profit that is seeking to achieve “predictable biology” by hosting tournaments and hosting and sharing “living datasets backed by automated, open source methods.”

I find this review + position paper extremely useful for framing the problem of “end-to-end protein expression” (my own description of the problem). The phenomenal progress we have seen in the last five years -AlphaFold, AlphaFold2-3, Rosetta Fold, RFDiffusion, ProteinMPNN, ESM and now Boltz-1 and -2, has been focusing on the “end protein”, assuming it already exists in a well-defined, properly folded, soluble state. For recombinant proteins obtained from microbial organisms, a predictive model of soluble protein expression would be a significant advance with major benefits to protein engineering, biomanufacturing or pharmaceuticals. There is expression itself (e.g., promoters, origin of replication, tags ..) and then foldability, stability and solubility of the protein expressed in the microbial host. A predictive model could then be used to optimize the various modifiable elements (e.g., picking a promoter) to maximize soluble expression.

The authors make a great case for why this is an important question and offer a roadmap to the objective of building a predictive model, starting with how to build a robust expression dataset (or datasets, one for each organism).

I would like to suggestion one addition to the model: don’t stop at end-to-end expression and include secretion to your overarching objective. The ability of a microbial organism to not only express but also secrete a protein has a lot of additional applications. The secretion apparatus has modifiable elements (e.g., signal peptide) that could also be optimized if we had a predictive model for secretion. Let’s make it the next step!

Microbiota-driven antitumour immunity mediated by dendritic cell migration: https://www.nature.com/articles/s41586-025-09249-8

It has been known for a decade or so that the gut microbiome influences the success of cancer immunotherapy: if the right bacteria are present (e.g., Ruminococcus spp. and Prevotellaceae spp.), they can boost the immune system and support checkpoint inhibitors, drugs that block proteins called checkpoints. Checkpoints are exploited by cancer cells to evade immune destruction, so blocking them lets the immune system do its job, further supported by molecules or receptors produced by some bacteria.

In this article, a team from the National Cancer Center in Tokyo analyzed faecal samples from non-small cell lung cancer and gastric cancer patients who had been treated with checkpoint inhibitors: they found one particular bacterial strain enriched in “responders”, i.e., patients who did show improvement from treatment. Finding this one strain, of Hominenteromicrobium mulieris, is a colossal endeavor with no guarantee of success, so it is fantastic to see it work -apparently even better than other bacteria that were previously found to support checkpoint inhibitors. They further found, after transplanting the bacteria to mice, that it works by stimulating dendritic cells in the gut, which can migrate to the tumor to activate tumor-specific T cells. Now we have to wait for human trials.

In other words, that is one example where the inflammatory (or immune-stimulatory) potential of gut bacteria has a positive impact. I will come back to this topic soon.

Imidazole propionate is a driver and therapeutic target in atherosclerosis: https://www.nature.com/articles/s41586-025-09263-w

While some bacteria may boost the immune system and drive better immunotherapy outcomes in cancer patients, other bacteria produce molecules that promote inflammation. In fact, the same molecule can be beneficial in one context (stimulating the immune system) and detrimental in another (over-stimulating the immune system). In other words, inflammation is a good thing to fight acute conditions (tumor, infection) but not good if it constant.

This article in Nature by a team led by @david sancho from @Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain reports on one bacterial metabolite, imidazole propionate (ImP) alone is sufficient to induce and aggravate atherosclerosis in a mouse model (Apoe -/- mice). It works by binding a receptor of certain immune cells: by blocking this connection, the authors were able to inhibit the development of atherosclerosis induced by either ImP or a high-cholesterol diet. Receptor activation induces p-S6 and proinflammatory cytokine production, consistent with mTOR activation in macrophages contributing to atherosclerosis: immune cell recruitment to fatty plaques in blood vessels increases plaque size through the build-up of cholesterol and immune cells.

As far as the microbial culprits are concerned, plasma ImP concentration was associated with a relative enrichment of Escherichia and Shigella, or Eubacterium in mice, and the relative abundance of Veillonella and Acidaminococcus in humans. Previous studies of AMP had identified Clostridium bolteae, Clostridium symbiosum, and Ruminococcus gnavus as ImP contributors (Molinaro, A., Bel Lassen, P., Henricsson, M. et al. Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology. Nat Commun 11, 5881 (2020))

Perhaps the most striking observation is that atherosclerosis can be induced by ImP without change in cholesterol: this may explain why lipid-lowering interventions are not sufficient. Given that atherosclerosis is one of the main causes of cardiovascular diseases, that’s an important and promising discovery.

Imidazole propionate is increased in diabetes and associated with dietary patterns and altered microbial ecology : link

Identification of medication–microbiome interactions that affect gut infection: https://www.nature.com/articles/s41586-025-09273-8

Non-antibiotics disrupt colonization resistance against enteropathogens: https://www.nature.com/articles/s41586-025-09217-2