The nose is the primary entry point for respiratory infections (hence rhinoviruses). This is because most of the air you breathe enters through here. The nose has a host of defenses, including the physical barrier of mucus (which also contains antibodies and peptidases) and the production of nitric oxide, a potent antimicrobial.

The other major entrypoint is the mouth, which has its own defenses including proteases and antibodies in saliva and the fact you can swallow viruses, yet mouth breathing is a significant risk factor for respiratory infections in children.

Respiratory infections are not treated as seriously as they should be. The flu causes tens of thousands of deaths per year in the US and costs tens or hundreds of billions a year; RSV is the leading cause of infant hospitalizations; long COVID affects millions and globally costs an estimated $1T annually; TB remains one of the world's deadliest infectious agents. Even the "common cold", which is actually infection by any of 200 or so viruses, can cause severe complications like pneumonia.

We are increasingly recognizing the accumulated burden of frequent infections, including connections to Alzheimer's and other neurological diseases, and the profound neurological benefits of vaccination, like the huge relative risks in this 2025 paper from Maggi et al.:

Vaccination against herpes zoster was associated with a reduced risk of any dementia (RR 0.76, 95% CI 0.69–0.83) and Alzheimer’s disease (RR 0.53, 95% CI 0.44–0.64). Influenza vaccination was linked to a reduction in dementia risk (RR 0.87, 95% CI 0.77–0.99), as was pneumococcal vaccination (RR 0.64, 95% CI 0.47–0.87) for Alzheimer’s disease. Tetanus, diphtheria, pertussis (Tdap) vaccination was also associated with a significant reduction for any dementia (RR 0.67, 95% CI 0.54–0.83).

We should not be surprised if today's viral load is damaging. The variety and frequency of infections we are now subjected to is an unnatural state for humans; as a species, we are accustomed to living in small tribes with no international travel.

By coincidence, just this week, Stripe launched the Intercept project, "a $500M philanthropic initiative to make respiratory infections, like the common cold and flu, a thing of the past" so at least some people are starting to take the problem seriously.

Nose sprays

This brings me on to one of my favorite subjects: nose sprays! Preventing infectious disease is one of the easiest way to improve long-term health and longevity. Luckily, nose sprays are a pretty simple, inexpensive, and effective intervention. These sprays primarily act outside of your cells, so the risks compared to e.g., antibiotics, are very low.

There are two major types of nose spray: those that act as a physical barrier to prevent entry of viruses, and the more drug-like antimicrobial or antihistamine sprays. The fact that most of the studies below are COVID-specific is just because of the timing of the pandemic and associated funding.

Physical barrier

• Salinex and LUCA V-Defense are carrageenan-based sprays with evidence for COVID infection prevention. $20 per bottle
• Profi is a gellan/pectin-based spray with evidence for influenza infection prevention in mice. $25 per bottle.
• Xlear is a xylitol-based spray with weak evidence as a COVID treatment. $33 per bottle

Antimicrobial / antihistamine

• NOWONDER is a nitric oxide spray with evidence for treating mild COVID. $40 per bottle.
• ViralDine is a povidone iodine spray with evidence for COVID inactivation. $20 per bottle.
• AstePro is an antihistamine spray with evidence for COVID infection prevention. $18 per bottle

Surprisingly, many of the papers above show pretty good evidence. Like masks, the mechanisms of action here are very straightforward.

My personal preference is for the physical barrier sprays. They act as an additional barrier like sunscreen, and appear to be very safe. For example, carrageenan is a GRAS food additive, sometimes used as a vegan alternative to gelatin.

Arguably, the successor to carrageenan sprays is Profi, which essentially builds on the "augmented mucus" concept. Profi has two main advantages over carrageenan: it provides both a physical barrier and pathogen neutralization, and it lasts a claimed eight hours. In 2024, the two professors at Harvard behind Profi published an intriguing study showing complete protection from Influenza A in a mouse model.

Profi acts as a physical barrier and neutralizes pathogens

I currently use Profi a maximum of once per day, but for more protection I would probably recommend Profi in the morning, and maybe NOWONDER nitric oxide spray before bed.

The alternatives

Good evidence and real papers are the exception in the supplement/wellness space. Maybe the nuttiest example is Oscillococcinum, which is somehow both homeopathic and snake oil, yet still gets sold in supermarkets all over the US and Europe.

Despite its insane ingredients, Oscillococcinum had revenue of $15M/yr in the US in 2008

Zicam, a nose spray you can find everywhere for "cold and allergy relief" appears to be exploiting the homeopathy loophole too. The evidence in its favor is weak, and there are hundreds of lawsuits filed against the company, alleging loss of sense of smell.

Despite weak evidence and potential anosmia, Zicam has revenue of approximately $100M/yr

A viral infection case study

The children sometimes get respiratory infections at school. The last time this happened was a couple of months ago, and I decided to sequence some saliva to see what the infectious agent was.

Many of the likely culprits are RNA viruses, so sadly you can't just do DNA sequencing, you need to do metatranscriptome sequencing.

Zymo has a great service where they will do 30 million paired end reads of metatranscriptomic sequencing for $375 from an unprocessed sample (e.g., saliva).

Zymo is very amenable to small projects, and processed my single sample. I did need Zymo DNA/RNA shield ($74) to stabilize the RNA, but I had some from a previous project. The sequencing took around six weeks, and the results look exceptionally clean.

Metatranscriptomic results

The metatranscriptomic analysis found a normal, healthy distribution of bacteria.

Top bacterial species

Top viral species

The tobamovirus hit is probably from recently eaten food. There is only one human virus in the dataset: human metapneumovirus (HMPV). HMPV is a single-stranded RNA virus with a lipid coat that is one of the most common causes of the common cold. There is no antiviral treatment for HMPV. Like most viruses, the advice is to wait for your immune system to fight it off.

HMPV is usually not serious in older children or adults, but accounts for 5% to 10% of hospitalizations among pediatric patients with acute respiratory tract infections.

Diagram of HMPV from Lianou et al., 2025

Virus sequence

The sequence of the genome is about 3% diverged from the closest reference (FJ168778.1), with 64 missense mutations. It's not that surprising that it all matches a reference sequence so well, but it's still gratifying to see.

The sequence of my HMPV vs a reference sequence

Virus structure

The structure of the HMPV virus is way bigger and more complicated than you would think from the diagram above.

I am used to thinking of viruses as small icosahedra, with a tightly coiled genome inside (see this great article on icosahedral viruses from Asimov Press).

HMPV is pretty different: it's a coiled nucleoprotein, which requires around 1900 "N" proteins to cover the genome, producing a massive structure of hundreds of megadaltons per virion. All of this is squished into a lipid sphere like a ball of yarn.

I used AlphaFold 3 to fold ten nucleoproteins and some RNA from my virus. As you'd expect, given there are good reference structures in PDB, AlphaFold 3 does a fine job folding the nucleoproteins into a coil. In contrast, the RNA has formed a double-stranded hairpin and does not match the crystal structure.

(Left) Ten N proteins from my HMPV in a circular configuration (blue/yellow), with some RNA (orange) wound around. (Right) Eleven N proteins in a spiral configuration (PDB:8PDN)

Viral target

The "F" (Fusion) protein is the obvious target for a therapeutic. It is on the surface of the lipid envelope and mediates cell adhesion and membrane fusion. It has two configurations: pre-fusion and post-fusion. Pre-fusion is the unstable form. When it comes into contact with the host cell, it snaps into the more stable "harpoon" that mediates membrane fusion.

The pre-fusion F protein is compact and the post-fusion F protein is elongated

Luckily, there is a paper (Wen et al., 2012) where the authors created a Fab ("DS7", PDB:4DAG) that binds both the pre-fusion and post-fusion forms. This is the perfect example for us to use as a reference. The sequence of their F protein is 99% similar to ours.

Making a peptide therapy

What if we could design a peptide that binds to the virus and neutralizes it? How hard would that be?

I hear binder design is all the rage these days, so I tried to design a peptide binder. I happened to get some credits for the new BoltzGen API so I decided to try that.

Thanks to the Wen et al. paper, I had a good epitope to go after and a crystal structure of the pre-fusion F protein.

I pointed Claude at the BoltzGen API and asked for a peptide binder of length 20-40, aimed at the DS7 epitope. I spent around $200 on the BoltzGen API, and came up with a length 28 peptide: VKVYDTETPEGYEKWKELARESHGMADV.

The properties of the binder are pretty good, but not ideal.

The ipTM is high; the ipSAE is relatively high, given the size of the peptide; the iLIS is far into the "confident" range (>0.223), implying a low false positive rate.

One potential limitation is that in theory the pre- and post-fusion forms of the F protein have the same epitope, but when I refold with the post-fusion form it does not appear to bind. In practice, we probably only care about binding the pre-fusion form (before adhesion has occurred).

So I can't say it's definitely a binder, but I think it has a reasonably good shot of binding. Usually, if there is a known binder in PDB, making another binder for the same epitope is not so difficult.

How to make the peptide

There are two main ways to make a peptide: with a ribosome or with chemistry (solid phase synthesis). If you use a ribosome (i.e., translation in a cell or cell-free system), then you need to purify the peptide. For short peptides it's generally easier to synthesize chemically. For example, you can order a peptide from GenScript for around $10-25 per amino acid.

The main advantages of using chemical synthesis are (a) purity: specifically, the lack of endotoxins you get with ribosomal production; (b) the ability to go beyond the simple 20 proteinogenic amino acids.

For this peptide, we may want to add an N-terminal Palmitic acid or a similar fatty acid, which should anchor the peptide in the cell membrane, and prevent it getting flushed as mucus refreshes.

This peptide would cost around $600 and take around 20 business days to arrive

Note, I did not test the binder against the F protein! Maybe I'll do it at Adaptyv at some point just for interest's sake.

Safety

One big open question is whether a peptide like this, sprayed into the nose, would be safe. The main reasons I think it probably would be are that (a) it's extracellular; (b) our noses are exposed to tons of peptides all day (e.g., pollen); (c) if the user experienced irritation, they could stop using it—it doesn't persist.

I did a quick review of the literature, and did not find much on the topic.

Conclusion

It's fun to sequence viruses and design peptide binders, but how would a peptide therapeutic like this actually work in practice?

Detection

First we would need a rapid test that could tell us which virus is present. In theory, sequencing would be best. Oxford Nanopore could do it, but it is still a bit impractical, especially since you'd need RNA, and ideally results within an hour or so.

The most practical thing would probably be an ELISA, similar to the rapid COVID-19 tests. Today you can buy a COVID-19 / Flu A/B / RSV test in the US for around $10. Or, if you go on alibaba, you can buy a 10 in 1 test that includes HMPV for $2.

10 in 1 test kit for "cat, dog, human"(!)

Once you have identified HMPV as the virus, then you would spray the peptide in your nose. Would this actually work post-infection? That is very unclear, though even "protective" sprays like carrageenan do appear to reduce the duration of infection. It is much more likely it could prevent others from getting the virus.

My original idea here was to see if it would make sense to sequence and make a personalized peptide per virus. The answer is probably no, because, as we saw, the viruses are usually not that different, and the steps currently take way too long when a virus can run its course within a week or less.

Instead, we could make a cocktail of peptides to address the top ten common cold viruses. Influenza may evolve too quickly to be included in the panel—it depends on whether we can design a binder to a slowly-evolving part of the virus. Arguably this is all overkill when safe, protective nose sprays exist, but we should do it anyway!

A few months ago I launched a VHH binder design mini-competition. The itch I wanted to scratch was to see how well binder design tools do when run without hand-holding by the developers themselves—i.e., when run the way a typical user would.

There are more details in the original blogpost, but the gist was that the competitor submits a script to generate designs, and I run that script on a target.

If we had a "best script" for binder design, kind of like AlphaFold 3 is for folding, it would be hugely enabling for scientists.

I ended up allowing $100 of compute per design, which I thought was just on the edge of possibly producing a binder. It's also approximately the price of testing one design in the lab, which seems like a reasonable benchmark. The consensus from experts I talked to was that this would be insufficient to generate a binder. Turns out they were right! Nevertheless, here is the rundown.

Competitors

I convinced one person to enter this competition: Nick Boyd from Escalante Bio. Nick won the recent Adaptyv Nipah G competition using his own Mosaic protein design library (and it wasn't close!)

As you'd expect, Nick entered using a Mosaic script, similar to his Nipah G script, but adapted to generate a VHH instead of a mini-binder. While Mosaic is well validated for mini-binders, it has not really been tested for VHH designs, which are generally believed to be more difficult.

I entered using a BoltzGen script. My reasoning was that BoltzGen showed very strong results for VHH designs in their preprint, though they certainly used a lot more GPU hours than I did.

BoltzGen has arguably the strongest published VHH design results

Results

I tested the designs against MBP, part of Adaptyv's BenchBB benchmark, which is a set of seven standardized targets designed to be used for benchmarking. If you elect to make the results public, as I did, you get a discount.

I posted the scripts and full results from Apaptyv on the competition github repo. The results should also appear on proteinbase.com in the near future. Of course, there is not much to see here, since none of the designs bound!

EasyMosaic

One complication of Mosaic compared to other tools like BindCraft, BoltzGen, or mBER is that Mosaic is a library, so the user is expected to define their own optimization parameters and loss function. For example, you could define a loss function as a weighted sum of ipTM, pLDDT, and distance to epitope. Different binder design problems might require a different balance of weights. This is a very powerful approach, and allows the user to tune Mosaic for different targets and use-cases, but it can be difficult to know where to start.

Part of the point of this competition was to see if Mosaic could be packaged into a user-friendly script. Since its success in the Nipah G competition, there has been quite a bit of interest in this.

With some advice from Nick on parameters, I made a web-based interface to mosaic called easymosaic. As with most of my stuff, it runs on modal and lets you run Mosaic with some reasonable default parameters for mini-binders or VHHs. The minibinder parameters should match the parameters used by Nick in the Nipah G competition.

Easymosaic is designed to do a decent job producing a binder without the need for parameter tuning. Your mileage will certainly vary a lot based on your target!

Like protein folding tools, easymosaic's interface has almost no options

Mosaic-TUI

Nick's own Mosaic-TUI is a similar idea, but is more suitable for power users. It runs in the terminal, exposes all the relevant parameters, and has some nice features like the ability to use multiple GPUs.

Both easymosaic and Mosaic-TUI use B200 GPUs by default, so it is very easy to spend hundreds of dollars for a few good designs. Each design, before filtering out the bad ones, can cost $1 or more.

Mosaic-TUI has a sweet retro-futuristic UI

Sadly it's a bit too late to use either of these tools to enter the Adaptyv RBX1 competition but I'm sure there will be more competitions coming!

Hopefully, binder design tools will make some advances and I can try this again in a year or so, with a better chance of success. There are still plenty of things to try: combining the strengths of diffusion with hallucination; grounding designs in physics, etc.

This article is a deeper look at Adaptyv's binder design competition, and some thoughts on what we learned. If you are unfamiliar with the competition, there is background information on the Adaptyv blog and my previous article.

The data

Adaptyv did a really nice job of packaging up the data from the competition (both round 1 and round 2). The also did a comprehensive analysis of which metrics predicted successful binding in this blogpost.

The data from round 2 is more comprehensive than round 1 — it even includes Alphafolded structures — so I downloaded the round 2 csv and did some analysis.

Regressions

Unlike the Adaptyv blogpost, which does a deep dive on each metric in turn, I just wanted to see how well I could predict binding affinity (Kd) using the following features provided in the csv: pae_interaction, esm_pll, iptm, plddt, design_models (converted to one-hot), seq_len (inferred from sequence). Three of these metrics (pae_interaction, esm_pll, iptm) were used to determine each entry's rank in the competition's virtual leaderboard, which was used to prioritize entries going into the binding assay.

I also added one more feature, prodigy_kd, which I generated from the PDB files provided using prodigy. Prodigy is an old-ish tool for predicting binding affinity that identifies all the major contacts (polar–polar, charged–charged, etc.) and reports a predicted Kd (prodigy_Kd).

I used the typical regression tools: Random Forest, Kaggle favorite XGBoost, SVR, linear regression, as well as just using the mean Kd as a baseline. There is not a ton of data here for cross-validation, especially if you split by submitter, which I think is fairest. If you do not split by submitter, then you can end up with very similar proteins in different folds.

# get data and script
git clone https://github.com/adaptyvbio/egfr_competition_2
cd egfr_competition_2/results
wget https://gist.githubusercontent.com/hgbrian/1262066e680fc82dcb98e60449899ff9/raw/regress_adaptyv_round_2.py
# run prodigy on all pdbs, munge into a tsv
find structure_predictions -name "*.pdb" | xargs -I{} uv run --with prodigy-prot prodigy {} > prodigy_kds.txt
(echo -e "name\tprodigy_kd"; rg "Read.+\.pdb|25.0˚C" prodigy_kds.txt | sed 's/.*\///' | sed 's/.*25.0˚C:  //' | paste - - | sed 's/\.pdb//') > prodigy_kds.tsv
# run regressions
uv run --with scikit-learn --with polars --with matplotlib --with seaborn --with pyarrow --with xgboost regress_adaptyv_round_2.py

The results are not great! There are a few ways to slice the data (including replicates or not; including similarity_check or not; including non-binders or not). There is a little signal, but I think it's fair to say nothing was strongly predictive.

XGBoost performance looks ok here but is not much more predictive than just taking the mean Kd

Surprisingly, no one feature dominates in terms of predictive power

Virtual leaderboard rank vs competition rank

If there really is no predictive power in these computational metrics, there should be no correlation between rank in the virtual leaderboard and rank in the competition. In fact, there is a weak but significant correlation (Spearman correlation ~= 0.2). However, if you constrain to the top 200 (of 400 total), there is no correlation. My interpretation is that these metrics can discriminate no-hope-of-binding from some-hope-of-binding, but not more than that.

It may be too much to ask one set of metrics to work for antibodies (poor PLL, poor PAE?), de novo binders (poor PLL), and EGF/TNFa-derived binders (natural, so excellent PLL). However, since I include design_models as a covariate, the regression models above can use different strategies for different design types, so at the very least we know there is not a trivial separation that can be made.

BindCraft's scoring heuristics

So how can BindCraft work if it's mostly using these same metrics as heuristics? I asked this on twitter and got an interesting response.

It is possible that PyRosetta's InterfaceAnalyzer is adding a lot of information. However, if this were the case, you might expect Prodigy's Kd prediction to also help, which it does not. It is also possible that by using AlphaFold2, the structures produced by BindCraft are inherently biased towards natural binding modes. Then a part of the binding heuristics are implicit in the weights of the model?

What did we learn?

I learned a couple of things:

• Some tools, specifically BindCraft, can consistently generate decent binders, at least against targets and binding pockets present in its training set (PDB). (The BindCraft paper also shows success with at least one de novo protein not present in the PDB.)
• We do not have a way to predict if a given protein will bind a given target.

I think this is pretty interesting, and a bit counterintuitive. More evidence that we cannot predict binding comes from the Dickinson lab's Prediction Challenges, where the goal is to match the binder to the target. Apparently no approach can (yet).

The Adaptyv blogpost ends by stating that binder design has not been solved yet. This is clearly true. So what comes next?

• We could find computational metrics that work, based on the current sequence and structure data. For example, BindCraft includes "number of unsatisfied hydrogen bonds at the interface" in its heuristics. I am skeptical that we can do a lot better with this approach. For one thing, Adaptyv has already iterated once on its ranking metrics, with negligible improvement in prediction.
• We could get better at Molecular Dynamics, which probably contains some useful information today (at exorbitant computational cost), and could soon be much better with deep learning approaches.
• We could develop an "AlphaFold for Kd prediction". There are certainly attempts at this, e.g., ProAffinity-GNN and the PPB-Affinity dataset to pick two recent examples, but I don't know if anything works that well. The big problem here, as with many biology problems, is a lack of data; PDBbind is not that big (currently ~2800 protein–protein affinities.)

Luckily, progress in this field is bewilderingly fast so I'm sure we'll see a ton of developments in 2025. Kudos to Adaptyv for helping push things forward.