Filippo Caschera

Pleased to share my latest review on Cell-Free Protein Synthesis #CFPS and #AI/ML for accelerating drug discovery sciencedirect.com/science/articl… #CellFree #SynBio

@geochurch I wonder how scalable this approach is for PK. Does it truly overcome a key bottleneck in drug discovery?

Purification of post-transcriptionally modified tRNAs for enhanced cell-free translation systems academic.oup.com/nar/article/54…

CF2H: a cell-free two-hybrid platform for rapid protein binder screening nature.com/articles/s4146…

Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression. By Olsen, M.L., Copeland, C.E., Sundberg, C.A. et al. doi.org/10.1038/s41467…

We evolved GPCR entirely in vitro. The evolved GPCR was shown to exhibit improved function in vivo. Many thanks to all collaborators. We have another one coming up soon. doi.org/10.1021/jacs.6…

The paper from Matsuura laboratory in ELSI @tomo_matsuuraG. I assited with ribosome display part. In Vitro Evolution of the Adenosine A2A Receptor Based on an Antagonist Binding Using a Ribosome Display | Journal of the American Chemical Society pubs.acs.org/doi/full/10.10….

Experimental verification of the error minimization theory using non-standard genetic codes constructed in vitro biorxiv.org/content/10.648…

CADGE 2.0, Transcription-Translation-Coupled DNA Replication is Improved in a Chemically Modified Cell-Free System biorxiv.org/content/10.648… #biorxiv_synbio

In vitro selection of VNAR antibodies using bioinformatics-based library design and cDNA display papers.ssrn.com/sol3/papers.cf…

In @elife: Single-step in vitro ribosome reconstitution mediated by two GTPase factors, EngA and ObgE doi.org/10.7554/eLife.…

Combinatorial optimization of protein systems in synthetic cells biorxiv.org/content/10.648… #biorxiv_synbio

Nucleotide-level chemical reaction network modeling enables quantitative prediction of reconstituted cell-free expression system biorxiv.org/content/10.648…

We’ve released two related preprints! 1.biorxiv.org/content/10.648… 2.biorxiv.org/content/10.648… Together, these studies establish a rapid, cell-free directed evolution workflow directly from linear DNA.

Boosted cell-free gene expression for robust signal readout from a single-copy DNA template in microdroplets biorxiv.org/content/10.648… #biorxiv_synbio

Expressing 10,000 proteins — how long and how much does it really take? I ran the numbers comparing CFPS vs E. coli vs HEK (based on recent refs). 🧪 Time to expression (no purification) • CFPS → ~3.5 days (84 h) • HEK cells → ~81 days • E. coli → ~11–12 months CFPS cycle assumed 12 h, automated workflow, ~0.75 µg/µL yield. ⚡ Speed advantage: CFPS is ~23× faster than HEK CFPS is ~100×+ faster than E. coli 💰 DIY CFPS reagent cost (10,000 proteins) • Core reagents ≈ $258 • SDS-PAGE QC ≈ $8,160 • Misc consumables ≈ $8–15k total And cost drops further if QC shifts to fluorescent readouts instead of gels. DNA cost not included. No downstream purification included. Expression only. CFPS is removing constraints. linkedin.com/posts/filippo-…

Running a quick poll on CFPS benchmarks: if you had to produce 10,000 proteins, what’s your realistic time-to-results (with parallelisation) and target yield? Vote here 👉 linkedin.com/posts/filippo-…

Another interesting paper from @arcinstitute. This one combines protein language models with directed evolution to rapidly engineer proteins. Here is how it works, step-by-step: 1. Select the protein you want to engineer. Give the protein sequence to four different protein language models which, together, "score" the likely fitness of each amino acid mutation. Get a ranked list of 50-100 protein mutations that these models *predict* might improve function. (This is all done on the computer.) 2. Take the top 15 predicted "hits" and then synthesize the pairwise combination for each of them. For example, if a mutation at amino acid #100 boosted activity, but so did mutations at positions #120, #135, and so on, then you'd make a protein with each of these "double" mutations. If you took the top 15 hits, then this is only 105 total proteins to synthesize. 3. Make and test all the double mutants in the wet-lab. Measure the activity of each double mutant. So, for example, if you wanted to engineer a protein to be "brighter," then you would put each double mutant in a microplate well and measure this directly. (This step basically captures epistatic relationships; it helps the models figure out which mutations are beneficial or damaging.) Feed the single + double mutant activity data to a neural network, called MULTI-evolve. The model extrapolates these data to infer *additional* mutations that might be synergistic, like combinations of 5-7 amino acid swaps. 4. Take the top three predictions for proteins with 5-, 6-, or 7 amino acid mutations, based on the neural network. Synthesize these proteins using a new DNA assembly approach, also reported in this paper, called MULTI-assembly. (The gist is that you anneal together a bunch of short oligos, each carrying one of the mutations, in a tube to reconstruct each of the full genes. This yields correctly built sequences 40-70% of the time.) 5. Finally, express the proteins in cells, measure their activities, and benchmark them against the wild-type protein. The researchers used this method for various proteins. For one protein, called dCasRx (a Crispr protein that targets RNA instead of DNA), they used it to create a variant with 9.8-fold better activity, and they validated this across three different human genes. You can also optimize proteins for two different properties at the same time. The authors used their method, for example, to engineer an antibody targeting CD122 for both binding affinity AND its expression yield in cells. TL;DR This is a new way to speed up directed evolution. Instead of using random mutations to search through a huge amount of "biological space" (remember that a single protein, with just 100 amino acids, has 20^100 possible combinations), these researchers use AI models to navigate this search space for proteins more quickly. Can we use the same approach to make entire gene circuits?

AI-driven autonomous robots are coming to biology laboratories, but researchers insist that human skills remain essential go.nature.com/3MCzePt

Accessible biocatalyst development by rapid in vitro semi-rational engineering (RISE) of enzymes: iScience cell.com/iscience/fullt…

PL-display: A cell-free platform for tunable selection of affinity peptides doi.org/10.1093/pnasne…