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James Aaron Kraemer

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#gene editing Open access Sep 2026

Evaluation of Alcalase pretreatment for Chlamydomonas reinhardtii CRISPR knock-in

We wanted to use CRISPR-Cas9-mediated homology-directed repair (HDR) in C. reinhardtii to insert genomic landing pads as part of a larger project. In cell-walled algae, delivering Cas9 ribonucleoproteins (RNPs) and donor DNA requires first digesting that wall. The standard approach uses autolysin, a gamete-derived protease that must be prepared in-house over several days and varies in activity between batches. We tested whether Alcalase, a commercially available serine endopeptidase, could serve as an off-the-shelf alternative. We tested Alcalase pretreatment and compared it to GeneArt MAX Efficiency Transformation Reagent for Algae, which facilitates transformation and random integration of donor DNA. We electroporated Cas9 RNPs carrying one of four guide RNAs plus an antibiotic-resistance donor gene into our cells and found that Alcalase digested cell walls, but reduced viability independent of electroporation. Further, surviving cells were less likely to produce nourseothricin (NAT)-resistant colonies. Amplicon sequencing of NAT-resistant colonies detected no knock-in events or target-site indels with either method, indicating that on-target editing efficiency was low and NAT resistance arose primarily from off-target genomic integration of donor DNA. We stopped pursuing this work after bigger-picture priorities shifted, but are sharing our initial findings in case this is useful to others considering this approach. One attempt under one set of conditions isn't enough to rule out Alcalase pretreatment for targeted gene integration in Chlamydomonas, and we think this could be worth pursuing using gentler conditions.

Daniel Caddell, Raymond Futia, James Aaron Kraemer · Arcadia Science · 0 citations

#protein folding Open access Sep 2026

Joint steering of protein generation across multiple target properties

Ideally, protein design could optimize several properties at once while ensuring the protein satisfies basic biophysical constraints such as folding, stability, solubility, and expression. However, most guided generation methods optimize only one or two simple objectives. We developed a workflow that uses predictive models of several desired properties to steer sequence generation. Using fluorescent proteins as a test case, we show computationally that guiding generation toward target excitation and emission peaks produces designs closer to those targets than unguided generation with subsequent filtering, particularly when starting from parent proteins the guiding model was never trained on. We also find that a family-specific sequence profile, a probability distribution derived from multiple sequence alignments, provides a more useful generative prior for fluorescent proteins than ESM-2. Out of the ten designs we tested experimentally, one came back as a working fluorescent protein, but didn't fluoresce near our target wavelength. We're sharing this work for researchers using generative protein models who want to incorporate multiple, potentially high-dimensional experimental measurements directly into design. We discuss generalizable lessons on protein search space and predictor-based guidance that we hope will inform similar projects.

Prachee Avasthi, Josie Bircher, James Aaron Kraemer et al. · Arcadia Science · 0 citations