Enzyme design pioneer Steve Mayo: I was trying to capture the fundamental physics of the problem as a way to elucidate mechanisms
Bibliographic record
Abstract
Steve Mayo is a Bren Professor of Biology and Chemistry and Merkin Institute Professor at the California Institute of Technology. He is widely recognized as a pioneer of computational protein design, having achieved the first fully automated design of a novel protein sequence that folds into its target structure1 as well as the first computational design of a biocatalyst capable of transforming a predefined organic substrate2. PEDS recently sat down with Dr Mayo to talk about his background and contributions to the development of computational enzyme design, and thoughts about the future of this field. Mayo: I was a sophomore undergraduate student at Penn State majoring in chemistry. I started working in Roy Olofson’s lab to obtain Honors credits. As part of that, I was synthesizing small molecule drugs and using X-ray crystallography to study these molecules. I became very frustrated with trying to visualize their structures using a program where you had to type in the rotation angles, and then a couple of minutes later, it would print out an image of your molecule. And of course you could never get it right. You had to spend all day trying to get this program to produce a nice image. And so I decided to take a graduate level computer science and graphics course and learn how to use an old school real-time vector graphics machine that Penn State had. As I was learning how to do computer graphics to visualize molecules in real time, I was also taking some biochemistry courses and got really fascinated with proteins. This led me to write my undergraduate thesis on a modeling program to do visualization of molecules, including proteins and nucleic acids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".