Designing a protein with a chosen structure or function from scratch, by computer, so that the first designs tested in the laboratory work as intended.
Scope
In: computational design of new protein binders, enzymes and assemblies that do not exist in nature, and the experimental success rate of the designs. Out: directed evolution of natural proteins, and delivery of the proteins into the body (biotech/in-vivo-gene-delivery).
Readiness
not assessed
Serves
Good health and well-being, Industry, innovation and infrastructure
Experimental hit rate headline1.0 orders of magnitude to go
Share of designed molecules that meet the stated success criterion, such as binding the target below a stated affinity, when tested experimentally. Higher is better.
Current (2025-01-01)10%
Target90%
Limit–
Conditions. De novo protein binders, experimental success rate across the targets tested by the method's developers.
Why this target. The goal of the one-design-one-binder approach named in the BindCraft paper: with a success rate of 90%, testing three designs gives more than a 99.9% chance of at least one working binder (1 minus 0.1 cubed), so no high-throughput screening is needed. Atlas reasoning, not an agency target.
Note. Lower end of a reported range of 10 to 100% across targets. as_of is the publication year. Independent groups report lower rates for other methods (see the gap).
Success rate varies widely between targets and between groups
highscientific unknownlayer: deviceactive
The developers of one pipeline report experimental success rates of 10 to 100% depending on the target. An earlier study found the overall design success rate low and raised it nearly 10-fold with deep-learning filtering. An independent group testing another method on six targets, five designs each, reported that most targets gave no working binder.