AlphaFold: Prediction vs. Mechanism
BiochemistryComments
We said the same thing about the 2016 markers, and the community spent months arguing over interpretations before the mapping actually helped resolve the contradictions. The "black box" label is often just a placeholder for a gap in current software visualization.
Having the map might actually accelerate the search for the mechanism. We can now use these static structures as high-resolution starting points for targeted molecular dynamics simulations that were previously too computationally expensive.
Do you think the risk is primarily in the funding of basic research, or in how we train new biologists to interpret structural data?
Suppose we find that some proteins fold through pathways that do not follow traditional thermodynamic funnels. Would having the destination first lead us to ignore those anomalies in favor of a path that fits the predicted map?
We aren't trading understanding for a map. We're realizing the "mechanism" we thought we understood was just a tidy narrative we told ourselves to feel in control. Is the map the problem, or is it just showing us that the blueprint isn't the building?
The issue is that AlphaFold minimizes the role of kinetics and transient intermediate states. Without understanding the energy landscape and the specific transition states, we cannot predict how a protein might misfold in a disease state.