8 Common Mistakes When Using AlphaFold Structures in Research (And How to Avoid Every One)

AlphaFold models the full protein sequence, including regions it knows it cannot predict well. Disordered loops, flexible linkers, and unstructured termini all receive predicted coordinates — but those coordinates are essentially arbitrary placeholders. The pLDDT score below 50 on these residues is AlphaFold’s explicit signal that the predicted position is not reliable.

The mistake: treating these coordinates as meaningful and using them for analysis. Drawing conclusions from the predicted conformation of a low-pLDDT loop. Designing mutations at low-pLDDT residues based on their predicted position. Running docking calculations where the binding site includes low-pLDDT residues. All of these build on coordinates that carry no structural information.

This is the most common AlphaFold mistake in published papers — and the hardest to catch without knowing to look for it. A protein can have excellent pLDDT throughout (every residue individually well-predicted) while the relative orientation of two domains is completely uncertain (high inter-domain PAE). The individual domains are reliably predicted; how they face each other is not.

Researchers who don’t check the PAE plot proceed to analyze the predicted domain interface, design mutations to disrupt it, or run docking calculations targeting a binding site that spans the domain boundary — all based on a domain arrangement that AlphaFold explicitly flagged as uncertain.

pLDDT is a confidence score — not an accuracy measurement. A region with pLDDT 90 is predicted confidently. It is not guaranteed to be within 1 Å of the true experimental structure. AlphaFold can be confidently wrong for proteins whose folds were poorly represented in its training data, for proteins that require cofactors or binding partners to adopt their functional conformation, or for regions where the evolutionary signal is misleading.

The mistake manifests as statements like “the high pLDDT of this region confirms the predicted structure is accurate” — which overstates what pLDDT means. Confidence and accuracy are correlated but not identical.

The output of a docking calculation is only as good as the receptor structure used as input. If the binding site residues have pLDDT below 70, the binding pocket geometry is uncertain — loop conformations are arbitrary, side chain positions are unreliable, and the pocket volume and shape may bear little resemblance to the true binding site. Docking results from such a receptor are not meaningful predictions of binding — they are docking artifacts.

This mistake is particularly insidious because the docking software will generate plausible-looking scores and poses regardless of receptor quality. Nothing in the output signals that the receptor was problematic.

AlphaFold predicts the most thermodynamically stable conformation of the protein — typically the apo (unbound) state, not the ligand-bound, active, or induced-fit conformation. For many drug targets, these conformations differ substantially: kinases switch between DFG-in and DFG-out states, GPCRs open and close their binding cavity, nuclear receptors adopt different helix-12 positions depending on the ligand.

Using an AlphaFold apo model for docking without acknowledging this limitation means potentially docking into a binding site that is closed or collapsed in the apo state — giving misleading results for the biologically relevant bound conformation.

AlphaFold models can contain geometric imperfections — non-ideal bond lengths and angles, Ramachandran outliers, steric clashes — especially in lower-confidence loop regions. These are not caught by looking at pLDDT. A region with pLDDT 75 can have a Ramachandran outlier; a region with pLDDT 85 can have a steric clash introduced during the prediction. These problems affect docking (incorrect binding site geometry), MD (simulation crashes or artifacts), and the credibility of any published structural figure.

AlphaFold-Multimer’s ipTM score measures confidence in the predicted interface geometry — not whether the proteins actually interact in biology. A high ipTM (above 0.75) means AlphaFold is confident about the predicted complex structure, given that the proteins are bound. It says nothing about whether they bind, with what affinity, under what conditions, or in which cellular context.

Conversely, a low ipTM does not prove the proteins don’t interact — they may bind transiently, weakly, or in a conformation AlphaFold couldn’t predict from sequence alone. Both directions of the misinterpretation appear in published papers.

Using an AlphaFold structure without reporting the pLDDT of the relevant regions in the methods section is the structural biology equivalent of using an antibody without citing its validation — it makes the work unreproducible and unverifiable. Reviewers at Nature, Science, PNAS, JACS, and most field-specific journals now routinely ask for pLDDT values, and increasingly for PAE information, when AlphaFold structures are used in docking, MD, or structure-function analysis.

This mistake doesn’t invalidate the science — but it triggers revision requests and signals to reviewers that the quality assessment was not performed rigorously.