AlphaFold Predictions Can Be Powerful - Or Deeply Misleading: Eight Critical Traps That Undermine Mutant and Complex Modeling, and How Experienced Bioinformaticians Avoid Them

Introduction

AlphaFold 2 and its variants - especially AlphaFold-Multimer and AlphaFold 4 - have changed how we look at protein structure modeling. It becomes very easy now to get a PDB file, even for large protein complexes or challenging targets. But that does not mean it solves all the real scientific problems. In our actual project experience, many researchers run AlphaFold, get high-looking pLDDT scores, and then find their biological interpretation is still uncertain - or sometimes, completely wrong.

We have helped teams working on receptor-ligand models, antibody-epitope prediction, variant prioritization, multimer complex design, and stability engineering. Often, AlphaFold alone was not enough. Additional steps using FoldX, Rosetta, AutoDock, or molecular dynamics (MD) were required to validate the structure, estimate binding energy, or assess ΔΔG from mutation.

This article is not a guide to running ColabFold. Instead, we summarize eight technical and conceptual traps we see repeatedly in real AlphaFold-based projects - and how experienced bioinformaticians solve them. Each section gives a common failure mode, explains why it happens, and shares what we do differently, with examples based on project work (anonymized when needed). We believe that learning these lessons can make AlphaFold work much more useful, and avoid misleading or wasted effort.

Table of Contents

1. Misinterpreting Confidence Metrics Like pLDDT, PAE, and ipTM

The Problem

Many researchers look at pLDDT scores >90 and assume the model is perfect. Or they see a good ipTM score and conclude the interaction is strong. But this confidence is not always justified.

Why It Happens

AlphaFold's pLDDT reflects local per-residue confidence, and PAE gives relative positional error. But pLDDT can be high even when domain orientation is wrong. ipTM measures interface confidence, but only under the assumption that the chains are meant to interact. These numbers are statistical outputs from the network - they do not replace biological sense.

Real Example

In one homodimer case, the model had ipTM >0.85 and low PAE for the interface. But experimental pull-down showed no dimerization. Further analysis found the model forced an interface due to close N/C-terminal regions, but in vivo they were flexible and not binding. No co-evolutionary signal supported the dimer.

What We Do Differently

We interpret confidence metrics together with biological annotation. We cross-reference predicted interfaces with prior literature, interaction databases (e.g., BioGRID), and co-evolution signals. For low PAE but biologically implausible contacts, we flag the issue. We also visualize attention maps (if available) and use disorder prediction to evaluate domain flexibility. AlphaFold gives predictions - we apply judgment.

2. Poor Setup of Multimers and Complexes Leads to Artificial Interfaces

The Problem

Researchers want to model protein-protein interactions, so they feed sequences to AlphaFold-Multimer. But often the input setup is wrong - either the chains are in wrong order, stoichiometry is misdefined, or linkers are added inappropriately.

Why It Happens

AlphaFold-Multimer is sensitive to sequence arrangement. Chains are predicted jointly, and artificial fusion of sequences (especially with linkers like GGGGS) can confuse the model. Also, chain naming and number matters - (A+B+C) is not the same as (A+A+B). The tool makes assumptions if not explicitly told otherwise.

Real Example

In a vaccine modeling project, a fusion construct was modeled as a heterotrimer. The model showed tight binding, but it was later discovered that the linker sequence was interpreted as an interface, and buried deep inside the core. Removing the linker and modeling as separate chains gave a totally different orientation.

What We Do Differently

We carefully define stoichiometry and run multiple permutations when modeling complexes. For engineered fusions, we model both the fused and the separated forms, and compare. When modeling true biological multimers, we check whether symmetry or prior crystal structures suggest known arrangements. We also look at inter-chain contacts and compare with AlphaFold2_single to validate folding consistency. No complex model is taken as true just because it looks compact.

3. Predicting Point Mutants Without Evaluating ΔΔG or Stability

The Problem

Many teams predict a mutant structure using AlphaFold (by changing the sequence), then directly use that model for downstream interpretation. But AlphaFold is not designed to predict folding stability or mutation effect.

Why It Happens

AlphaFold is trained to return one most-likely conformation. It may ignore energetic penalties or small shifts due to mutation. Also, pLDDT may stay high even if the mutation destabilizes the fold. This leads to false confidence in the mutant model.

Real Example

A team studying channelopathies modeled 12 point mutants of a transmembrane protein. All showed high pLDDT and similar structure. But patch-clamp data showed several had major loss of function. We ran FoldX ΔΔG calculations and found three of them had >3 kcal/mol destabilization, which AlphaFold didn’t indicate.

What We Do Differently

We never rely on AlphaFold alone for mutant stability. After predicting the structure, we run FoldX or Rosetta ddG protocols to compute ΔΔG. We compare mutant vs wild-type energies, check buried vs surface location, and integrate with conservation and solvent exposure data. For flexible regions, we add disorder prediction to decide whether mutation affects dynamics. Only then we suggest whether the mutation is structurally significant.

4. Modeling Ligand Binding Without Energy or Shape Validation

The Problem

A predicted structure is used to dock a ligand - but without checking pocket compatibility or validating the binding pose. The result looks good in PyMOL but fails experimentally.

Why It Happens

AlphaFold does not see ligands during training. So pockets may not be fully accurate, side chains may clash, and local flexibility is not modeled. Docking tools like AutoDock or SwissDock are needed, but are often used blindly on AlphaFold outputs.

Real Example

In one kinase-inhibitor case, docking placed the small molecule into a hydrophobic pocket. But the model lacked Mg²⁺ ion and the side chains were distorted. AutoDock scoring was high, but mutagenesis failed to confirm binding. When we refined the pocket using RosettaRelax and added explicit ion coordination, the correct pose was recovered.

What We Do Differently

We clean AlphaFold models before docking - remove loops with poor pLDDT, optimize side chain rotamers, and identify flexible regions. For metal-coordinated or cofactor-containing pockets, we model them explicitly. Docking is done with constraints when known (e.g., from homologs), and multiple poses are compared. We also cross-check with FTMap or MD-based pocket detection to assess likelihood of real binding. A docking pose without biophysical filtering is not convincing.

5. Blind Trust in High pLDDT Scores for Flexible or Disordered Regions

The Problem

Some models show high pLDDT in loops or linker regions - and users interpret them as rigid. But these regions are actually dynamic or unstructured in vivo.

Why It Happens

AlphaFold sometimes overconfidently predicts structured turns or helices in intrinsically disordered regions, especially when sequence is hydrophobic or repeats. The model "hallucinates" structure because the training data was biased toward folded domains.

Real Example

A transcription factor was modeled with a long linker between two domains. AlphaFold gave a tight helix with pLDDT ~85. But NMR data showed it was disordered and flexible. This misinterpretation led to wrong hypotheses about allostery.

What We Do Differently

We always compare AlphaFold models with disorder predictors like IUPred or MetaDisorder. If the region is predicted disordered, we downweight AlphaFold confidence and mark the structure as provisional. For multimer modeling, we also simulate flexibility using MDAnalysis or ensemble sampling. AlphaFold gives one frame - we often need more than that.

6. Misannotation of Chain Identity or Stoichiometry in Multimers

The Problem

Models of heterodimers or higher-order complexes sometimes mismatch chain identity or assume wrong stoichiometry, leading to incorrect interface interpretation.

Why It Happens

The user inputs sequences A+B+C, expecting A-B-C trimer. But AlphaFold may assume A₂B, or swap order due to homology or length. The output PDB chain IDs may also be scrambled.

Real Example

In a virus capsid modeling task, AlphaFold predicted three-chain interaction. But the stoichiometry was misinterpreted, and chain A was actually interacting with itself. The downstream mutagenesis targeted wrong interface. We re-ran modeling with symmetry constraints and corrected chain definitions.

What We Do Differently

We explicitly define chains, stoichiometry, and symmetry when modeling multimers. We post-process output using PyMOL scripts to assign correct chain IDs. We also run pairwise AlphaFold-Multimer models (A+B, A+C, etc.) to compare interface stability. When possible, we validate with known cryo-EM maps or symmetry models. Getting the stoichiometry wrong can derail the whole structural interpretation.

7. Folding Artifacts Due to Isoform Errors, Engineered Tags, or Truncated Sequences

The Problem

The input sequence is incorrect - wrong isoform, extra His-tags, or internal truncations - and AlphaFold predicts a structure that never occurs biologically.

Why It Happens

AlphaFold assumes the input sequence is biologically meaningful. But transcript variants, cloning artifacts, or fusion constructs can confuse it. Tag sequences may form artificial helices, and internal truncation can break folding domains.

Real Example

A team submitted a 385-aa protein to AlphaFold, including a thrombin-cleavable tag and TEV site. The model folded it into a globular domain. But the natural isoform was 360-aa, and the additional 25-aa caused a misfolding loop over the functional surface.

What We Do Differently

We verify sequence before modeling - match to UniProt canonical or tissue-specific isoforms. For constructs, we remove tags unless they are known to affect folding. When modeling truncations, we assess whether the truncation cuts within a known Pfam domain or breaks secondary structure. We also run wild-type models for comparison. AlphaFold doesn’t know what parts are experimental artifacts - we do.

8. Failure to Integrate AlphaFold Results with Functional Data and Experimental Design

The Problem

Even when the model looks good, teams fail to convert it into actionable insight - which residues to mutate, what regions to validate, or how to use it in publications.

Why It Happens

AlphaFold is treated as a black-box tool, rather than a part of integrated structural biology. There is no pipeline to turn the model into experiments, or figures for reviewers.

Real Example

In a membrane receptor study, AlphaFold predicted a multimer with a suspected ligand site. But no validation was done, and the reviewer asked for epitope mapping and docking. The team had to re-analyze everything before acceptance.

What We Do Differently

We don’t stop at the model. We design mutagenesis plans, identify conserved interface residues, run stability simulations, and prepare publication-quality figures with domain labels and interface highlights. We also prepare validation proposals - docking, cryo-EM fitting, qPCR. Reviewers often ask "so what?" - we make sure there’s a real answer.

Final Remarks

AlphaFold is a breakthrough - but not a magic bullet. It produces single-frame structural predictions without energy calculations, dynamics, or direct knowledge of function. If used carelessly, it can mislead. If used wisely, with integration of additional modeling, energy tools, and biological judgment, it can become very powerful.

In our experience, the biggest gains come when AlphaFold is treated as a starting point, not an endpoint. Adding FoldX, Rosetta, AutoDock, and functional annotation layers helps you go from model to mechanism - or from prediction to publication.

Avoiding the eight pitfalls described here does not guarantee correctness, but it does prevent many common mistakes. And that is already a big step forward.

This blog article was authored by Justin T. Li, Ph.D., Lead Bioinformatician. To learn more about AccuraScience's Lead Bioinformaticians, visit https://www.accurascience.com/our_team.html.