How To Use AlphaFold To Predict Structure: Structural Analysis and Computational Methodologies in Bioinformatics

Introduction

The advent of deep learning-based protein structure prediction has fundamentally altered the landscape of structural bioinformatics [1]. AlphaFold, developed by DeepMind, represents a transformative computational tool that predicts three-dimensional protein coordinates directly from amino acid sequences [1, 2]. Its architecture leverages transformer neural networks and co-evolutionary information encoded in multiple sequence alignments (MSAs) to achieve near-experimental accuracy for many globular domains [3, 4]. For veterinary virologists and molecular diagnosticians, AlphaFold provides an unprecedented ability to model viral proteins, host receptors, and their complexes without recourse to X-ray crystallography or cryo-electron microscopy [5, 6]. This article provides a rigorous methodological framework for deploying AlphaFold in structural analysis, emphasizing critical assessment of prediction confidence, handling of topological artifacts, and integration with complementary computational and experimental techniques.

Core Methodology: Input Preparation and Model Execution

AlphaFold operates on a principle of end-to-end differentiable architecture that processes sequence and evolutionary information through two main stages: the distillation of MSA and template features into a representation space, followed by iterative structure refinement via equivariant attention [4, 7]. The primary input is the target amino acid sequence in FASTA format. For multimer predictions, multiple sequences are concatenated with a chain break token [8].

Multiple Sequence Alignment Generation

The quality of the predicted structure is heavily dependent on the depth and diversity of the MSA [8, 4]. AlphaFold uses a custom pipeline that employs Jackhmmer and HHblits to search sequence databases such as UniRef90 and BFD. The resulting MSA captures evolutionary covariation between residue pairs, which the model interprets as spatial proximity constraints [4, 9]. For viral proteins with limited sequence representation in public databases, the MSA may be shallow, leading to lower prediction confidence [6]. In such cases, using phylogenetic diverse homologs rather than simply increasing alignment depth can stabilize the latent space [4].

Template Identification

AlphaFold also queries the Protein Data Bank (PDB) for homologous solved structures to use as templates [10, 9]. These templates provide initial fold information but are not strictly required; the model can predict structures de novo using only MSA-derived information. The PDB and structural formats are discussed in detail in a companion article, "The Protein Data Bank (PDB): Structural Formats, Coordinates, and Archival Validation Standards" [see link].

Model Execution and Output

The user may choose between AlphaFold2 (AF2) and AlphaFold3 (AF3). AF3 extends the architecture to predict complexes with nucleic acids, ligands, and post-translational modifications [5, 11]. The primary outputs include:

• Predicted structure in PDB or mmCIF format.
• Per-residue confidence score (pLDDT) ranging from 0 to 100.
• Predicted Aligned Error (PAE) matrix.
• For multimers, the interface pTM (ipTM) score [12, 11].

These outputs are essential for evaluating the reliability of the prediction.

Confidence Metrics: pLDDT, pTM, and ipSAE

The predicted Local Distance Difference Test (pLDDT) score estimates the accuracy of each residue's local environment [13]. Residues with pLDDT > 90 are considered highly confident and suitable for detailed structural analysis. Regions with pLDDT < 50 correspond to intrinsically disordered regions (IDRs) or flexible loops [6, 13]. The pLDDT score can be combined with local contact models to predict backbone dynamics, as shown in the cdsAF2 approach [13].

For protein-protein interfaces, AlphaFold provides the interface predicted Template Modeling (ipTM) score. However, the ipTM has documented mathematical limitations: it is sensitive to the inclusion of disordered or accessory domains, because it normalizes over total chain length [12]. The corrected ipSAE (interaction prediction Score from Aligned Errors) metric, which uses only residue pairs with good PAE, discriminates true from false complexes more effectively than raw ipTM [12].

Table 1 summarizes the key confidence metrics.

The pTM and ipTM metrics have been used to assess transcription factor binding predictions for non-coding variant evaluation [11] and to validate leucine zipper dimer predictions [14]. However, high pLDDT does not guarantee correct topology; AlphaFold can predict complex knots that are physically impossible due to topological barriers [3].

Applications in Veterinary Virology

AlphaFold has been applied to predict structures of viral replication complexes, envelope glycoproteins, and host-pathogen interfaces [5, 6]. For example, AF3 successfully modeled the late transcription factor (LTF) complex of beta- and gamma-herpesviruses, revealing conserved interfaces and metal-binding sites despite low sequence conservation [5]. The predicted structures of viral RNA-dependent RNA polymerases (RdRps) have elucidated conformational ensembles and IDRs critical for replication [6].

In the context of viral entry mechanisms, AlphaFold can model receptor-binding domains and predict the impact of mutations on host tropism. These approaches are discussed in related articles such as "Structural Prediction of Viral Envelope Glycoproteins Using AlphaFold2: Implications for Host Receptor Binding and Vaccine Design" [see link] and "Structural Bioinformatics of Viral Glycoproteins" [see link]. For structural characterization of polymerase-host factor complexes, see "Structural characterization of viral polymerase-host factor complexes using hybrid modeling" [see link].

The utility of AlphaFold extends to guiding peptide design. In one study, AF-Multimer predicted a previously unknown interface between MYC and Miz-1, enabling the design of stapled peptidomimetics that disrupt MYC/MAX dimerization [15]. This exemplifies how predicted structures can inform therapeutic targeting of intrinsically disordered proteins.

Limitations and Cautions

Despite its power, AlphaFold possesses recognized limitations that must be considered before interpreting predictions.

First, AlphaFold does not predict alternative conformations or fold-switching [7]. It outputs a single static structure for each input, which may not capture the conformational ensemble relevant to function. For membrane proteins, AlphaFold often predicts closed or inactive states even when the biological context demands an open conformation [16]. Combining AlphaFold sampling with small-angle scattering data can recover weighted conformational ensembles [16].

Second, the model is blind to topological barriers. Dabrowski-Tumanski and Stasiak demonstrated that AlphaFold predicts complex composite knots in synthetic constructs where such knots cannot form due to chain non-permeability [3]. This highlights the need for caution when interpreting topological features.

Third, predictions can be biased by training data. Guan and Keating found that AF2-Multimer struggles to generalize to novel binding sites not represented in the training set [8]. Performance on protein-peptide docking depends heavily on the quality of the peptide MSA [17, 8].

Fourth, AlphaFold models cannot account for post-translational modifications, ligand binding, or environmental factors that influence structure [18, 19]. The predictions should be considered hypotheses that require experimental validation [18]. For drug docking, AF2 models are not superior to traditional homology models in predicting binding poses, likely due to subtle inaccuracies in side-chain positioning and pocket geometry [20, 10].

Fifth, high-confidence predictions can be misleading for low-accuracy regions. Terwilliger et al. showed that high-pLDDT models can still have global distortions in domain orientation [18]. Similarly, AlphaFold predicts the FosB homodimer leucine zipper with high confidence even though electrostatics prevent its formation in vivo [14].

Computational Workflow: A Decision Tree

The following Mermaid diagram outlines a recommended workflow for using AlphaFold in structural analysis, from input preparation to experimental validation.

flowchart TD
    A[Target Sequence / Pairwise Interaction], > B[Generate Multiple Sequence Alignments]
    B, > C{MSA depth sufficient?}
    C, Yes, > D[Run AlphaFold2 or AlphaFold3]
    C, No, > E[Search broader databases or use structural templates]
    E, > D
    D, > F[Evaluate pLDDT and PAE]
    F, > G{Confidence thresholds met?}
    G, Yes, > H[Assess topological plausibility]
    G, No, > I[Consider experimental structure determination or alternative modeling]
    H, > J{Topology consistent with known physics?}
    J, Yes, > K[Use model for hypothesis generation]
    J, No, > L[Reject predicted topology / interrogate potential IDR]
    K, > M[Validate key features experimentally\n(e.g., mutagenesis, crosslinking, SAXS)]
    M, > N[Iterative refinement or publication]

This workflow emphasizes that AlphaFold predictions are not final answers but starting points for hypothesis-driven research [18].

Integration with Other Computational Methods

AlphaFold predictions can be improved and validated through integration with other computational tools. For homooligomeric assemblies with cubic symmetry, combining AlphaFold subunit predictions with symmetric docking (e.g., using Rosetta or template-based docking) yields high-quality models with median TM-scores of 0.99 [21]. For enzyme thermostability prediction, ensembles of AlphaFold models provide more accurate free energy calculations than single crystallographic structures [22].

Residue contact maps derived from AlphaFold predicted structures achieve higher precision than classical contact prediction methods, and structural features from the neighborhood of residue pairs can further improve contact prediction to over 91% precision [23]. This is particularly relevant for transmembrane proteins, where AlphaFold's performance on inter-helical contacts is already strong [23].

For predicting the functional impact of mutations, machine learning classifiers trained on AlphaFold structures can achieve >80% accuracy in identifying protein regions that perturb transcriptional activity [24]. The Conformational Attention Analysis Tool (CAAT) can identify amino acids critical for a given conformation by perturbing the model's attention [25].

Conclusion

AlphaFold represents a paradigm shift in structural biology, offering rapid and often accurate predictions that accelerate research in veterinary virology and diagnostics. However, the tool requires careful handling: users must assess confidence metrics, verify topological consistency, and understand the model's training biases. Predictions are most valuable when treated as hypotheses that guide experimental design rather than as definitive structures. By adopting the rigorous workflow described here and integrating AlphaFold with orthogonal computational and experimental methods, researchers can leverage deep learning to probe viral protein structure, host-pathogen interactions, and potential therapeutic targets with previously unattainable speed and scale.

Disclaimer: This article is for educational and informational purposes only. It is not intended to substitute for professional veterinary advice, diagnosis, treatment, or regulatory guidance. Always consult a licensed veterinarian or qualified specialist regarding animal health, disease diagnosis, and therapeutic decisions.

References

[1] Hill, R., & Stein, C. (2026). How Artificial Intelligence Shapes Science: Evidence from AlphaFold. Social Science Research Network. https://www.semanticscholar.org/paper/f872ede0ce70f748bb815445d4855bbf72ece372

[2] Desai, D., Kantliwala, S. V., Vybhavi, J., et al. (2024). Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics. Cureus. https://www.semanticscholar.org/paper/6e713174bb14797063b74af3a3971c4baa7eada6

[3] Dabrowski-Tumanski, P., & Stasiak, A. (2023). AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology. Molecules. https://www.semanticscholar.org/paper/399ac30b2117dcacce43ada834f32e48784f6ee4

[4] Feldman, J., & Skolnick, J. (2026). AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction. bioRxiv. https://www.semanticscholar.org/paper/a644c5ae1f1d9b59d228bc0f6e56613d404c69c4

[5] Price, D. H. (2025). Structure Prediction of Complexes Controlling Beta- and Gamma-Herpesvirus Late Transcription Using AlphaFold 3. Viruses. https://www.semanticscholar.org/paper/f450920174237a7f2e16c176c701c265a004a529

[6] Tahzima, R., Charon, J., Díaz, A., et al. (2025). Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enable RNA virus discovery. Frontiers in Virology. https://www.semanticscholar.org/paper/3debd19a595ca875046369f04fb61caf53cd99d3

[7] Laurents, D. (2022). AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function. Frontiers in Molecular Biosciences. https://www.semanticscholar.org/paper/44b5116e5c7ad3ade1fc929da3a99c1dbd76e0e9

[8] Guan, L., & Keating, A. (2025). Training bias and sequence alignments shape protein–peptide docking by AlphaFold and related methods. Protein Science. https://www.semanticscholar.org/paper/065f7d9cc989b5b4162a3f3f82b1f4bae6b610be

[9] Tong, A., Burch, J., McKay, D., et al. (2021). Could AlphaFold revolutionize chemical therapeutics? Nature Structural & Molecular Biology. https://www.semanticscholar.org/paper/9f72c3aea2220ecd32cfdeff23b0095d0f2085a3

[10] Scardino, V., Di Filippo, J. I., & Cavasotto, C. N. (2022). How good are AlphaFold models for docking-based virtual screening? iScience. https://www.semanticscholar.org/paper/73c5288748a71ad705b10e65154d6fda682b89d9

[11] Gerasimavicius, L., Biddie, S., & Marsh, J. A. (2025). A structure-guided approach to noncoding variant evaluation for transcription factor binding using AlphaFold 3. bioRxiv. https://www.semanticscholar.org/paper/3f886787563f9d5f5d2f84f1c940ac8e79f898cf

[12] Dunbrack, R. (2025). Rēs ipSAE loquunt: What’s wrong with AlphaFold’s ipTM score and how to fix it. bioRxiv. https://www.semanticscholar.org/paper/b38b986a04306fab239a9b182b5f948c536da35c

[13] Ma, P., Li, D.-W., & Brüschweiler, R. (2023). Predicting protein flexibility with AlphaFold. Proteins: Structure, Function, and Bioinformatics. https://www.semanticscholar.org/paper/d0bea4c517356bd59f7b9da261e16b7f228a2633

[14] Mitic, I., Rowell, K., Litfin, T., et al. (2025). Assessing the validity of leucine zipper constructs predicted by AlphaFold. Protein Science. https://www.semanticscholar.org/paper/814707e0dfa0f9ff385baa7e1dec687a0f653d10

[15] Ellenbroek, B., Tirtosentono, A. S. S., & Pomplun, S. (2025). AlphaFold‐Guided Discovery of an Overlapping MYC/Miz‐1 Interface Enables Peptidomimetic Disruption of MYC/MAX. ChemMedChem. https://www.semanticscholar.org/paper/fe30f603cc84f1b0ea55bc5014e867841b6bf116

[16] Eriksson Lidbrink, S., Howard, R. J., Haloi, N., et al. (2024). Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold. bioRxiv. https://www.semanticscholar.org/paper/d2d96c46113b01d56c3eed958b6e7cd08d184697

[17] Guan, L., & Keating, A. (2025). How AlphaFold and related models predict protein-peptide complex structures. bioRxiv. https://www.semanticscholar.org/paper/4f89f1a0998f0aec1b838bed77202de73047ea65

[18] Terwilliger, T., Liebschner, D., Croll, T., et al. (2023). AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. bioRxiv. https://www.semanticscholar.org/paper/5d7a68cc2346e688913a3f303690cc05799a1af9

[19] Bagdonas, H., Fogarty, C. A., Fadda, E., et al. (2021). The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. https://www.semanticscholar.org/paper/712ca0d704fb3a9f2436d512425757eddb1eeeb5

[20] Karelina, M., Noh, J. J., & Dror, R. (2023). How accurately can one predict drug binding modes using AlphaFold models? bioRxiv. https://www.semanticscholar.org/paper/e076577525f90820f6e80f0cb5eb8f34ebaa4590

[21] Jeppesen, M., & André, I. (2023). Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking. bioRxiv. https://www.semanticscholar.org/paper/3347ea893481b6761b411958253f10d5fde241a5

[22] Peccati, F., Alunno-Rufini, S., & Jiménez‐Osés, G. (2023). Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles. Journal of Chemical Information and Modeling. https://www.semanticscholar.org/paper/c0b50a01303b80c88e525113645bd07990efa09e

[23] Sawhney, A., Li, J., & Liao, L. (2024). Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features. International Journal of Molecular Sciences. https://www.semanticscholar.org/paper/7cec18625c58508e4597dc1516ff2958e1c3d8d8

[24] Shukla, K., Idanwekhai, K., Naradikian, M. S., et al. (2024). Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation. Journal of Chemical Information and Modeling. https://www.semanticscholar.org/paper/b64ef26bfd41ff840b591cb89b0b47a3450c3d4c

[25] Clore, M. F., Thole, J. F., Dontha, S., et al. (2025). Explaining how mutations affect AlphaFold predictions. bioRxiv. https://www.semanticscholar.org/paper/ee08413b3b2632b3d3b03804a2bd31943347fcc1

[26] Chattopadhyay, M., Xu, J., & Rizo, J. (2026). Guiding AlphaFold to predict how Munc13-1 opens Syntaxin-1. FEBS Open Bio. https://www.semanticscholar.org/paper/dcb62a165d84524980e330466e0518fd413c9875

[27] Christian, L., Winslett, O., Thiemann, C., et al. (2025). Using Alphafold to Deepen Understanding of Protein Folding and Big Data in an Engineering Cell and Molecular Physiology Course. Frontiers in Education Conference. https://www.semanticscholar.org/paper/ed767a1bb1e83db7aaeaa882b04ecd9df4e8aece

[28] Sherpa, P., Chong, K., & Tayara, H. (2024). FvFold: A model to predict antibody Fv structure using protein language model with residual network and Rosetta minimization. Comput. Biol. Medicine. https://www.semanticscholar.org/paper/1e73fd25c0901d117b86247bc1d50b7848d0e29b

[29] Haley, O. C., Tibbs-Cortes, L. E., Hayford, R. K., et al. (2025). Why do some predicted protein structures fold poorly? Benchmarking AlphaFold, ESMFold, and Boltz in maize. bioRxiv. https://www.semanticscholar.org/paper/76f6cb7f19e4f09db73830ba5299bf8e7ce178c

[30] Namazova, S., Brondetta, A., Strittmatter, Y., et al. (2025). Not Yet AlphaFold for the Mind: Evaluating Centaur as a Synthetic Participant. arXiv.org. https://www.semanticscholar.org/paper/0a38b6a2e9574ba42696a8d785e154386f738d4b

[31] Najafi, N. N., Karbassian, R., Hajihassani, H., et al. (2025). Unveiling the influence of fastest nobel prize winner discovery: alphafold’s algorithmic intelligence in medical sciences. Journal of Molecular Modeling. https://www.semanticscholar.org/paper/9b7452722c6b69b9e37a99a6d6498e9340afe093

[32] Zhao, X., Yang, V. B., Menta, A. K., et al. (2024). AlphaFold’s Predictive Revolution in Precision Oncology. AI in Precision Oncology. https://www.semanticscholar.org/paper/9b9557f288a58991378f1f9d472e1ea834f7cfde

[33] Gadde, N., Dodamani, S., Altaf, R., et al. (2024). Leveraging AlphaFold 3 for Structural Modeling of Neurological Disorder-Associated Proteins: A Pathway to Precision Medicine. bioRxiv. https://www.semanticscholar.org/paper/b4f6446fb9460f15bd5a531de87b54dfad7e487a

[34] Hu, Y., Li, R., Xie, C., et al. (2024). Research on mutant proteins based on AlphaFold model. Other Conferences. https://www.semanticscholar.org/paper/7de832aa630970f2796828c5e809b7004d4afd97

[35] Walmsley, M., Connolly, J. A., Takano, E., et al. (2026). Graph neural networks can predict ketosynthase substrate specificity. Scientific Reports. https://www.semanticscholar.org/paper/e21d3a53e53233e67fde4dc22933a9b5f93075df