PreprintCited by 197Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Impact of protein conformational diversity on AlphaFold predictions

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Saldaño, Tadeo, Escobedo, Nahuel, Marchetti, Julia, Zea, Diego Javier, Mac Donagh, Juan, Velez Rueda, Ana Julia, Gonik, Eduardo, Melani, Agustina García, Novomisky Nechcoff, Julieta, Salas, Martín N., Peters, Tomás, Demitroff, Nicolás, Fernandez Alberti, Sebastian, Palopoli, Nicolas, Fornasari, Maria Silvina, Parisi, Gustavo·2021·Bioinformatics

DOI 10.1101/2021.10.27.466189

Preprint — published as 10.1093/bioinformatics/btac202 · Paper page

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Abstract

Abstract After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here we address the performance of AlphaFold2 predictions under this ensemble paradigm. Using a curated collection of apo-holo conformations, we found that AlphaFold2 predicts the holo form of a protein in 70% of the cases, being unable to reproduce the observed conformational diversity with an equivalent error than in the estimation of a single conformation. More importantly, we found that AlphaFold2’s performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions.

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“Far from disappointing, this observation was expected since a large set of redundant protein structures (conformers) would have been required during the neural network training processes [5] in order to predict conformational diversity.”
Background
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Method
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Local Normal Mode Analysis for Fast Loop Conformational Sampling
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(2025) · doi:10.7554/elife.94029.3 · Cited by 40
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“Although AF2 has revolutionized static structure prediction, capturing this dynamic behavior remains an unresolved and complex challenge [24].”
Understanding protein folding with machine learning models? The case of AlphaFold2
Schuster, A. (2026) · Synthese · doi:10.1007/s11229-025-05426-4 · Cited by 3
“( Saldaño et al., 2022, p. 2742).”
Hidden in protein sequences: Predicting cryptic binding sites
Škrhák, V., Gamouh, H., & Hoksza, D. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3765612.3767221 · Cited by 4
Structural flexibility of a recombinant intrinsically disordered LEA protein from Ramonda serbica
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“These results must be interpreted with caution, due to AlphaFold’s biased prediction towards helical structures and its low confidence in modeling disordered regions 43,44 .”
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“AlphaFold2 can miss the corresponding open conformation and instead favor a ligand-bound (closed) form (Salda ~ no et al. 2022), which may affect downstream analyses that require structural flexibility.”
Influential
Optimizing 3D Structural Predictions of Murine β2-Adrenergic Receptor: Swiss-Model Outperforms AlphaFolds
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“39 Azzaz et al .”
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Result
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Method
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Conditional Diffusion with Locality-Aware Modal Alignment for Generating Diverse Protein Conformational Ensembles
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Method
Sequence‐Similar Protein Domain Pairs With Structural or Topological Dissimilarity
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Background
AlphaFold2 predicts interactions amidst confounding structural compatibilityPreprint
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Self-citation
Incremental Boundary Refinement using Self Axial Reverse Attention and Uncertainty-aware Gate for Colon Polyp Segmentation
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“Nowadays, axial attention is used in broad scope, including the main block for feature extraction, information auto-focusing, or interpreting protein sequences in AlphaFold [9].”
Background
Conformational epistasis impairs AlphaFold structural predictionsPreprint
Sawicki, L. R., Benítez, G. I., Carletti, M. S., Palópoli, N., Fornasari, M. S., & Parisi, G. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.15.516638 · Cited by 1
“Furthermore, the AF2 output consists of top ranked models which do not reproduce the ensemble nature of proteins essential to understand protein function (Nussinov et al., 2022; Saldaño et al., 2022).”
BackgroundSelf-citation
AlphaFold modeling of polyubiquitin complexes and covalently linked proteins
Fábián, B., Stuke, J. F. M., Heinz, M., & Hummer, G. (2025) · doi:10.1101/2025.05.27.656350 · Cited by 3
“Their linker approach is generalizable to other covalent interchain linkages and can enforce distance restraints for integrative structural modeling.”
Computational methods in physical virology: a critical perspective across lengths and timescales
Poma, A. B., Cofas-Vargas, L. F., da Silva, F. L. B., et al. (2026) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuag050
Evaluation of methods for AlphaFold-based integrative modeling
Majila, K., & Viswanath, S. (2026) · bioRxiv · doi:10.64898/2026.09.13.751323
“In the absence of experimental structures for multi-state protein complexes, we evaluated two monomeric proteins known to exist in apo and holo conformations 26 .”
DESCIFRANDO LA FORMA DE LA VIDA: DEL MODELADO POR HOMOLOGÍA A LA REVOLUCIÓN DE ALPHAFOLD
Hernández, M. A. S., Abrech, N. N., & Lozano-Aponte, J. (2026) · RD-ICUAP · doi:10.32399/icuap.rdic.2448-5829.2026.35.1729
“Predice únicamente una sola forma de la proteína (Saldaño et al., 2022).”
From implicit learning to explicit structural constraints for efficient protein language modelingPreprint
Shen, L., Chao, L., Liu, T., Liu, Q., Zhou, G., Wang, H., Dong, X., Li, T., Zhang, X., & Ni, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.17.712034
“AFDB greatly expands structural coverage [17, 18], but AlphaFold-derived structures may carry PDB coverage bias, MSA and homology dependence, single-conformation bias and geometric idealization [56, 57].”
Sampling in structure-token space enables accurate prediction of multiple protein conformationsPreprint
Wang, Z., Yu, Y., Zheng, W., Bu, D., & Bu, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.03.708411
“The benchmark was assembled from three sources: apo–holo protein pairs [23], fold-switching proteins [24], and a custom-curated subset of the Protein Data Bank (PDB) [6].”
Deep Learning for Protein Modeling: From Single Structure to Bound Complex and Thermodynamic Ensemble, With a Focus on Architectural Design
Angioletti, D., Carli, M., Nobile, M., & Limongelli, V. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70082 · Cited by 1
Dynamic Protein Structure Paradox: An Integrative Framework for Endpoint-Conditioned Evidentiary Sufficiency in Structure-to-Function Claims
Niazi, S. K. (2026) · Cells · doi:10.3390/cells15171560
“Conformational diversity can reduce concordance between a single predicted model and multiple experimental conformations with low confidence [76], and regions of low confidence may indicate intrinsic disorder, weak evolutionary signals, ambiguous domain orientations, or other factors [50].”
Structural basis for nutrient-activated channel opening in bacterial spore germination receptorsPreprint
Cofsky, J. C., Ramírez‐Guadiana, F. H., Sobecks, B. L., Artzi, L., Amon, J. D., Gao, Y., Dror, R. O., del Mármol, J. I., Rudner, D. Z., & Kruse, A. C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.27.747515
AlphaConformers: Structure-guided sampling enables prediction of multiple protein conformationsPreprint
Daniel, J., vitoriano, L., & Zea, D. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.18.745512
“This threshold was selected based on systematic testing on our development set of 12 proteins from Salda˜no et al. [11], where a cutoff of 0.6 effectively eliminated low-confidence predictions without reducing overall performance.”
InfluentialSelf-citation
Hierarchical discrete representations for coarse-to-fine protein conformation generation
On, S., Jeong, Y., Kim, K., Kang, K., & Kim, E.-S. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag611
“· Conformational-changing pairs (Chakravarty and Porter 2022, Salda ~ no et al. 2022): Utilized to measure the reproducibility of structural variations, including per-residue flexibility and secondary structure transitions.”
Carbonara: a SAXS-guided seeding framework for exploring protein solution-state dynamicsPreprint
Mckeown, J. J. C., Brown, C., Bale, A., Fisher, H., Rambo, R. P., Essex, J. W., Degiacomi, M. T., & Prior, C. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.25.740665
“Systematic analyses have revealed that AI models under-represent larger-scale structural rearrangements [10].”
Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions
Parikh, V., Foley, B., Gatlin, W., Ludwick, M., Turano, L., & Verkhivker, G. M. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103448 · Cited by 1
Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins
Ye, M., Wang, Y.-H., Brogi, M., et al. (2026) · bioRxiv · doi:10.64898/2026.07.10.737860
Structural Prediction and Antigenic Characterization of Recombinant Nucleocapsid Protein (p14) of Small Ruminant Lentivirus
Castañeda-Montes, M. A., Cerriteño-Sánchez, J. L., Cuevas‐Romero, J. S., Montes, F. J. C., González-Esparragoza, D., la Vega, L. D. M. Á.-D., & Álvarez, H. R. (2026) · Viruses · doi:10.3390/v18070803
Sheep erythrocyte potassium locus highlights MYADM family regulation of electroneutral potassium chloride cotransporters (KCC)
Gonzalez, M. V., Mousel, M. R., Herndon, D. R., Massa, A. T., Durfee, C. J., Knowles, D. P., Taylor, B. J., Neibergs, H. L., Seabury, C. M., & White, S. N. (2026) · BMC Genomics · doi:10.1186/s12864-026-13029-7
“However, AlphaFold has a tendency to model only one conformation among many (probably due to biases in training input data, where some conformations have been more common and perhaps more easily observed experimentally) [131].”
From prediction to discovery: AI as an observatory of physical organization in protein space
Zheng, Y., Zhang, Z., Wang, Y., Kang, W., Ren, W., & Tang, Q.-Y. (2026) · AI for Science · doi:10.1088/3050-287x/ae78ea
“Large-scale comparisons of pLDDT with experimental conformational diversity show that pLDDT-based ranking of flexible and rigid regions is reproducible across diverse protein sets [75, 128].”
Learning the Structural Diversity of Olfactory Receptors: A Genomic Case Study in Two Longhorn Beetles (Cerambycidae: Lamiinae)
Duncan, M., Sylvester, T. P., Walden, E., Lozano, J. R., Turner, E., Duncan, S., Mitchell, R. F., McKenna, D. D., & Adams, R. (2026) · Insects · doi:10.3390/insects17060587 · Cited by 1
“AlphaFold models were generated using Google Colab Notebooks [79] according to the recommended default settings, including Amber energy minimization to obtain relaxed structures, as suggested in prior studies [10,80].”
Dynamic proteomics of Nasonia vitripennis pupal development reveals testis-specific regulation of new proteins (orphans) likely related to exo-α-sialidases
Lécureuil, C., Almunia, C., Guérin, V., Armengaud, J., Moreau, T., & Fouchécourt, S. (2026) · BMC Genomics · doi:10.1186/s12864-026-12918-1
“This likely reflects both their intrinsically disordered nature, as well as lack of knowledge due to lacunar information in databases and current algorithm limits due to insufficient alignments [38, 65].”
ConforFlux: Particle-Guided Trunk Repulsion for Diverse Protein ConformationsPreprint
Suzuki, S., & Amagasa, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.16.725138
“Deep-learning structure predictors now achieve near-experimental accuracy on individual folds [Jumper et al., 2021, Abramson et al., 2024, Passaro et al., 2025], yet their standard inference pipelines remain concentrated around a dominant conformation for each input [Saldaño et al., 2022].”
Computational engineering of Amphibalanus amphitrite CP19k cement protein to design reduced immunogenicity underwater bioadhesive
Zargari, G., Irani, M. A., Assadizadeh, M., Khoniki, S., & Eslami, M. J. (2026) · Results in Chemistry · doi:10.1016/j.rechem.2026.103190
Controlled Protein Design via Statistical Energy Functions: A Rossmann Fold Case Study
Zhang, L., Wang, C., & Hu, S. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00612
Mechanistic Insights into the Structural Asymmetry of the LanFEG Transporter NisFEG in Lantibiotic ImmunityPreprint
Cea, P. A., Gottstein, J., Schott-Verdugo, S., Mammen, C., Smits, S. H. J., & Gohlke, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.09.681468
“Modelling of NisFEG in the apo and ATP-bound states revealed no significant reorientations of the E-loop; however, it is critical to point out that deep learning-based structure prediction methods are limited in predicting the conformational diversity of mobile proteins [46].”
Microscopic Insights into a Ligand Escape Pathway and Energetics in Leucine–Isoleucine–Valine Binding Protein
Das, J. (2026) · ACS Omega · doi:10.1021/acsomega.5c12025 · Cited by 1
Multiple disulfide‐bonded states confer extensive conformational diversity in fibrinogen
Pijning, A. E., Butera, D., & Hogg, P. J. (2026) · Protein Science · doi:10.1002/pro.70558
Several multiple sequence alignment-perturbing methods enhance AlphaFold3 sampling of alternative protein states
Lidbrink, S. E., Nissen, I., Ahrlind, J. K., Howard, R. J., & Lindahl, E. R. (2026) · Communications Chemistry · doi:10.1038/s42004-026-02198-x
ProtCross: Bridging the PDB-AlphaFold Gap for Binding Site Prediction with Protein Point Clouds
Zhong, S., & Jiang, Y. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03224 · Cited by 1
Experimental Data Driven AI Framework for Flexible Protein Conformational ReconstructionPreprint
Yu, F., Prince, S., Tritt, A., Pande, K., Hura, G. L., Ruebel, O., & Tsutakawa, S. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.12.708611 · Cited by 1
An Energy Landscape Approach to Miniaturizing Enzymes using Protein Language Model EmbeddingsPreprint
Lála, J., Agrawal, H., Dong, F., Wells, J., & Angioletti‐Uberti, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.04.709378 · Cited by 1
“Indeed, in our mini-variant systems, we do observe the previously described correlation between pLDDT and RMSF [26] (Figure 11 in Supplementary Information), suggesting that one could potentially rely on folding models alone, reserving MD simulations only for a filtration step, instead of directly…”
Learning conformational flexibility of immune receptors
Shen, L.-C., Yu, D., & Song, J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01133-4
Exploring the Bottleneck in Cryo-EM Dynamic Disorder Feature and Advanced Hybrid Prediction Model
Zheng, S. (2025) · Biophysica · doi:10.3390/biophysica5030039 · Cited by 1
“Subsequent studies revealed that the predicted local distance difference test (pLDDT) from AlphaFold2 serves as an indicator of disordered regions, demonstrating unique capabilities in disorder prediction [22,23].”
Structural insights on the differentiation and reversion of conformational changes in SARS-CoV-2 spike protein models across variants occurring from December, 2019 to November, 2021
Cueno, M., & Imai, K. (2025) · ImmunoInformatics · doi:10.1016/j.immuno.2025.100055
Relative stability of different conformations and pathways of ligand escape in LIV-binding protein: A molecular dynamics simulation studyPreprint
Das, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.20.619253
Structure and dynamics of the cyanobacterial regulator SipA
Neira, J. L., Lopez-Redondo, M., Cámara-Artigas, A., Marina, A., & Contreras, A. (2024) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2024.109943
“In fact, it has been suggested that the regions with low plDDT values can be correlated to different existing conformations in the protein structure [41].”
Background
Convergent evolution of noxious heat sensing by TRPA5, a novel class of heat sensor in Rhodnius prolixus
Liénard, M. A., Baez-Nieto, D., Tsai, C.-C., et al. (2024) · bioRxiv · doi:10.1101/2023.05.26.542450
“These models are snapshots of certain conformations and evidently do not reproduce the diversity of the different states a protein may adopt (72).”
Background

Authors: Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., Novomisky Nechcoff, J., Salas, M. N., Peters, T., Demitroff, N., Fernandez Alberti, S., Palopoli, N., Fornasari, M. S., & Parisi, G.
Affiliations: National University of Quilmes (AR)
Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
Fundación Instituto Leloir (AR)
Corresponding author: Parisi, Gustavo
ORCID: Saldaño, Tadeo
Escobedo, Nahuel
Marchetti, Julia
Zea, Diego Javier
Gonik, Eduardo
Peters, Tomás
Demitroff, Nicolás
Parisi, Gustavo
Venue: Bioinformatics
Publisher: openRxiv
Published: 2021-10-28
Volume: 38
Issue: 10
Pages: 2742-2748
ISSN: 1367-4803 (ISSN-L)
DOI: 10.1101/2021.10.27.466189
OpenAlex: W4225552973
Cited by: 196
References: 55
Citations by year: 2026: 45 · 2025: 54 · 2024: 52 · 2023: 29 · 2022: 16
FWCI: 12.32
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by
Locations: Bioinformatics (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed (publishedVersion)
Greater South Information System
Funders: European Commission (HORIZON2020, 778247)
Consejo Nacional de Investigaciones Científicas y Técnicas (PIP-2015-2017 11220150100853CO, PICT2018)
Universidad Nacional de Quilmes (PUNQ 1309/19, 778247)
Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (PICT-2018 3457)
Agencia Nacional de Promoción Científica y Tecnológica (778247, CONICET)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords: protein conformational diversity, AlphaFold2, ColabFold, protein ensembles, ligand binding, pLDDT, model quality assessment, homologous protein families, protein structure prediction
MeSH terms: Protein Binding, Protein Conformation, Proteins

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Impact of protein conformational diversity on AlphaFold predictions

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