Impact of protein conformational diversity on AlphaFold predictions
Open accessPreprint — published as 10.1093/bioinformatics/btac202 · Paper page
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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AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function.
“Several preprints have provided evidence that AF2, despite its accuracy, likely does not learn the energy landscapes underpinning protein folding and function (Saldaño et al., 2021; Pak et al., 2021; Akdel et al., 2021).”
From Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022) · cites this paper as Saldaño et al., 2021
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“The choice of model should be carefully aligned with the intended applications; ( 3 ) While generative models extended from classic folding models have shown the ability to sample protein conformations, challenges remain in both multiple-state prediction and distribution prediction.”
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Representing structures of the multiple conformational states of proteins
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“Subsequently, the proclivity of AF2 to model one of multiple conformational states was also reported for a collection of ~100 apo / holo protein structure pairs [51].”
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Wang, Y.-Y., Lu, J.-R., Jaitly, N., Susskind, J. M., & Bautista, M. A. (2025) · International Conference on Learning Representations · doi:10.48550/arxiv.2509.18480 · Cited by 33 · see citing works
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AFsample2: Predicting multiple conformations and ensembles with AlphaFold2Preprint
Kalakoti, Y., & Wallner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596195 · Cited by 26 · see citing works
Ellaway, J. I. J., Anyango, S., Nair, S. S., Zaki, H. A., Nadzirin, N., Powell, H. R., Gutmanas, A., Váradi, M., & Velankar, S. S. (2024) · Structural Dynamics · doi:10.1063/4.0000251 · Cited by 27 · see citing works
Recent Advances and Challenges in Protein Structure Prediction
Peng, C., Liang, F., Xia, Y., Zhao, K., Hou, M., & Zhang, G. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01324 · Cited by 35 · see citing works
Aubel, M., Eicholt, L. A., & Bornberg‐Bauer, E. (2023) · F1000Research · doi:10.12688/f1000research.130443.1 · Cited by 24 · see citing works
Deep generative modeling of temperature-dependent structural ensembles of proteins
Janson, G., Jussupow, A., & Feig, M. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01737-2 · Cited by 24 · see citing works
“We further analyzedhomologousproteins with different experimental T m to explore whether a generative model can probe sequence properties in fl uencing thermal stability 47 , focusing on fi ve protein pairs 48 (Supplementary Table 6).”
InfluentialRandom, de novo, and conserved proteins: How structure and disorder predictors perform differently
Middendorf, L., & Eicholt, L. A. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26652 · Cited by 24 · see citing works
“44,46,48 We here conclude that such kinds of analyses are limited for de novo and random proteins, and their results from structure predictions differ highly from those of conserved proteins.”
BackgroundNeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
Qiao, Z., Ding, F.-Z., Dresselhaus, T., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2412.10743 · Cited by 23
Olanders, G., Testa, G., Tibo, A., Nittinger, E., & Tyrchan, C. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01475 · Cited by 26 · see citing works
Meller, A., de Oliveira, S. H. P., Davtyan, A., Abramyan, T. M., Bowman, G. R., & Bedem, H. V. D. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1171143 · Cited by 26 · see citing works
“While our work was based off a single AF structure as a starting point, we are aware of efforts to use these DL protein structure prediction tools to sample multiple conformations, thus better capturing protein flexibility (Saldanõ et al., 2022; Meller et al., 2023a).”
Generation of protein dynamics by machine learning
Janson, G., & Feig, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103115 · Cited by 18 · see citing works
Exploring protein functions from structural flexibility using CABS ‐flex modeling
Nithin, C., Fornari, R. P., Pilla, S. P., Wróblewski, K., Zalewski, M., Madaj, R., Koliński, A., Macnar, J. M., & Kmiecik, S. (2024) · Protein Science · doi:10.1002/pro.5090 · Cited by 21 · see citing works
“However, these endeavors are not without challenges, such as reconciling the static nature of structural predictions with the inherently dynamic nature of proteins (Saldaño et al., 2022) or better prediction of protein regions that are disordered under ambient conditions (Ma et al., 2023).”
BackgroundAlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!
Bonin, J. P., Aramini, J. M., Dong, Y., Wu, H., & Kay, L. E. (2024) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2024.107725 · Cited by 20 · see citing works
“In cases where a protein adopts multiple conformers, AF2 typically predicts only one of the structures, failing to capture the conformational diversity seen in the protein [19].”
Automated benchmarking of combined protein structure and ligand conformation prediction
Leemann, M., Sagasta, A., Eberhardt, J., Schwede, T. F., Robin, X., & Durairaj, J. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26605 · Cited by 23 · see citing works
“Despite AlphaFold being shown to prefer prediction of the holo form, 21 homology modeling was recently shown to be better at the PLC prediction task.”
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures
Masri, C. A., Trozzi, F., Lin, S.-H., Tran, O., Sahni, N., Pátek, M., Cichońska, A., Ravikumar, B., & Rahman, R. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad129 · Cited by 24 · see citing works
“Heged } us et al. (2022), Guo et al. (2022), and Salda ~ no et al. (2022) have also shown a relationship between pLDDT and protein conformational dynamics.”
Protein Structure Tokenization: Benchmarking and New Recipe
Yuan, X., Wang, Z., Collins, M., & Rangwala, H. (2025) · arXiv · doi:10.48550/arxiv.2503.00089
“3 shows that: (1) AminoAseed outperforms all models in Apo Holo and Fold Switching datasets, with an average relative gain of +12.83% over ESM3 across all metrics; (2) ESM3 performs second best; (3) VanillaVQ slightly trails ESM3 by -0.82%; (4) IF-based PSTs underperform most VQ-VAE-based PSTs because their training o…”
InfluentialAlphaFold2 can predict single-mutation effects
McBride, J. M., Polev, K., Abdirasulov, A., Reinharz, V., Grzybowski, B. A., & Tlusty, T. (2022) · doi:10.1101/2022.04.14.488301 · Cited by 87 · see citing works
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01131-6 · Cited by 20 · see citing works
“22,23,26–30).”
Deep learning methods for protein structure prediction
Qin, Y., Chen, Z., Ye, P., Xiao, Y., Zhong, T., & Yu, X. (2024) · MedComm – Future Medicine · doi:10.1002/mef2.96 · Cited by 29 · see citing works
Challenges in describing the conformation and dynamics of proteins with ambiguous behavior
Roca‐Martínez, J., Lázár, T., Gavaldá-García, J., Bickel, D., Pancsa, R., Dixit, B., Tzavella, K., Ramasamy, P., Sanchez-Fornaris, M., Grau, I., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.959956 · Cited by 20 · see citing works
“Indeed, in relation to conformational diversity as observed in the PDB from apo-holo pairs of conformers for the same protein (Saldaño et al., 2022), AlphaFold2 predicts the holo form in ~70% of cases but is unable to capture both states.”
BackgroundReliable protein-protein docking with AlphaFold, Rosetta, and replica-exchange
Harmalkar, A., Lyskov, S., & Gray, J. J. (2023) · doi:10.1101/2023.07.28.551063 · Cited by 40 · see citing works
Modeling Alternate Conformations with Alphafold2 via Modification of the Multiple Sequence Alignment
Stein, R. A., & Mchaourab, H. S. (2021) · doi:10.1101/2021.11.29.470469 · Cited by 258 · see citing works
“A recent report examining a curated subset of apo and ligand bound structures 302 suggested that pLDDT values infer local conformational changes.(31) The results presented here do not 303 suggest that these regions of low confidence are incorrect in their two- or three-dimensional structure, 304 but appear to be regio…”
BackgroundKersten, C., Clower, S., & Barthels, F. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01400 · Cited by 16 · see citing works
Deep learning approaches for conformational flexibility and switching properties in protein design
Rudden, L. S. P., Hijazi, M., & Barth, P. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.928534 · Cited by 12 · see citing works
“However, it has been indicated that AlphaFold2 is unable to accurately learn the underlying energy landscape that describes protein folding and function (Saldaño et al., 2021).”
BackgroundHidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR
Huang, Y., Ramelot, T. A., Spaman, L. E., et al. (2024) · bioRxiv · doi:10.21203/rs.3.rs-5994356/v1 · Cited by 13
“However, for highly-flexible proteins like GLuc, these differences highlight AF2's limitations in capturing the solution-dominant states 11,29,35-38 .”
Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00222 · Cited by 17 · see citing works
Verkhivker, G. M., Alshahrani, M., Gupta, G., Xiao, S., & Tao, P. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097747 · Cited by 15 · see citing works
“Several recent studies have provided strong evidence that structure prediction capabilities of the AF2 methods are not trivially expandable for the prediction of complex allosteric landscapes and mechanisms of allosteric regulation [69,70].”
BackgroundUrban, P., & Pompon, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-20390-6 · Cited by 16 · see citing works
Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy
Behera, S., Hahn, D. F., Wilson, C. J., Marsili, S., Tresadern, G., Gapsys, V., & de Groot, B. L. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00947 · Cited by 14 · see citing works
Shen, C., Han, X., Cai, H., Chen, T., Kang, Y., Pan, P., Ji, X., Hsieh, C., Deng, Y., & Hou, T. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c02740 · Cited by 10 · see citing works
Rustamov, K., & Baev, A. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae197 · Cited by 13 · see citing works
“…these advancements, AF2’s initial limitation was its inability to predict different substates of metamorphic proteins (Chakravarty and Porter 2022, Salda ~ no et al. 2022), a challenge that has been recently addressed by various multiple sequence alignment (MSA) clustering approaches (Monteiro da…”
InfluentialRaisinghani, N., Parikh, V., Foley, B., & Verkhivker, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312968 · Cited by 10 · see citing works
Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development
Niazi, S. K., Mariam, Z., & Paracha, R. Z. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010007 · Cited by 19 · see citing works
Sampling the conformational landscapes of transporters and receptors with AlphaFold2
del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2021) · doi:10.1101/2021.11.22.469536 · Cited by 511 · see citing works
“In contrast with a recent preprint 35 , the predicted flexibility values failed to correlate with their pLDDT values, which reflect the confidence of the AF2 prediction of each residue’s local environment 36 .”
BackgroundBerksoz, M., & Atilgan, C. (2024) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26688 · Cited by 8 · see citing works
“40 Models also showed varying degrees of similarity to the holo sensor structure (PDB: 3OSR).”
BackgroundRamasamy, P., Zuallaert, J., Martens, L., & Vranken, W. F. (2026) · Protein Science · doi:10.1002/pro.70376 · Cited by 15 · see citing works
Rotem‐Bamberger, S., Fahoum, J., Keinan‐Adamsky, K., Tsaban, T., Avraham, O., Shalev, D. E., Chill, J. H., & Schueler‐Furman, O. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102145 · Cited by 14 · see citing works
Toward the Prediction of Binding Events in Very Flexible, Allosteric, Multidomain Proteins
Basciu, A., Athar, M., Kurt, H., Neville, C., Malloci, G., Muredda, F. C., Bosin, A., Ruggerone, P., Bonvin, A. M. J. J., & Vargiu, A. V. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01810 · Cited by 12 · see citing works
The Rapid Evolution of De Novo Proteins in Structure and Complex
(2024) · doi:10.1093/gbe/evae107 · Cited by 13 · see citing works
Piomponi, V., Cazzaniga, A., & Cuturello, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.08.647782 · Cited by 1 · see citing works
“Nevertheless, despite its remarkable performance, AF2 is primarily designed to predict a single dominant conformation, and its ability to capture multiple functional states of a protein remains limited [2,3].”
Hashem, S., Dougha, A., & Tufféry, P. (2025) · Molecules · doi:10.3390/molecules30051047 · Cited by 18 · see citing works
Learning to engineer protein flexibility
Kouba, P., Planas-Iglesias, J., Damborský, J., et al. (2024) · International Conference on Learning Representations · doi:10.48550/arxiv.2412.18275 · Cited by 10
“…be induced by sequence variability in the region, for instance), it has been shown to be negatively correlated with flexibility (Ruff & Pappu, 2021; Guo et al., 2022; Salda ˜ no et al., 2022) and exploited to approximate flexibility as measured by Nuclear Magnetic Resonance (NMR) (Ma et al., 2023).”
Multi-state Protein Design with DynamicMPNN
Abrudan, A., Ojeda, S., Joshi, C. K., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2507.21938 · Cited by 9
“For dataset splitting, we first curate a benchmark set from five previous studies of proteins with large conformational changes: 92 metamorphic proteins (Porter & Looger, 2018), 91 apo-holo proteins (Salda˜no et al., 2022 Overall architecture .”
Swapna, G. V. T., Dube, N., Roth, M. J., & Montelione, G. T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013590 · Cited by 5 · see citing works
“Recent advances have been reported using modified AF2 protocols and “enhanced sampling” methods to model multiple conformational states of proteins, including integral membrane proteins [14].”
Renthal, R. D. (2024) · Current Research in Insect Science · doi:10.1016/j.cris.2024.100082 · Cited by 8 · see citing works
“The favoring by AF of the open (or apo) conformation contrasts with a recent survey of 91 apo/holo pairs of AF models that found 67 % of the structures were similar to the holo (ligand-bound) structure (Salda ˜ no et al., 2022).”
Feidakis, C. P., Krivák, R., Hoksza, D., & Novotný, M. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168545 · Cited by 10 · see citing works
Zlobin, A. S., & Golovin, A. V. (2022) · ACS Omega · doi:10.1021/acsomega.2c05201 · Cited by 11 · see citing works
Ferrario, E., Miggiano, R., Rizzi, M., & Ferraris, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.07.027 · Cited by 9 · see citing works
“It has been previously observed that AF favours the prediction of the ligand-bound rather than the ligand-free protein conformations [35].”
BackgroundHow to select the best model from AlphaFold2 structures?Preprint
Takei, Y., & Ishida, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.05.487218 · Cited by 10 · see citing works
“Holo -structures are more likely to be output than apo-structures when multiple prediction models are used[19].”
BackgroundAccelerated sampling of protein dynamics using BioEmu augmented molecular simulationPreprint
Bhakat, S., & Strauch, E.-M. (2026) · bioRxiv · doi:10.64898/2026.01.07.698041 · Cited by 9 · see citing works
Biała‐Leonhard, W., Bigos, A., Brezovský, J., & Jasiński, M. (2025) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiaf146 · Cited by 7 · see citing works
Raisinghani, N., Alshahrani, M., Gupta, G., & Verkhivker, G. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810082 · Cited by 8 · see citing works
Computational design of Periplasmic binding protein biosensors guided by molecular dynamics
O’Shea, J. M., Richardson, A., Doerner, P. W., & Wood, C. W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012212 · Cited by 7 · see citing works
“…Coefficient matrix can be accessed from only the holo crystal structure, and could even be accessed for PBPs with no known crystal structure thanks to structural prediction algorithms such as AlphaFold2, which are known to mostly predict holo states of binding proteins (Saldaño et al. , 2022).”
BackgroundZhao, H.-F., Zhang, H., She, Z., Gao, Z., Wang, Q., Geng, Z. Z., & Dong, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032740 · Cited by 12 · see citing works
“Moreover, AF2 generally provides a single static picture of a protein structure that it thinks is most likely to appear in the PDB, whereas proteins exist in many different conformations, and AF2 currently cannot capture all aspects of a protein’s biological function [61,62].”
BackgroundLi, H., Nithin, C., Kmiecik, S., & Huang, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104382 · Cited by 9 · see citing works
Robust Prediction of Multiple Protein Conformations with Entropy GuidancePreprint
Wu, D. T., & Feng, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.26.650728 · Cited by 7 · see citing works
Li, J., Zhu, Z., & Song, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.09.598121 · Cited by 6 · see citing works
“13 Predicting alternative conformations and assessing the impact of sequence mutations still pose difficulties with standard AF2 usage.”
BackgroundMethodInfluentialTRPA5 encodes a thermosensitive ankyrin ion channel receptor in a triatomine insect
Liénard, M. A., Báez-Nieto, D., Tsai, C., Valencia‐Montoya, W. A., Werin, B., Johanson, U., Lassance, J., Pan, J. Q., Yu, N., & Pierce, N. E. (2024) · iScience · doi:10.1016/j.isci.2024.109541 · Cited by 8 · see citing works
Rueda, A. J. V., Bulgarelli, F. L., Palópoli, N., & Parisi, G. D. (2023) · Database · doi:10.1093/database/baad010 · Cited by 7 · see citing works
Self-citationLombard, V., Van, J. N., Grudinin, S., & Laine, É. (2026) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798326006054 · Cited by 3 · see citing works
“Beyond the single-structure frontier, several studies have underscored the limitations and potential of protein structure predictors (PSP) for generating alternative conformations (Salda˜no et al., 2022; Lane, 2023; Bryant & No´e, 2024; Chakravarty et al., 2025).”
Prediction of structural variation
Kalakoti, Y., Sanjeev, A., & Wallner, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103003 · Cited by 7 · see citing works
Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04985 · Cited by 5 · see citing works
Tang, Y., Yu, M., Bai, G., Li, X., Xu, Y., & Ma, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.27.600251 · Cited by 5 · see citing works
“All these methods are developed and trained as deterministic maps from input (sequence or MSA) to output (structure), making them suboptimal for modeling structural ensembles 9 .”
MethodModeling with Alternate Locations in X-ray Protein Structures
Gutermuth, T., Sieg, J., Stohn, T., & Rarey, M. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00100 · Cited by 6 · see citing works
Sawhney, A., Li, J., & Liao, L. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25105247 · Cited by 6 · see citing works
“Several studies have examined AlphaFold2’s predicted structures, for example to assess the impact of conformational diversity on its predictions [29] or to evaluate if AlphaFold2 learned the physics of folding [30].”
BackgroundExploring the potential of AlphaFold distograms for predicting binding‐induced hinge motions
Savaş, B., Barlas, A. B., & Karaca, E. (2026) · FEBS Letters · doi:10.1002/1873-3468.70297 · Cited by 6 · see citing works
Jurgelaitis, E., Zagorskaitė, E., Kopu̅stas, A., et al. (2025) · Cell Research · doi:10.1038/s41422-025-01198-1 · Cited by 5
“57 Since the beta-relay involves signi fi cant rearrangement of the local structural environment, AlphaFold might be expected to assign lower con fi dence values to the corresponding region.”
Kim, T. D., Pretorius, D., Murray, J. W., & Cardona, T. (2025) · Physiologia Plantarum · doi:10.1111/ppl.70284 · Cited by 4 · see citing works
“Several studies have found that the pLDDT score is a good indicator of the flexibility of the region, with lower values indicating more dynamicity (Guo et al. 2022; Saldano et al. 2022; Ma et al. 2023).”
JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
Daigavane, A., Vani, B. P., Davidson, D. S., Saremi, S., Rackers, J. A., & Kleinhenz, J. (2024) · doi:10.48550/arxiv.2410.14621 · Cited by 5
“Protein structure prediction models tend to struggle to capture this diversity, especially when sampling the conformations of flexible domains [79, 17, 2] and rarer conformations [56] not found in the Protein Data Bank (PDB) [13].”
Drake, L. A., Hahn, A., Dixon, A., et al. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.104869 · Cited by 5 · see citing works
- 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
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2021-10-28
- Volume
- 38
- Issue
- 10
- Pages
- 2742-2748
- ISSN
- 1367-4803 (ISSN-L)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › 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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