On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764

Principles that Govern the Folding of Protein Chains

Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096

Biomolecular condensates: organizers of cellular biochemistry

Banani, S. F., Lee, H. O., Hyman, A. A., & Rosen, M. K. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.7 · Cited by 6,058

Accurate prediction of protein structures and interactions using a 3-track network

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549

Liquid phase condensation in cell physiology and disease

Shin, Y., & Brangwynne, C. P. (2017) · Science · doi:10.1126/science.aaf4382 · Cited by 4,143

CRYSOL– a Program to Evaluate X-ray Solution Scattering of Biological Macromolecules from Atomic Coordinates

Svergun, D. I., Barberato, C., & Koch, M. (1995) · Journal of Applied Crystallography · doi:10.1107/s0021889895007047 · Cited by 3,388

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378

Phase transitions in the assembly of multivalent signalling proteins

Li, P., Banjade, S., Cheng, H., Kim, S., Chen, B., Guo, L., Llaguno, M. C., Hollingsworth, J. V., King, D., Banani, S. F., et al. (2012) · Nature · doi:10.1038/nature10879 · Cited by 2,565

A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins

Wang, J., Choi, J.-M., Holehouse, A., Lee, H. O., Zhang, X., Jahnel, M., Maharana, S., Lemaitre, R., Pozniakovsky, A., Drechsel, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.006 · Cited by 1,876

Phase Transition of a Disordered Nuage Protein Generates Environmentally Responsive Membraneless Organelles

Nott, T. J., Petsalaki, E., Farber, P., Jervis, D., Fussner, E., Plochowietz, A., Craggs, T. D., Bazett-Jones, D. P., Pawson, T., Forman-Kay, J. D., et al. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.01.013 · Cited by 1,803

Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins

Fernández‐Escamilla, A. M., Rousseau, F., Schymkowitz, J. W. H., & Serrano, L. G. (2004) · Nature Biotechnology · doi:10.1038/nbt1012 · Cited by 1,667

Intrinsically Disordered Proteins

Uversky, V. N. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-374984-0.01698-3 · Cited by 1,646

Deep learning in bioinformatics

Min, S., Lee, B., & Yoon, S. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw068 · Cited by 1,559

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434

Improved protein structure prediction using predicted interresidue orientations

Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421

Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions

Simons, K. T., Kooperberg, C. L., Huang, E. S., & Baker, D. A. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0959 · Cited by 1,405

Transcriptional enhancers: from properties to genome-wide predictions

Shlyueva, D., Stampfel, G., & Stark, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3682 · Cited by 1,356

Effective gene expression prediction from sequence by integrating long-range interactions

Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326

Deep mutational scanning: a new style of protein science

Fowler, D. M., & Fields, S. (2014) · Nature Methods · doi:10.1038/nmeth.3027 · Cited by 1,287

Intrinsic Disorder in Cell-signaling and Cancer-associated Proteins

Iakoucheva, L. M., Brown, C. J., Lawson, J. D., Obradović, Z., & Dunker, A. K. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00969-5 · Cited by 1,214

Harnessing protein folding neural networks for peptide–protein docking

Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205

Structural Characterization of Flexible Proteins Using Small-Angle X-ray Scattering

Bernadó, P., Mylonas, E., Petoukhov, M. V., Blackledge, M. J., & Svergun, D. I. (2007) · Journal of the American Chemical Society · doi:10.1021/ja069124n · Cited by 1,172

Biomolecular condensates at the nexus of cellular stress, protein aggregation disease and ageing

Alberti, S., & Hyman, A. A. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-00326-6 · Cited by 1,165

Valence and patterning of aromatic residues determine the phase behavior of prion-like domains

Martin, E. W., Holehouse, A. S., Peran, I., Farag, M., Incicco, J. J., Bremer, A., Grace, C. R., Soranno, A., Pappu, R. V., & Mittag, T. (2020) · Science · doi:10.1126/science.aaw8653 · Cited by 1,147

Protein 3D Structure Computed from Evolutionary Sequence Variation

Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144

Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131

Rationalization of the effects of mutations on peptide andprotein aggregation rates

Chiti, F., Stefani, M., Taddei, N., Ramponi, G., & Dobson, C. M. (2003) · Nature · doi:10.1038/nature01891 · Cited by 1,072

Mechanism of coupled folding and binding of an intrinsically disordered protein

Sugase, K., Dyson, H. J., & Wright, P. E. (2007) · Nature · doi:10.1038/nature05858 · Cited by 1,071

Identification of direct residue contacts in protein–protein interaction by message passing

Weigt, M., White, R. A., Szurmant, H., Hoch, J. A., & Hwa, T. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805923106 · Cited by 1,025

Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues

Das, R. K., & Pappu, R. V. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1304749110 · Cited by 1,008

Developing a molecular dynamics force field for both folded and disordered protein states

Robustelli, P. J., Piana, S., & Shaw, D. (2018) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1800690115 · Cited by 962

Physical Principles Underlying the Complex Biology of Intracellular Phase Transitions

Choi, J., Holehouse, A. S., & Pappu, R. V. (2020) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-121219-081629 · Cited by 908

Pi-Pi contacts are an overlooked protein feature relevant to phase separation

Vernon, R. M., Chong, P. A., Tsang, B., Kim, T. H., Bah, A., Farber, P. J., Lin, H., & Forman-Kay, J. D. (2018) · eLife · doi:10.7554/elife.31486 · Cited by 905

Correlated mutations and residue contacts in proteins

Göbel, U., Sander, C., Schneider, R., & Valencia, A. (1994) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340180402 · Cited by 863

How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization?

Piana, S., Lindorff‐Larsen, K., & Shaw, D. E. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.03.051 · Cited by 798

Generalized-ensemble algorithms for molecular simulations of biopolymers

Mitsutake, A., Sugita, Y., & Okamoto, Y. (2001) · Biopolymers · doi:10.1002/1097-0282(2001)60:2 · Cited by 764

Evaluating Protein Transfer Learning with TAPE

Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719

Phosphorylation of the FUS low‐complexity domain disrupts phase separation, aggregation, and toxicity

Monahan, Z. T., Ryan, V. H., Janke, A. M., Burke, K. A., Rhoads, S. N., Zerze, G. H., O’Meally, R. N., Dignon, G. L., Conicella, A. E., Zheng, W., et al. (2017) · The EMBO Journal · doi:10.15252/embj.201696394 · Cited by 692

Critical assessment of methods of protein structure prediction (CASP) — round x

Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24452 · Cited by 687

SHIFTX2: significantly improved protein chemical shift prediction

Han, B., Liu, Y., Ginzinger, S., & Wishart, D. S. (2011) · Journal of Biomolecular NMR · doi:10.1007/s10858-011-9478-4 · Cited by 685

Biomolecular Phase Separation: From Molecular Driving Forces to Macroscopic Properties

Dignon, G. L., Best, R. B., & Mittal, J. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-071819-113553 · Cited by 657

Phase Separation of FUS Is Suppressed by Its Nuclear Import Receptor and Arginine Methylation

Hofweber, M., Hutten, S., Bourgeois, B. M. R., Spreitzer, E., Niedner, A., Schifferer, M., Ruepp, M., Simons, M., Niessing, D., Madl, T., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.004 · Cited by 654

Extreme disorder in an ultrahigh-affinity protein complex

Borgia, A., Borgia, M. B., Bugge, K., Kissling, V. M., Heidarsson, P. O., Fernandes, C. B., Sottini, A., Soranno, A., Buholzer, K. J., Nettels, D., et al. (2018) · Nature · doi:10.1038/nature25762 · Cited by 653

Sequence determinants of protein phase behavior from a coarse-grained model

Dignon, G., Zheng, W.-W., Kim, Y. C., Best, R., & Mittal, J. (2017) · bioRxiv · doi:10.1371/journal.pcbi.1005941 · Cited by 621

Intrinsically disordered proteins: regulation and disease

Babu, M. M., van der Lee, R., de Groot, N. S., & Gsponer, J. A. (2011) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2011.03.011 · Cited by 620

Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners

Oldfield, C. J., Meng, J., Yang, J. Y., Yang, M. Q., Uversky, V. N., & Dunker, A. K. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-s1-s1 · Cited by 608

Modeling aspects of the language of life through transfer-learning protein sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3220-8 · Cited by 606

Modeling the language of life – Deep Learning Protein Sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606

Deep generative models of genetic variation capture the effects of mutations

Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601

Widespread macromolecular interaction perturbations in human genetic disorders

Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.013 · Cited by 594

Prediction of “Aggregation-prone” and “Aggregation-susceptible” Regions in Proteins Associated with Neurodegenerative Diseases

Pawar, A. P., DuBay, K. F., Zurdo, J., Chiti, F., Vendruscolo, M., & Dobson, C. M. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.04.016 · Cited by 593

Phase-separation mechanism for C-terminal hyperphosphorylation of RNA polymerase II

Lu, H., Yu, D., Hansen, A. S., Ganguly, S., Liu, R., Heckert, A. B., Darzacq, X., & Zhou, Q. (2018) · Nature · doi:10.1038/s41586-018-0174-3 · Cited by 576

Building Force Fields: An Automatic, Systematic, and Reproducible Approach

Wang, L., Martı́nez, T. J., & Pande, V. S. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz500737m · Cited by 563

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

Shen, Y., & Bax, A. (2010) · Journal of Biomolecular NMR · doi:10.1007/s10858-010-9433-9 · Cited by 557

A large‐scale experiment to assess protein structure prediction methods

Moult, J., Pedersen, J. T., Judson, R., & Fidelis, K. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340230303 · Cited by 555

A Million Peptide Motifs for the Molecular Biologist

Tompa, P., Davey, N. E., Gibson, T. J., & Babu, M. M. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.05.032 · Cited by 515

Short Linear Motifs: Ubiquitous and Functionally Diverse Protein Interaction Modules Directing Cell Regulation

Roey, K. V., Uyar, B., Weatheritt, R. J., Dinkel, H., Seiler, M., Budd, A., Gibson, T. J., & Davey, N. E. (2014) · Chemical Reviews · doi:10.1021/cr400585q · Cited by 513

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25823 · Cited by 506

Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains

Bremer, A., Farag, M., Borcherds, W. M., Peran, I., Martin, E. W., Pappu, R. V., & Mittag, T. (2021) · Nature Chemistry · doi:10.1038/s41557-021-00840-w · Cited by 489

Learning the protein language: Evolution, structure, and function

Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483

Polymer scaling laws of unfolded and intrinsically disordered proteins quantified with single-molecule spectroscopy

Hofmann, H., Soranno, A., Borgia, A., Gast, K., Nettels, D., & Schuler, B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207719109 · Cited by 475

Mutual synergistic folding in recruitment of CBP/p300 by p160 nuclear receptor coactivators

Demarest, S. J., Martinez‐Yamout, M. A., Chung, J., Chen, H.-W., Xu, W., Dyson, H. J., Evans, R. M., & Wright, P. E. (2002) · Nature · doi:10.1038/415549a · Cited by 466

Rewiring the Specificity of Two-Component Signal Transduction Systems

Skerker, J. M., Perchuk, B. S., Siryaporn, A., Lubin, E. A., Ashenberg, O., Goulian, M., & Laub, M. T. (2008) · Cell · doi:10.1016/j.cell.2008.04.040 · Cited by 464

Single-sequence protein structure prediction using a language model and deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455

Single-sequence protein structure prediction using language models from deep learning

Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455

ELM--the database of eukaryotic linear motifs

Dinkel, H., Michael, S., Weatheritt, R. J., Davey, N. E., Roey, K. V., Altenberg, B., Toedt, G., Uyar, B., Seiler, M., Budd, A., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1064 · Cited by 440

Acid blobs and negative noodles

Sigler, P. B. (1988) · Nature · doi:10.1038/333210a0 · Cited by 438

Atomic-level characterization of disordered protein ensembles

Mittag, T., & Forman-Kay, J. D. (2007) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2007.01.009 · Cited by 436

A structural model for unfolded proteins from residual dipolar couplings and small-angle x-ray scattering

Bernadó, P., Blanchard, L., Timmins, P. A., Marion, D., Ruigrok, R. W. H., & Blackledge, M. J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506202102 · Cited by 436

Multiplex assessment of protein variant abundance by massively parallel sequencing

Matreyek, K. A., Starita, L. M., Stephany, J. J., Martin, B., Chiasson, M. A., Gray, V. E., Kircher, M., Khechaduri, A., Dines, J. N., Hause, R. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0122-z · Cited by 436

Relation between single-molecule properties and phase behavior of intrinsically disordered proteins

Dignon, G. L., Zheng, W., Best, R. B., Kim, Y. C., & Mittal, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804177115 · Cited by 413

Distance-based protein folding powered by deep learning

Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401

Cancer Mutations of the Tumor Suppressor SPOP Disrupt the Formation of Active, Phase-Separated Compartments

Bouchard, J. J., Otero, J. H., Scott, D. C., Szulc, E., Martin, E. W., Sabri, N., Granata, D., Marzahn, M. R., Lindorff‐Larsen, K., Salvatella, X., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.08.027 · Cited by 387

Analysis of Main Chain Torsion Angles in Proteins: Prediction of NMR Coupling Constants for Native and Random Coil Conformations

Smith, L. J., Bolin, K. A., Schwalbe, H., MacArthur, M. W., Thornton, J. M., & Dobson, C. M. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0041 · Cited by 385

Sequence Determinants of Compaction in Intrinsically Disordered Proteins

Marsh, J. A., & Forman-Kay, J. D. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.02.006 · Cited by 382

Friend or foe—Post-translational modifications as regulators of phase separation and RNP granule dynamics

Hofweber, M., & Dormann, D. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.tm118.001189 · Cited by 382

Variant interpretation: functional assays to the rescue

Starita, L. M., Ahituv, N., Dunham, M. J., Kitzman, J. O., Roth, F. P., Seelig, G., Shendure, J., & Fowler, D. M. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.07.014 · Cited by 381

End-to-End Differentiable Learning of Protein Structure

AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371

Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology

Shen, Y., & Bax, A. (2007) · Journal of Biomolecular NMR · doi:10.1007/s10858-007-9166-6 · Cited by 362

Widespread occurrence of the droplet state of proteins in the human proteome

Hardenberg, M. C., Horváth, A., Ambrus, V., Fuxreiter, M., & Vendruscolo, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007670117 · Cited by 362

The language of proteins: NLP, machine learning & protein sequences

Ofer, D., Brandes, N., & Linial, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.022 · Cited by 361

Systematic Discovery of New Recognition Peptides Mediating Protein Interaction Networks

Neduva, V., Linding, R., Su-Angrand, I., Stark, A., Masi, F. D., Gibson, T. J., Lewis, J., Serrano, L., & Russell, R. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030405 · Cited by 359

Principles of protein structural ensemble determination

Bonomi, M., Heller, G. T., Camilloni, C., & Vendruscolo, M. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.12.004 · Cited by 354

Inferential Structure Determination

Rieping, W., Habeck, M., & Nilges, M. (2005) · Science · doi:10.1126/science.1110428 · Cited by 351

Molecular Mechanisms of Disease-Causing Missense Mutations

Stefl, S. K., Nishi, H., Petukh, M. G., Panchenko, A. R., & Alexov, E. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.014 · Cited by 345

Critical assessment of protein intrinsic disorder prediction

Necci, M., Piovesan, D., Predictors, C., Hoque, M. T., Walsh, I., Iqbal, S., Vendruscolo, M., Sormanni, P., Wang, C., Raimondi, D., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01117-3 · Cited by 343

Phase Separation: Linking Cellular Compartmentalization to Disease

Aguzzi, A., & Altmeyer, M. (2016) · Trends in Cell Biology · doi:10.1016/j.tcb.2016.03.004 · Cited by 333

Biomolecular Condensates and Cancer

Boija, A., Klein, I. A., & Young, R. A. (2021) · Cancer Cell · doi:10.1016/j.ccell.2020.12.003 · Cited by 333

The Eukaryotic Proteome Is Shaped by E3 Ubiquitin Ligases Targeting C-Terminal Degrons

Koren, I., Timms, R. T., Kula, T., Xu, Q., Li, M. Z., & Elledge, S. J. (2018) · Cell · doi:10.1016/j.cell.2018.04.028 · Cited by 328

Learning generative models for protein fold families

Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316

HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins 1 1Edited by J. Thornton

Bystroff, C., Thórsson, V., & Baker, D. A. (2000) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.3837 · Cited by 312

A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins

Sawle, L., & Ghosh, K. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4929391 · Cited by 309

Deep learning methods in protein structure prediction

Torrisi, M., Pollastri, G., & Le, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.011 · Cited by 297

Unfoldomics of human diseases: linking protein intrinsic disorder with diseases

Uversky, V. N., Oldfield, C. J., Midic, U., Xie, H. M., Xue, B., Vučetić, S., Iakoucheva, L. M., Zoran Obradovic, A., & Dunker, A. K. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-s1-s7 · Cited by 283

Phase Separation as a Missing Mechanism for Interpretation of Disease Mutations

Tsang, B., Pritišanac, I., Scherer, S. W., Moses, A. M., & Forman-Kay, J. D. (2020) · Cell · doi:10.1016/j.cell.2020.11.050 · Cited by 281

Phase Separation and Single-Chain Compactness of Charged Disordered Proteins Are Strongly Correlated

Lin, Y., & Chan, H. S. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.04.021 · Cited by 280

ELM—the eukaryotic linear motif resource in 2020

Kumar, M., Gouw, M., Michael, S., Sámano‐Sánchez, H., Pancsa, R., Glavina, J., Diakogianni, A., Alvarado-Valverde, J., Bukirova, D., Čalyševa, J., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1030 · Cited by 278

Automated prediction of 15N, 13Cα, 13Cβ and 13C′ chemical shifts in proteins using a density functional database

Xu, X.-P., & Case, D. A. (2001) · Journal of Biomolecular NMR · doi:10.1023/a:1013324104681 · Cited by 269

Fast and Accurate Predictions of Protein NMR Chemical Shifts from Interatomic Distances

Kohlhoff, K., Robustelli, P., Cavalli, A., Salvatella, X., & Vendruscolo, M. (2009) · Journal of the American Chemical Society · doi:10.1021/ja903772t · Cited by 264

Machine learning in protein structure prediction

AlQuraishi, M. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.005 · Cited by 263

Allostery and Intrinsic Disorder Mediate Transcription Regulation by Conditional Cooperativity

Garcia‐Pino, A., Balasubramanian, S., Wyns, L., Gazit, E., Greve, H. D., Magnuson, R. D., Charlier, D., van Nuland, N. A. J., & Loris, R. (2010) · Cell · doi:10.1016/j.cell.2010.05.039 · Cited by 259

Conformational propensities of intrinsically disordered proteins influence the mechanism of binding and folding

Arai, M., Sugase, K., Dyson, H. J., & Wright, P. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1512799112 · Cited by 250

Matter over mind: Liquid phase separation and neurodegeneration

Elbaum‐Garfinkle, S. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.rev118.001188 · Cited by 247

How frequent are correlated changes in families of protein sequences?

Neher, E. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.1.98 · Cited by 240

Fuzzy Complexes: A More Stochastic View of Protein Function

Fuxreiter, M., & Tompa, P. (2012) · Advances in Experimental Medicine and Biology · doi:10.1007/978-1-4614-0659-4_1 · Cited by 240

Intrinsically Disordered Segments Affect Protein Half-Life in the Cell and during Evolution

van der Lee, R., Lang, B., Kruse, K., Gsponer, J. A., de Groot, N. S., Huynen, M. A., Matouschek, A. T., Fuxreiter, M., & Babu, M. M. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.07.055 · Cited by 233

Learning the molecular grammar of protein condensates from sequence determinants and embeddings

Saar, K. L., Morgunov, A. S., Qi, R., Arter, W. E., Krainer, G., Lee, A. A., & Knowles, T. P. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2019053118 · Cited by 232

DisProt: intrinsic protein disorder annotation in 2020

Hatos, A., Hajdu-Soltész, B., Monzón, A. M., Palópoli, N., Álvarez, L., Fas, B. A., Bassot, C., Benítez, G. I., Bevilacqua, M., Chasapi, A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz975 · Cited by 231

Describing intrinsically disordered proteins at atomic resolution by NMR

Jensen, M. R., Ruigrok, R. W. H., & Blackledge, M. J. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.02.007 · Cited by 230

Short linear motifs – ex nihilo evolution of protein regulation

Davey, N. E., Cyert, M., & Moses, A. M. (2015) · Cell Communication and Signaling · doi:10.1186/s12964-015-0120-z · Cited by 228

Deep Learning in Protein Structural Modeling and Design

Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Gkeka, P., Stoltz, G., Farimani, A. B., Belkacemi, Z., Ceriotti, M., Chodera, J. D., Dinner, A. R., Ferguson, A. L., Maillet, J., Minoux, H., et al. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00355 · Cited by 221

Coarse graining molecular dynamics with graph neural networks

Husic, B. E., Charron, N. E., Lemm, D., Wang, J., Pérez, A., Majewski, M., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G. D., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0026133 · Cited by 220

PhaSepDB: a database of liquid–liquid phase separation related proteins

You, K., Huang, Q., Yu, C., Shen, B., Sevilla, C., Shi, M., Hermjakob, H., Chen, Y., & Li, T. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz847 · Cited by 219

MobiDB: intrinsically disordered proteins in 2021

Piovesan, D., Necci, M., Escobedo, N., Monzón, A. M., Hatos, A., Mičetić, I., Quaglia, F., Paladin, L., Ramasamy, P., Dosztányi, Z., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1058 · Cited by 218

Deep neural network processing of DEER data

Worswick, S. G., Spencer, J. A., Jeschke, G., & Kuprov, I. (2018) · Science Advances · doi:10.1126/sciadv.aat5218 · Cited by 212

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Zarin, T., Strome, B., Ba, A. N. N., Alberti, S., Forman-Kay, J. D., & Moses, A. M. (2019) · eLife · doi:10.7554/elife.46883 · Cited by 212

Intrinsically disordered proteins and their (disordered) proteomes in neurodegenerative disorders

Uversky, V. N. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00018 · Cited by 211

Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water

Riback, J. A., Bowman, M. A., Zmyslowski, A. M., Knoverek, C. R., Jumper, J., Hinshaw, J. R., Kaye, E. B., Freed, K. F., Clark, P. L., & Sosnick, T. R. (2017) · Science · doi:10.1126/science.aan5774 · Cited by 210

Deep Dive into Machine Learning Models for Protein Engineering

Xu, Y., Verma, D., Sheridan, R. P., Liaw, A. I., Ma, J., Marshall, N., McIntosh, J. A., Sherer, E. C., Svetnik, V., & Johnston, J. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00073 · Cited by 210

Computational and theoretical advances in studies of intrinsically disordered proteins

Best, R. B. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.01.006 · Cited by 208

Deep‐learning contact‐map guided protein structure prediction in CASP13

Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208

14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides

Madeira, F., Tinti, M., Murugesan, G., Berrett, E., Stafford, M. J., Toth, R. L., Cole, C., MacKintosh, C., & Barton, G. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv133 · Cited by 207

NMR-based protein potentials.

Li, D.-W., & Brüschweiler, R. (2010) · Angewandte Chemie · doi:10.1002/anie.201001898 · Cited by 204

The Acidic Transcription Activator Gcn4 Binds the Mediator Subunit Gal11/Med15 Using a Simple Protein Interface Forming a Fuzzy Complex

Brzovic, P. S., Heikaus, C. C., Kisselev, L., Vernon, R., Herbig, E., Pacheco, D., Warfield, L., Littlefield, P., Baker, D., Klevit, R. E., et al. (2011) · Molecular Cell · doi:10.1016/j.molcel.2011.11.008 · Cited by 203

DrLLPS: a data resource of liquid–liquid phase separation in eukaryotes

Ning, W., Guo, Y., Lin, S., Mei, B., Wu, Y. Z., Jiang, P., Tan, X., Zhang, W., Chen, G., Peng, D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1027 · Cited by 202

Decoupling of size and shape fluctuations in heteropolymeric sequences reconciles discrepancies in SAXS vs. FRET measurements

Fuertes, G., Banterle, N., Ruff, K. M., Chowdhury, A., Mercadante, D., Koehler, C., Kachala, M., Girona, G. E., Milles, S., Mishra, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1704692114 · Cited by 198

PhaSePro: the database of proteins driving liquid–liquid phase separation

Mészáros, B., Erdős, G., Szabó, B., Schád, É., Tantos, Á., Abukhairan, R., Horváth, T., Murvai, N., Kovacs, O. P., Kovács, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz848 · Cited by 195

LLPSDB: a database of proteins undergoing liquid–liquid phase separation in vitro

Li, Q., Peng, X., Li, Y., Tang, W., Zhu, J., Huang, J., Qi, Y., & Zhang, Z. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz778 · Cited by 192

PROSHIFT: Protein chemical shift prediction using artificial neural networks

Meiler, J. (2003) · Journal of Biomolecular NMR · doi:10.1023/a:1023060720156 · Cited by 191

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

Burger, L., & van Nimwegen, E. (2008) · Molecular Systems Biology · doi:10.1038/msb4100203 · Cited by 191

Functional advantages of dynamic protein disorder

Berlow, R. B., Dyson, H. J., & Wright, P. E. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.06.003 · Cited by 184

Model for disordered proteins with strongly sequence-dependent liquid phase behavior

Statt, A., Casademunt, H., Brangwynne, C. P., & Panagiotopoulos, A. Z. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5141095 · Cited by 184

Phase Equilibria of Lattice Polymers from Histogram Reweighting Monte Carlo Simulations

Panagiotopoulos, A. Z., Wong, V., & Floriano, M. A. (1998) · Macromolecules · doi:10.1021/ma971108a · Cited by 178

Force field development and simulations of intrinsically disordered proteins

Huang, J., & MacKerell, A. D. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.008 · Cited by 173

Comparison between the φ Distribution of the Amino Acids in the Protein Database and NMR Data Indicates that Amino Acids have Various φ Propensities in the Random Coil Conformation

Serrano, L. (1995) · Journal of Molecular Biology · doi:10.1006/jmbi.1995.0619 · Cited by 170

High‐throughput discovery of functional disordered regions: investigation of transactivation domains

Ravarani, C. N. J., Erkina, T. Y., Baets, G. D., Dudman, D. C., Erkine, A. M., & Babu, M. M. (2018) · Molecular Systems Biology · doi:10.15252/msb.20188190 · Cited by 168

Biophysical and Mechanistic Models for Disease-Causing Protein Variants

Stein, A., Fowler, D. M., Hartmann‐Petersen, R., & Lindorff‐Larsen, K. (2019) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.01.003 · Cited by 168

Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator

Sanborn, A. L., Yeh, B. T., Feigerle, J. T., Hao, C. V., Townshend, R. J. L., Aiden, E. L., Dror, R. O., & Kornberg, R. D. (2021) · eLife · doi:10.7554/elife.68068 · Cited by 168

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain

Staller, M. V., Holehouse, A. S., Swain-Lenz, D., Das, R. K., Pappu, R. V., & Cohen, B. A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.01.015 · Cited by 165

The mutational landscape of a prion-like domain

Bolognesi, B., Faure, A. J., Seuma, M., Schmiedel, J. M., Tartaglia, G. G., & Lehner, B. (2019) · Nature Communications · doi:10.1038/s41467-019-12101-z · Cited by 165

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations

Livesey, B., & Marsh, J. A. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199380 · Cited by 164

Helical Propensity in an Intrinsically Disordered Protein Accelerates Ligand Binding

Iešmantavičius, V., Dogan, J., Jemth, P., Teilum, K., & Kjærgaard, M. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201307712 · Cited by 162

NMR‐Based Protein Potentials

Li, D., & Brüschweiler, R. (2010) · Angewandte Chemie · doi:10.1002/ange.201001898 · Cited by 161

Hypersensitive termination of the hypoxic response by a disordered protein switch

Berlow, R. B., Dyson, H. J., & Wright, P. E. (2017) · Nature · doi:10.1038/nature21705 · Cited by 161

An introduction to deep learning on biological sequence data: examples and solutions

Jurtz, V. I., Johansen, A. R., Nielsen, M., Armenteros, J. J. A., Nielsen, H., Sønderby, C. K., Winther, O., & Sønderby, S. K. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx531 · Cited by 161

Contextual Specificity in Peptide-Mediated Protein Interactions

Stein, A., & Aloy, P. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002524 · Cited by 160

A generative, probabilistic model of local protein structure

Boomsma, W., Mardia, K. V., Taylor, C. C., Ferkinghoff‐Borg, J., Krogh, A., & Hamelryck, T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801715105 · Cited by 159

Disease-Associated Mutations Disrupt Functionally Important Regions of Intrinsic Protein Disorder

Vacic, V., Markwick, P. R. L., Oldfield, C. J., Zhao, X., Haynes, C. A., Uversky, V. N., & Iakoucheva, L. M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002709 · Cited by 157

Interactions by disorder – A matter of context

Bugge, K., Brakti, I., Fernandes, C. B., Dreier, J. E., Lundsgaard, J. E., Olsen, J. G., Skriver, K., & Kragelund, B. B. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00110 · Cited by 155

Compensating changes in protein multiple sequence alignments

Taylor, W. R., & Hatrick, K. (1994) · Protein Engineering Design and Selection · doi:10.1093/protein/7.3.341 · Cited by 152

High-Throughput Discovery and Characterization of Human Transcriptional Effectors

Tycko, J., DelRosso, N. V., Hess, G. T., Aradhana, Banerjee, A., Mukund, A. X., Van, M. V., Ego, B. K., Yao, D., Spees, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.024 · Cited by 152

Coarse graining molecular dynamics with graph neural networks

Husic, B. E., Charron, N. E., Lemm, D., Wang, J., Pérez, A., Majewski, M., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G. D., et al. (2020) · Refubium (Universitätsbibliothek der Freien Universität Berlin) · doi:10.17169/refubium-29706 · Cited by 151

Molecular structure in biomolecular condensates

Peran, I., & Mittag, T. (2019) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.09.007 · Cited by 149

Effective concentrations enforced by intrinsically disordered linkers are governed by polymer physics

Sørensen, C. S., & Kjærgaard, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1904813116 · Cited by 148

Structural basis of recognition of interferon-α receptor by tyrosine kinase 2

Wallweber, H. J. A., Tam, C., Franke, Y., Starovasnik, M. A., & Lupardus, P. J. (2014) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2807 · Cited by 146

The most important thing is the tail: Multitudinous functionalities of intrinsically disordered protein termini

Uversky, V. N. (2013) · FEBS Letters · doi:10.1016/j.febslet.2013.04.042 · Cited by 144

Physics-based computational and theoretical approaches to intrinsically disordered proteins

Shea, J., Best, R. B., & Mittal, J. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.12.012 · Cited by 144

Hydropathy Patterning Complements Charge Patterning to Describe Conformational Preferences of Disordered Proteins

Zheng, W., Dignon, G. L., Brown, M., Kim, Y. C., & Mittal, J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00288 · Cited by 143

PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins

Lazar, T., Martínez-Pérez, E., Quaglia, F., Hatos, A., Chemes, L. B., Iserte, J. A., Méndez, N. A., Garrone, N. A., Saldaño, T. E., Marchetti, J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1021 · Cited by 142

Intrinsically disordered proteins and structured proteins with intrinsically disordered regions have different functional roles in the cell

Deiana, A., Forcelloni, S., Porrello, A., & Giansanti, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217889 · Cited by 140

The binding mechanisms of intrinsically disordered proteins

Dogan, J., Gianni, S., & Jemth, P. (2013) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp54226b · Cited by 139

Large-scale interaction profiling of PDZ domains through proteomic peptide-phage display using human and viral phage peptidomes

Ivarsson, Y., Arnold, R., McLaughlin, M., Nim, S., Joshi, R., Ray, D., Liu, B., Teyra, J., Pawson, T., Moffat, J., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312296111 · Cited by 135

A Data-Driven Hydrophobicity Scale for Predicting Liquid–Liquid Phase Separation of Proteins

Dannenhoffer-Lafage, T. P., & Best, R. B. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c11479 · Cited by 135

Recent Force Field Strategies for Intrinsically Disordered Proteins

Mu, J., Liu, H., Zhang, J., Luo, R., & Chen, H. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01175 · Cited by 134

Mutations in Disordered Regions Can Cause Disease by Creating Dileucine Motifs

Meyer, K., Kirchner, M., Uyar, B., Cheng, J.-Y., Russo, G., Hernandez-Miranda, L. R., Szymborska, A., Zauber, H., Rudolph, I.-M., Willnow, T. E., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.019 · Cited by 132

Behaviour of intrinsically disordered proteins in protein–protein complexes with an emphasis on fuzziness

Olsen, J. G., Teilum, K., & Kragelund, B. B. (2017) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-017-2560-7 · Cited by 129

Factors controlling relative stability of anomers and hydroxymethyl conformers of glucopyranose

Barrows, S. E., Storer, J. W., Cramer, C. J., French, A. D., & Truhlar, D. G. (1998) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(19980730)19:10 · Cited by 127

Structured States of Disordered Proteins from Genomic Sequences

Tóth-Petróczy, Á., Palmedo, P., Ingraham, J., Hopf, T. A., Berger, B., Sander, C., & Marks, D. S. (2016) · Cell · doi:10.1016/j.cell.2016.09.010 · Cited by 124

A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning

Erijman, A., Kozlowski, L. P., Sohrabi-Jahromi, S., Fishburn, J., Warfield, L., Schreiber, J. M., Noble, W. S., Söding, J., & Hahn, S. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.04.020 · Cited by 122

Circular Dichroism Techniques for the Analysis of Intrinsically Disordered Proteins and Domains

Chemes, L. B., Alonso, L. G., Noval, M. G., & Gay, G. D. P. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-927-3_22 · Cited by 120

Predicting protein condensate formation using machine learning

van Mierlo, G., Jansen, J. R. G., Wang, J., Poser, I., van Heeringen, S. J., & Vermeulen, M. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108705 · Cited by 118

Conformational buffering underlies functional selection in intrinsically disordered protein regions

González-Foutel, N. S., Glavina, J., Borcherds, W. M., Safranchik, M., Barrera-Vilarmau, S., Sagar, A., Estaña, A., Barozet, A., Garrone, N. A., Fernandez-Ballester, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00811-w · Cited by 118

Intrinsically disordered proteins and biomolecular condensates as drug targets

Biesaga, M., Frigolé-Vivas, M., & Salvatella, X. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.02.009 · Cited by 115

Self-assembly of biological macromolecules

Perham, R. N. (1975) · Philosophical transactions of the Royal Society of London. Series B, Biological sciences · doi:10.1098/rstb.1975.0075 · Cited by 114

Coupled Folding and Binding of the Disordered Protein PUMA Does Not Require Particular Residual Structure

Rogers, J. M., Wong, C. T., & Clarke, J. (2014) · Journal of the American Chemical Society · doi:10.1021/ja4125065 · Cited by 113

Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance

Cagiada, M., Johansson, K. E., Valančiūtė, A., Nielsen, S. V., Hartmann‐Petersen, R., Yang, J. J. J., Fowler, D. M., Stein, A., & Lindorff‐Larsen, K. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab095 · Cited by 113

The PCNA interaction motifs revisited: thinking outside the PIP-box

Prestel, A., Wichmann, N., Martins, J. M. D. S., Marabini, R., Kassem, N., Broendum, S. S., Otterlei, M., Nielsen, O., Willemoës, M., Ploug, M., et al. (2019) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-019-03150-0 · Cited by 112

Maximum Entropy Optimized Force Field for Intrinsically Disordered Proteins

Latham, A. P., & Zhang, B. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00932 · Cited by 112

A Machine Learning Protocol for Predicting Protein Infrared Spectra

Ye, S., Zhong, K., Zhang, J., Hu, W., Hirst, J. D., Zhang, G., Mukamel, S., & Jiang, J. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c06530 · Cited by 112

Identifying molecular features that are associated with biological function of intrinsically disordered protein regions

Zarin, T., Strome, B., Peng, G., Pritišanac, I., Forman-Kay, J. D., & Moses, A. M. (2021) · eLife · doi:10.7554/elife.60220 · Cited by 110

Biophysical prediction of protein–peptide interactions and signaling networks using machine learning

Cunningham, J. M., Rogers, J. R., Koytiger, G., Sorger, P. K., & AlQuraishi, M. (2020) · Nature Methods · doi:10.1038/s41592-019-0687-1 · Cited by 108

Dif1 Is a DNA-Damage-Regulated Facilitator of Nuclear Import for Ribonucleotide Reductase

Lee, Y. D., Wang, J., Stubbe, J., & Elledge, S. J. (2008) · Molecular Cell · doi:10.1016/j.molcel.2008.08.018 · Cited by 107

Mechanism of Coupled Folding-upon-Binding of an Intrinsically Disordered Protein

Robustelli, P., Piana, S., & Shaw, D. E. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c03217 · Cited by 106

Structure of a Functional Amyloid Protein Subunit Computed Using Sequence Variation

Tian, P., Boomsma, W., Wang, Y., Otzen, D. E., Jensen, M. H., & Lindorff‐Larsen, K. (2014) · Journal of the American Chemical Society · doi:10.1021/ja5093634 · Cited by 105

PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles

Li, D., & Brüschweiler, R. (2012) · Journal of Biomolecular NMR · doi:10.1007/s10858-012-9668-8 · Cited by 104

Machine Learning Methods for X-Ray Scattering Data Analysis from Biomacromolecular Solutions

Franke, D., Jeffries, C. M., & Svergun, D. I. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.04.018 · Cited by 103

Interpreting solution X-ray scattering data using molecular simulations

Hub, J. S. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.11.002 · Cited by 101

Multi-Timescale Dynamics in Intrinsically Disordered Proteins from NMR Relaxation and Molecular Simulation.

Salvi, N., Abyzov, A., & Blackledge, M. (2016) · Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6b00885 · Cited by 100

Genetically tunable frustration controls allostery in an intrinsically disordered transcription factor

Li, J., White, J. T., Saavedra, H. G., Wrabl, J. O., Motlagh, H. N., Liu, K., Sowers, J. R., Schroer, T. A., Thompson, E. B., & Hilser, V. J. (2017) · eLife · doi:10.7554/elife.30688 · Cited by 98

Biomolecular Condensates and Their Links to Cancer Progression

Cai, D., Liu, Z. J., & Lippincott‐Schwartz, J. (2021) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2021.01.002 · Cited by 97

Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Larsen, A. H., Wang, Y., Bottaro, S., Grudinin, S., Arleth, L., & Lindorff‐Larsen, K. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007870 · Cited by 96

Experimental Parameterization of an Energy Function for the Simulation of Unfolded Proteins

Norgaard, A. B., Ferkinghoff‐Borg, J., & Lindorff‐Larsen, K. (2007) · Biophysical Journal · doi:10.1529/biophysj.107.108241 · Cited by 95

Deep mutational scanning reveals the structural basis for α-synuclein activity

Newberry, R. W., Leong, J. T., Chow, E. D., Kampmann, M., & DeGrado, W. F. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0480-6 · Cited by 95

Correlating disease-related mutations to their effect on protein stability: A large-scale analysis of the human proteome

Casadio, R., Vassura, M., Tiwari, S., Fariselli, P., & Martelli, P. L. (2011) · Human Mutation · doi:10.1002/humu.21555 · Cited by 94

SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures

Nagy, G., Igaev, M., Jones, N. C., Hoffmann, S. V., & Grubmüller, H. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00203 · Cited by 94

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

Ruff, K. M., Harmon, T. S., & Pappu, R. V. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4935066 · Cited by 93

Binding without folding – the biomolecular function of disordered polyelectrolyte complexes

Schuler, B., Borgia, A., Borgia, M. B., Heidarsson, P. O., Holmstrom, E. D., Nettels, D., & Sottini, A. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.006 · Cited by 91

Predicting chemical shifts with graph neural networks

Yang, Z., Chakraborty, M., & White, A. D. (2021) · Chemical Science · doi:10.1039/d1sc01895g · Cited by 91

How to learn from inconsistencies: Integrating molecular simulations with experimental data

Orioli, S., Larsen, A. H., Bottaro, S., & Lindorff‐Larsen, K. (2020) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2019.12.006 · Cited by 90

Conformational ensembles of the human intrinsically disordered proteome

Tesei, G., Trolle, A. I., Jonsson, N., Betz, J., Knudsen, F. E., Pesce, F., Johansson, K. E., & Lindorff‐Larsen, K. (2024) · Nature · doi:10.1038/s41586-023-07004-5 · Cited by 331

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14382-9 · Cited by 248

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240

AlphaFold2: A Role for Disordered Protein/Region Prediction?

Wilson, C. J., Choy, W.-Y., & Karttunen, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094591 · Cited by 173

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155

Bioinformatics Approaches to the Structure and Function of Intrinsically Disordered Proteins

Dosztányi, Z., & Tompa, P. (2017) · Springer eBooks · doi:10.1007/978-94-024-1069-3_6 · Cited by 10

Prediction of Intrinsically Disordered Protein Regions using Machine Learning Methods

Nguyen, H. (2021) · ScholarWorks @ The University of New Orleans (The University of New Orleans)

Identification of intrinsic disorder in complexes from Protein Data Bank

Jianhong, Z., Oldfield, C. J., Huang, F., Yan, W., Shen, B., & Dunker, A. K. (2018) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2018.8541994 · Cited by 2

Computational approaches for inferring the functions of intrinsically disordered proteins

Váradi, M., Vranken, W., Guharoy, M., & Tompa, P. (2015) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2015.00045 · Cited by 44

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

Aupič, J., Pokorná, P., Ruthstein, S., & Magistrato, A. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c01544 · Cited by 18