The Protein-Folding Problem, 50 Years On

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

SCOP: A structural classification of proteins database for the investigation of sequences and structures

Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,445

Prions

Prusiner, S. B. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.23.13363 · Cited by 5,373

Protein folding and misfolding

Dobson, C. M. (2003) · Nature · doi:10.1038/nature02261 · Cited by 4,791

Dominant forces in protein folding

Dill, K. A. (1990) · Biochemistry · doi:10.1021/bi00483a001 · Cited by 3,829

The HHpred interactive server for protein homology detection and structure prediction

Söding, J., Biegert, A., & Lupas, A. N. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki408 · Cited by 3,775

The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain

Pauling, L. C., Corey, R. B., & Branson, H. R. (1951) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.37.4.205 · Cited by 3,007

Funnels, pathways, and the energy landscape of protein folding: A synthesis

Bryngelson, J. D., Onuchic, J. N., Socci, N. D., & Wolynes, P. G. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340210302 · Cited by 2,648

Foldamers: A Manifesto

Gellman, S. H. (1998) · Accounts of Chemical Research · doi:10.1021/ar960298r · Cited by 2,550

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

Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials

Boles, M. A., Engel, M., & Talapin, D. V. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00196 · Cited by 2,095

HH-suite3 for fast remote homology detection and deep protein annotation

Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3019-7 · Cited by 1,145

DNA Origami: Scaffolds for Creating Higher Order Structures

Fan, H. B., Zhang, F., Liu, Y., & Yan, H. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00825 · Cited by 1,108

Coarse-Grained Protein Models and Their Applications

Kmiecik, S., Gront, D., Koliński, M., Wieteska, Ł., Badaczewska-Dawid, A. E., & Koliński, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,113

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Smith, J. S., Nebgen, B. T., Zubatyuk, R. I., Lubbers, N., Devereux, C., Barros, K. M., Tretiak, S., Isayev, O., & Roitberg, A. E. (2019) · Nature Communications · doi:10.1038/s41467-019-10827-4 · Cited by 702

Native contacts determine protein folding mechanisms in atomistic simulations

Best, R. B., Hummer, G., & Eaton, W. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311599110 · Cited by 593

AlphaFold2 and its applications in the fields of biology and medicine

Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 655

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

Wang, S., Peng, J., Ma, J., & Xu, J. (2016) · Scientific Reports · doi:10.1038/srep18962 · Cited by 567

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

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26237 · Cited by 503

Sequence heuristics to encode phase behaviour in intrinsically disordered protein polymers

Quiroz, F. G., & Chilkoti, A. (2015) · Nature Materials · doi:10.1038/nmat4418 · Cited by 475

Graph convolutional networks for computational drug development and discovery

Sun, M., Zhao, S., Gilvary, C. M., Elemento, O., Zhou, J., & Wang, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz042 · Cited by 424

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 393

Terpyridine-based metallosupramolecular constructs: tailored monomers to precise 2D-motifs and 3D-metallocages

Chakraborty, S., & Newkome, G. R. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00030a · Cited by 376

End-to-End Differentiable Learning of Protein Structure

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

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility

Heffernan, R., Yang, Y., Paliwal, K. K., & Zhou, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx218 · Cited by 355

Amyloid Polymorphism in the Protein Folding and Aggregation Energy Landscape

Adamčík, J., & Mezzenga, R. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201713416 · Cited by 348

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins

Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345

Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review

Kolluri, S., Lin, J., Liu, R., Zhang, Y., & Zhang, W. (2022) · The AAPS Journal · doi:10.1208/s12248-021-00644-3 · Cited by 349

Assembly-line synthesis of organic molecules with tailored shapes

Burns, M., Labouille, S., Bame, J. R., Bull, S. P., Webster, M. P., Balieu, S., Dale, J. W., Butts, C. P., Harvey, J. N., & Aggarwal, V. K. (2014) · Nature · doi:10.1038/nature13711 · Cited by 307

Mechanisms of protein-folding diseases at a glance

Valastyan, J. S., & Lindquist, S. L. (2014) · Disease Models & Mechanisms · doi:10.1242/dmm.013474 · Cited by 318

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

Inverse statistical problems: from the inverse Ising problem to data science

Nguyen, H. C., Zecchina, R., & Berg, J. (2017) · Advances In Physics · doi:10.1080/00018732.2017.1341604 · Cited by 319

The Hsp70–Hsp90 Chaperone Cascade in Protein Folding

Luengo, T. M., Mayer, M. P., & Rüdiger, S. (2018) · Trends in Cell Biology · doi:10.1016/j.tcb.2018.10.004 · Cited by 282

Principles of Protein Stability and Their Application in Computational Design

Goldenzweig, A., & Fleishman, S. J. (2018) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-062917-012102 · Cited by 291

State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold

Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518

Programmable self-assembly

Cademartiri, L., & Bishop, K. J. M. (2014) · Nature Materials · doi:10.1038/nmat4184 · Cited by 277

Chemical Kinetics and Mechanisms of Complex Systems: A Perspective on Recent Theoretical Advances

Klippenstein, S. J., Pande, V. S., & Truhlar, D. G. (2013) · Journal of the American Chemical Society · doi:10.1021/ja408723a · Cited by 270

Advances in single chain technology

González‐Burgos, M., Latorre‐Sánchez, A., & Pomposo, J. A. (2015) · Chemical Society Reviews · doi:10.1039/c5cs00209e · Cited by 263

NMR paves the way for atomic level descriptions of sparsely populated, transiently formed biomolecular conformers

Sekhar, A., & Kay, L. E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1305688110 · Cited by 252

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

Inverse statistical physics of protein sequences: a key issues review

Cocco, S., Feinauer, C., Figliuzzi, M., Monasson, R., & Weigt, M. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa9965 · Cited by 260

Folding Simulations for Proteins with Diverse Topologies Are Accessible in Days with a Physics-Based Force Field and Implicit Solvent

Nguyen, T. H. H., Maier, J., Huang, H., Perrone, V., & Simmerling, C. L. (2014) · Journal of the American Chemical Society · doi:10.1021/ja5032776 · Cited by 244

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Liu, X. R., Zhang, M. M., & Gross, M. L. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00815 · Cited by 245

Modification of Enzyme Activity by Vibrational Strong Coupling of Water

Vergauwe, R. M. A., Thomas, A., Nagarajan, K., Shalabney, A., George, J., Chervy, T., Seidel, M., Devaux, É., Torbeev, V. Y., & Ebbesen, T. W. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201908876 · Cited by 232

Rationally Designed Peptides and Peptidomimetics as Inhibitors of Amyloid-β (Aβ) Aggregation: Potential Therapeutics of Alzheimer’s Disease

Goyal, D., Shuaib, S., Mann, S., & Goyal, B. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.6b00116 · Cited by 232

Hidden dynamics in the unfolding of individual bacteriorhodopsin proteins

Yu, H., Siewny, M. G. W., Edwards, D. T., Sanders, A. W., & Perkins, T. T. (2017) · Science · doi:10.1126/science.aah7124 · Cited by 226

Synonymous codon substitutions perturb cotranslational protein folding in vivo and impair cell fitness

Walsh, I. M., Bowman, M. A., Santarriaga, I. F. S., Rodríguez, A., & Clark, P. L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907126117 · Cited by 231

Nanomanufacturing: A Perspective

Liddle, J. A., & Gallatin, G. M. (2016) · ACS Nano · doi:10.1021/acsnano.5b03299 · Cited by 220

The prospects of quantum computing in computational molecular biology

Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Maximova, T., Moffatt, R., Ma, B., Nussinov, R., & Shehu, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004619 · Cited by 214

Secondary Forces in Protein Folding

Newberry, R. W., & Raines, R. T. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00339 · Cited by 216

Defying the activity–stability trade-off in enzymes: taking advantage of entropy to enhance activity and thermostability

Siddiqui, K. S. (2016) · Critical Reviews in Biotechnology · doi:10.3109/07388551.2016.1144045 · Cited by 208

Machine learning in polymer informatics

Sha, W., Li, Y., Tang, S., Tian, J., Zhao, Y., Guo, Y., Zhang, W., Zhang, X., Lu, S., Cao, Y., et al. (2021) · InfoMat · doi:10.1002/inf2.12167 · Cited by 207

Cooperativity Principles in Self-Assembled Nanomedicine

Li, Y., Wang, Y., Huang, G., & Gao, J. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00195 · Cited by 191

AlphaFold, Artificial Intelligence (AI), and Allostery

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190

The α-synuclein hereditary mutation E46K unlocks a more stable, pathogenic fibril structure

Boyer, D. R., Li, B., Sun, C., Fan, W., Zhou, K., Hughes, M. P., Sawaya, M. R., Jiang, L., & Eisenberg, D. S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1917914117 · Cited by 179

RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles

Clementel, D., Conte, A. D., Monzón, A. M., Camagni, G. F., Minervini, G., Piovesan, D., & Tosatto, S. C. E. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac365 · Cited by 175

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

Perspectives on Electrostatics and Conformational Motions in Enzyme Catalysis

Hanoian, P., Liu, C. T., Hammes‐Schiffer, S., & Benkovic, S. J. (2015) · Accounts of Chemical Research · doi:10.1021/ar500390e · Cited by 173

Recent advances in understanding catalysis of protein folding by molecular chaperones

Balchin, D., Hayer-Hartl, M. K., & Hartl, F. U. (2020) · FEBS Letters · doi:10.1002/1873-3468.13844 · Cited by 173

Coevolutionary signals across protein lineages help capture multiple protein conformations

Morcos, F., Jana, B., Terence Hwa, B., & Onuchic, J. N. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315625110 · Cited by 170

Protein folding and de novo protein design for biotechnological applications

Khoury, G. A., Smadbeck, J. B., Kieslich, C. A., & Floudas, C. A. (2013) · Trends in biotechnology · doi:10.1016/j.tibtech.2013.10.008 · Cited by 162

Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference

MacCallum, J. L., Pérez, A., & Dill, K. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506788112 · Cited by 166

Nanopore Sensing of Protein Folding

Si, W., & Aksimentiev, A. (2017) · ACS Nano · doi:10.1021/acsnano.7b02718 · Cited by 164

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

Nature and Regulation of Protein Folding on the Ribosome

Waudby, C. A., Dobson, C. M., & Christodoulou, J. (2019) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2019.06.008 · Cited by 147

Chaperoning G Protein-Coupled Receptors: From Cell Biology to Therapeutics

Tao, Y., & Conn, P. M. (2014) · Endocrine Reviews · doi:10.1210/er.2013-1121 · Cited by 149

Sampling molecular conformations and dynamics in a multiuser virtual reality framework

O’Connor, M. B., Deeks, H. M., Dawn, E., Metatla, O., Roudaut, A., Sutton, M., Thomas, L. M., Glowacki, B. R., Sage, R., Tew, P., et al. (2018) · Science Advances · doi:10.1126/sciadv.aat2731 · Cited by 150

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Sidky, H., Chen, W., & Ferguson, A. L. (2020) · Molecular Physics · doi:10.1080/00268976.2020.1737742 · Cited by 157

Self-assembly of colloidal metal–organic framework (MOF) particles

Fonseca, J., Meng, L., Imaz, I., & Maspoch, D. (2023) · Chemical Society Reviews · doi:10.1039/d2cs00858k · Cited by 146

Adhesion-mediated mechanosignaling forces mitohormesis

Tharp, K. M., Higuchi‐Sanabria, R., Timblin, G. A., Ford, B., Garzón-Coral, C., Schneider, C. P., Muncie, J. M., Stashko, C., Daniele, J. R., Moore, A. S., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.04.017 · Cited by 147

The inverted free energy landscape of an intrinsically disordered peptide by simulations and experiments

Granata, D., Baftizadeh, F., Habchi, J., Galvagnion, C., Simone, A. D., Camilloni, C., Laio, A., & Vendruscolo, M. (2015) · Scientific Reports · doi:10.1038/srep15449 · Cited by 137

Rapid prediction and analysis of protein intrinsic disorder

Dayhoff, G. W., & Uversky, V. N. (2022) · Protein Science · doi:10.1002/pro.4496 · Cited by 141

Liquid‐Phase Electron Microscopy for Soft Matter Science and Biology

Wu, H., Friedrich, H., Patterson, J. P., Sommerdijk, N. A. J. M., & de Jonge, N. (2020) · Advanced Materials · doi:10.1002/adma.202001582 · Cited by 146

Homology modeling in the time of collective and artificial intelligence

Hameduh, T., Haddad, Y., Adam, V., & Heger, Z. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.007 · Cited by 146

AI-Driven Deep Learning Techniques in Protein Structure Prediction

Chen, L., Li, Q., Nasif, K. F. A., Xie, Y., Deng, B., Niu, S., Pouriyeh, S., Dai, Z., Chen, J., & Xie, Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25158426 · Cited by 152

The case for defined protein folding pathways

Englander, S. W., & Mayne, L. C. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706196114 · Cited by 137

Decoding mechanisms by which silent codon changes influence protein biogenesis and function

Bali, V., & Bebok, Z. M. (2015) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2015.03.011 · Cited by 136

Single-molecule FRET methods to study the dynamics of proteins at work

Mazal, H., & Haran, G. (2019) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2019.08.007 · Cited by 137

Optimized atomic statistical potentials: assessment of protein interfaces and loops

Dong, G. Q., Fan, H., Schneidman‐Duhovny, D., Webb, B., & Šali, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt560 · Cited by 129

Coding and decoding libraries of sequence-defined functional copolymers synthesized via photoligation

Zydziak, N., Konrad, W., Feist, F. A., Afonin, S., Weidner, S. M., & Barner‐Kowollik, C. (2016) · Nature Communications · doi:10.1038/ncomms13672 · Cited by 133

“Michael” Nanocarriers Mimicking Transient-Binding Disordered Proteins

Sanchez‐Sanchez, A., Akbari, S., Etxeberria, A., Arbe, A., Gasser, U., Moreno, A. J., Colmenero, J., & Pomposo, J. A. (2013) · ACS Macro Letters · doi:10.1021/mz400173c · Cited by 126

Folding the proteome

Braselmann, E., Chaney, J. L., & Clark, P. L. (2013) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2013.05.001 · Cited by 125

RING 4.0: faster residue interaction networks with novel interaction types across over 35,000 different chemical structures

Conte, A. D., Camagni, G. F., Clementel, D., Minervini, G., Monzón, A. M., Ferrari, C., Piovesan, D., & Tosatto, S. C. E. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae337 · Cited by 125

Characterizing hydrophobicity of amino acid side chains in a protein environment via measuring contact angle of a water nanodroplet on planar peptide network

Zhu, C., Gao, Y., Li, H., Meng, S., Li, L., Francisco, J. S., & Zeng, X. C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616138113 · Cited by 124

Non-Markovian modeling of protein folding

Ayaz, C., Tepper, L., Brünig, F. N., Kappler, J., Daldrop, J. O., & Netz, R. R. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023856118 · Cited by 123

Conformational strains of pathogenic amyloid proteins in neurodegenerative diseases

Li, D., & Liu, C. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00603-7 · Cited by 124

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

Giollo, M., Martín, A. J. M., Walsh, I., Ferrari, C., & Tosatto, S. C. E. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s4-s7 · Cited by 121

Dynamic Ordering and Phase Segregation in Hydrogen-Bonded Polymers

Chen, S., & Binder, W. H. (2016) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00174 · Cited by 121

Combining Orthogonal Chain‐End Deprotections and Thiol–Maleimide Michael Coupling: Engineering Discrete Oligomers by an Iterative Growth Strategy

Huang, Z., Zhao, J., Wang, Z., Meng, F., Ding, K., Pan, X., Zhou, N., Li, X., Zhang, Z., & Zhu, X. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201706522 · Cited by 120

A prevalent intraresidue hydrogen bond stabilizes proteins

Newberry, R. W., & Raines, R. T. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2206 · Cited by 120

Surfactant-like peptides: From molecular design to controllable self-assembly with applications

Li, J., Wang, J., Zhao, Y., Zhou, P., Carter, J., Li, Z., Waigh, T. A., Lu, J. R., & Xu, H. (2020) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213418 · Cited by 121

Single‐sequence‐based prediction of protein secondary structures and solvent accessibility by deep whole‐sequence learning

Heffernan, R., Paliwal, K. K., Lyons, J., Singh, J., Yang, Y., & Zhou, Y. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25534 · Cited by 115

Folding of guanine quadruplex molecules–funnel-like mechanism or kinetic partitioning? An overview from MD simulation studies

Šponer, J., Bussi, G., Stadlbauer, P., Kührová, P., Banáš, P., Islam, B., Haider, S. M., Neidle, S., & Otyepka, M. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.12.008 · Cited by 116

Aqueous ionic liquids and their effects on protein structures: an overview on recent theoretical and experimental results

Smiatek, J. (2017) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aa6c9d · Cited by 117

A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images

Torney, C. J., Lloyd‐Jones, D. J., Chevallier, M., Moyer, D. C., Maliti, H. T., Mwita, M., Kohi, E. M., & Hopcraft, G. C. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13165 · Cited by 110

The Protein Folding Problem: The Role of Theory

Nassar, R., Dignon, G. L., Razban, R. M., & Dill, K. A. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167126 · Cited by 111

The Ribosome Cooperates with a Chaperone to Guide Multi-domain Protein Folding

Liu, K., Maciuba, K., & Kaiser, C. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.01.043 · Cited by 104

Interaction of silver nanoparticles with antioxidant enzymes

Liu, W., Worms, I. A. M., & Slaveykova, V. I. (2020) · Environmental Science Nano · doi:10.1039/c9en01284b · Cited by 107

Energy Landscapes of Protein Aggregation and Conformation Switching in Intrinsically Disordered Proteins

Strodel, B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167182 · Cited by 102

Structural dynamics flexibility informs function and evolution at a proteome scale

Gerek, Z. N., Kumar, S., & Ozkan, S. B. (2013) · Evolutionary Applications · doi:10.1111/eva.12052 · Cited by 103

Programming Molecular Association and Viscoelastic Behavior in Protein Networks

Dooling, L. J., Buck, M. E., Zhang, W., & Tirrell, D. A. (2016) · Advanced Materials · doi:10.1002/adma.201506216 · Cited by 103

Folding of a large protein at high structural resolution

Walters, B. T., Mayne, L. C., Hinshaw, J. R., Sosnick, T. R., & Englander, S. W. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319482110 · Cited by 104

Comparing a simple theoretical model for protein folding with all-atom molecular dynamics simulations

Henry, E. R., Best, R. B., & Eaton, W. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317105110 · Cited by 102

Solving protein structures using short-distance cross-linking constraints as a guide for discrete molecular dynamics simulations

Brodie, N. I., Popov, K. I., Petrotchenko, E. V., Dokholyan, N. V., & Borchers, C. H. (2017) · Science Advances · doi:10.1126/sciadv.1700479 · Cited by 100

Novel microscale approaches for easy, rapid determination of protein stability in academic and commercial settings

Alexander, C. G., Wanner, R., Johnson, C. M., Breitsprecher, D., Winter, G., Duhr, S., Baaske, P., & Ferguson, N. S. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.09.016 · Cited by 106

Solid-State NMR-Based Approaches for Supramolecular Structure Elucidation

Weingarth, M. H., & Baldus, M. (2013) · Accounts of Chemical Research · doi:10.1021/ar300316e · Cited by 102

Deep convolutional networks for quality assessment of protein folds

Derevyanko, G., Grudinin, S., Bengio, Y., & Lamoureux, G. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty494 · Cited by 103

The Protein Folding Problem

Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.092707.153558 · Cited by 1,163

Protein folding prediction

Ma, B. (2016) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972016-00658 · Cited by 1

The protein folding problem: when will it be solved?

Dill, K. A., Ozkan, S. B., Weikl, T. R., Chodera, J. D., & Voelz, V. A. (2007) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2007.06.001 · Cited by 261

A Practical Guide to Protein Structure Prediction

Jones, D. T. (2003) · Humana Press eBooks · doi:10.1385/1-59259-368-2:131 · Cited by 10

Protein Folding Problem

(2008) · Springer eBooks · doi:10.1007/978-3-540-38918-7_6531 · Cited by 4