The role of local versus nonlocal physicochemical restraints in determining protein native structure
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The tertiary structure of a native protein is dictated by the interplay of local secondary structure propensities, hydrogen bonding, and tertiary interactions. It is argued that the space of known protein topologies covers all single domain folds and results from the compactness of the native structure and excluded volume. Protein compactness combined with the chirality of the protein's side chains also yields native-like Ramachandran plots. It is the many-body, tertiary interactions among residues that collectively select for the global structure that a particular protein sequence adopts. This explains why the recent advances in deep-learning approaches that predict protein side-chain contacts, the distance matrix between residues, and sequence alignments are successful. They succeed because they implicitly learned the many-body interactions among protein residues.
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Deep learning's ability to learn long-range correlations among residues enables it to map sequences to existing structures by exploiting PDB completeness for single domain proteins.
“As discussed in ref 37, deep learning’s ability to learn long-range correlations among residues enables it to map sequences to existing structures by exploiting PDB completeness for single domain proteins.”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [37]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · see citing works
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096 · see citing works
Statistical mechanics of chain molecules
Flory, P. J., & Volkenstein, M. V. (1969) · Biopolymers · doi:10.1002/bip.1969.360080514 · Cited by 6,449 · see citing works
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 · see citing works
Prediction of protein conformation
Chou, P. Y., & Fasman, G. D. (1974) · Biochemistry · doi:10.1021/bi00699a002 · Cited by 3,309 · see citing works
Prediction of the Secondary Structure of Proteins from their Amino Acid Sequence
Chou, P. Y., & Fasman, G. D. (1979) · Advances in enzymology and related areas of molecular biology/Advances in enzymology and related subjects · doi:10.1002/9780470122921.ch2 · Cited by 2,917 · see citing works
A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure
Bowie, J. U., Lüthy, R., & Eisenberg, D. (1991) · Science · doi:10.1126/science.1853201 · Cited by 2,851 · see citing works
Introduction to Protein Structure
Brändén, C.-I., & Tooze, J. (2012) · Garland Science eBooks · doi:10.1201/9781136969898 · Cited by 2,398 · see citing works
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 · see citing works
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 · see citing works
Introduction to Protein Structure
Banaszak, L. J. (2000) · Elsevier eBooks · doi:10.1016/b978-012077700-6/50001-8 · Cited by 1,054 · see citing works
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works
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 · see citing works
Theoretical Studies of Protein Folding
Gō, N. (1983) · Annual Review of Biophysics and Bioengineering · doi:10.1146/annurev.bb.12.060183.001151 · Cited by 833 · see citing works
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
Kamisetty, H., Ovchinnikov, S., & Baker, D. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314045110 · Cited by 672 · see citing works
Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661 · see citing works
Review: Protein Secondary Structure Prediction Continues to Rise
Rost, B. (2001) · Journal of Structural Biology · doi:10.1006/jsbi.2001.4336 · Cited by 534 · see citing works
Progress and challenges in protein structure prediction
Zhang, Y. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.02.004 · Cited by 507 · see citing works
A backbone-based theory of protein folding
Rose, G. D., Fleming, P. J., Banavar, J. R., & Maritan, A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606843103 · Cited by 478 · see citing works
Evolution of the Structure of Ferredoxin Based on Living Relics of Primitive Amino Acid Sequences
Eck, R. V., & Dayhoff, M. O. (1966) · Science · doi:10.1126/science.152.3720.363 · Cited by 471 · see citing works
ECOD: an evolutionary classification of protein domains
Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003926 · Cited by 426 · cited 1× · see citing works
“To address this issue, one could imagine applying deep learning tools to existing structures whose substructures have been identified as putative primordial fragments [24,56] to explore if they have stronger signals than later evolving fragments.”
BackgroundCCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations
Seemayer, S., Gruber, M., & Söding, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu500 · Cited by 416 · see citing works
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 · see citing works
Alva, V., Nam, S.-Z., Söding, J., & Lupas, A. (2016) · Nucleic Acids Res. · doi:10.1093/nar/gkw348 · Cited by 382 · see citing works
End-to-End Differentiable Learning of Protein Structure
AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371 · see citing works
Andreeva, A., Kulesha, E., Gough, J., & Murzin, A. (2019) · Nucleic Acids Res. · doi:10.1093/nar/gkz1064 · Cited by 326 · see citing works
Lupas, A. N., Ponting, C. P., & Russell, R. B. (2001) · Journal of Structural Biology · doi:10.1006/jsbi.2001.4393 · Cited by 293 · see citing works
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 289 · see citing works
Liang, J., & Dill, K. A. (2001) · Biophysical Journal · doi:10.1016/s0006-3495(01)75739-6 · Cited by 276 · see citing works
Xie, L., & Bourne, P. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704422105 · Cited by 245 · see citing works
Respective roles of short- and long-range interactions in protein folding.
Gō, N., & Taketomi, H. (1978) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.75.2.559 · Cited by 235 · see citing works
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 · see citing works
On the origin and highly likely completeness of single-domain protein structures
Zhang, Y., Hubner, I. A., Arakaki, A. K., Shakhnovich, E., & Skolnick, J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0509379103 · Cited by 197 · see citing works
Self-citationAssessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25407 · Cited by 177 · see citing works
Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm
Skolnick, J., Kihara, D., & Zhang, Y. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20106 · Cited by 168 · see citing works
Self-citationHou, J., Wu, T., Cao, R., & Cheng, J. J. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25697 · Cited by 162 · see citing works
Zheng, W. H., Zhang, C., Wuyun, Q., Pearce, R., Li, Y., & Zhang, Y. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz384 · Cited by 138 · see citing works
Global Fold Determination from a Small Number of Distance Restraints
Aszódi, A., Gradwell, M. J., & Taylor, W. R. (1995) · Journal of Molecular Biology · doi:10.1006/jmbi.1995.0436 · Cited by 133 · see citing works
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks
Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.014 · Cited by 114 · see citing works
Functional Proteins from Short Peptides: Dayhoff's Hypothesis Turns 50
Romero, M. L. R., Rabin, A., & Tawfik, D. S. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201609977 · Cited by 103 · see citing works
ECOD: new developments in the evolutionary classification of domains
Schaeffer, R. D., Liao, Y., Cheng, H., & Grishin, N. V. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1137 · Cited by 91 · see citing works
Adding backbone to protein folding: why proteins are polypeptides
Honig, B. H., & Cohen, F. E. (1996) · Folding and Design · doi:10.1016/s1359-0278(96)00005-3 · Cited by 84 · see citing works
Abriata, L. A., Tamò, G. E., & Dal Peraro, M. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25787 · Cited by 79 · see citing works
Nepomnyachiy, S., Ben‐Tal, N., & Kolodny, R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707642114 · Cited by 77 · see citing works
SPARKS 2 and SP3 servers in CASP6
Zhou, H., & Zhou, Y. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20732 · Cited by 72 · see citing works
DESTINI: A deep-learning approach to contact-driven protein structure prediction
Gao, M., Zhou, H., & Skolnick, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-40314-1 · Cited by 61 · see citing works
Self-citationBurgess, A. W., & Scheraga, H. A. (1975) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.72.4.1221 · Cited by 60 · see citing works
ChSeq : A database of chameleon sequences
Li, W., Kinch, L. N., Karplus, P. A., & Grishin, N. V. (2015) · Protein Science · doi:10.1002/pro.2689 · Cited by 59 · see citing works
From ancestral peptides to designed proteins
Alva, V., & Lupas, A. N. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.11.006 · Cited by 46 · see citing works
On the possible origin of protein homochirality, structure, and biochemical function
Skolnick, J., Zhou, H., & Gao, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908241116 · Cited by 45 · see citing works
Self-citationThe history of the CATH structural classification of protein domains
Sillitoe, I., Dawson, N. L., Thornton, J. M., & Orengo, C. A. (2015) · Biochimie · doi:10.1016/j.biochi.2015.08.004 · Cited by 44 · see citing works
Template‐based protein structure modeling using TASSERVMT
Zhou, H., & Skolnick, J. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23183 · Cited by 38 · see citing works
Self-citationCao, H., Gao, M., Zhou, H., & Skolnick, J. (2018) · Communications Biology · doi:10.1038/s42003-018-0236-y · Cited by 32 · see citing works
Self-citationWhisstock, J. C., Lesk, A. M., & Carrell, R. W. (1996) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(199611)26:3 · Cited by 30 · see citing works
Self-citationA novel sequence alignment algorithm based on deep learning of the protein folding code
Gao, M., & Skolnick, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa810 · Cited by 18 · see citing works
Self-citationBryliński, M., Gao, M., & Skolnick, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21140d · Cited by 16 · see citing works
Self-citationWhy Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods?
Skolnick, J., & Zhou, H. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b09517 · Cited by 14 · see citing works
Self-citationJagielska, A., & Skolnick, J. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20616 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
2 of the 62 references couldn’t be matched to a known paper.
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Sorted by citations
Skolnick, J., Gao, M., Zhou, H., & Singh, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01114 · Cited by 251 · see citing works
Self-citationA backbone-centred energy function of neural networks for protein design
Huang, B., Xu, Y., Hu, X., Liu, Y., Liao, S., Zhang, J., Huang, C., Hong, J., Chen, Q., & Liu, H. (2022) · Nature · doi:10.1038/s41586-021-04383-5 · Cited by 103 · see citing works
Qian, S., Lan, T., Zhao, X., Song, T., Cao, Y., Zhang, H., & Liu, J. (2023) · Ultrasonics Sonochemistry · doi:10.1016/j.ultsonch.2023.106532 · Cited by 51 · see citing works
Research progress of protein complex systems and their application in food: A review
Lu, X., Qian, S., Wu, X., Lan, T., Zhang, H., & Liu, J. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.130987 · Cited by 34 · see citing works
Artificial Intelligence-Driven de Novo Design of Robust Enzymes to Enhance Their Performance
Tang, M., Ge, F., Li, A., Hu, L., Wang, C., Tang, J., Song, X., Liu, X., Shi, H. S., & Tan, Z. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00452 · Cited by 28 · see citing works
High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function
Gao, M., Lund-Andersen, P. K., Morehead, A., Mahmud, S., Chen, C., Chen, X., Giri, N., Roy, R. S., Quadir, F., Effler, T. C., et al. (2021) · Workshop on Machine Learning in HPC Environments · doi:10.1109/mlhpc54614.2021.00010 · Cited by 12 · see citing works
Self-citationBalasco, N., Esposito, L., Simone, A. D., & Vitagliano, L. (2022) · Biomolecules · doi:10.3390/biom12091184 · Cited by 7 · see citing works
Molecular Bases of Protein Antigenicity and Determinants of Immunogenicity, Anergy, and Mitogenicity
Pedroza‐Escobar, D., Maldonado, I. C., González-Cortés, T., Delgadillo‐Guzmán, D., Ruíz-Flores, P., Cruz, J. H.-S., Espino-Silva, P.-K., Loyola, E. F., Ramírez‐Moreno, A., Ávalos-Soto, J., et al. (2023) · Protein and Peptide Letters · doi:10.2174/0929866530666230907093339 · Cited by 7 · see citing works
A General Framework to Learn Tertiary Structure for Protein Sequence Characterization
Gao, M., & Skolnick, J. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.689960 · Cited by 5 · see citing works
Self-citationChen, L., Nasif, K. F. A., Deng, B., Niu, S., & Xie, C. Y. (2024) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai62512.2024.00083 · Cited by 4 · see citing works
Zhang, L., & Liu, H. (2023) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-023-00518-0 · Cited by 3 · see citing works
Topology and Algebra of Bonded Knots and Braids
Diamantis, I., Kauffman, L. H., & Lambropoulou, S. (2025) · Mathematics · doi:10.3390/math13203260 · Cited by 2 · see citing works
Artificial Intelligence based System in Protein Folding using Alphafold
Srivastava, P., Suyash, S., & Jayapandian, N. (2022) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs55517.2022.10029190 · Cited by 2 · see citing works
The repetitive local sampling and the local distribution theory
(2021) · doi:10.1002/wcms.1588 · Cited by 2 · see citing works
A general framework to learn tertiary structure for protein sequence characterizationPreprint
Gao, M., & Skolnick, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.01.438098
Self-citationThe repetitive local sampling and the local distribution theoryPreprint
Tian, P. (2021) · doi:10.33774/chemrxiv-2021-v223l-v2
The repetitive local sampling and the local distribution theoryPreprint
Tian, P. (2021) · doi:10.33774/chemrxiv-2021-v223l
Artificial Intelligence-Driven Protein Folding System Employing Alpha Fold
Sowmiya, R., Kumar, M. P., Kumar, T. M. S., Rasika, P., Devi, M. P., & Dharshini, S. (2025) · International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) · doi:10.1109/icmsci62561.2025.10894449
Jannus, F., & Ramírez-Rodrigo, H. (2026) · Pharmaceutics · doi:10.3390/pharmaceutics18060728
- Authors
- Skolnick, J., & Gao, M.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Skolnick, Jeffrey
- Gao, Mu
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 68
- Pages
- 1-8
- ISSN
- 0959-440X (ISSN-L)
- PMC
- PMC8079554
- OpenAlex
- W3094695530
- Cited by
- 21
- References
- 62
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 9
- FWCI
- 0.36
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Opinion in Structural Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (R35GM118039)
- 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 › RNA and protein synthesis mechanisms
- Keywords
- protein folding, protein tertiary structure, nonlocal interactions, protein compactness, excluded volume, Ramachandran plot, many-body interactions, hydrogen bonding, secondary structure propensity
- MeSH terms
- Models, Molecular, Proteins, Sequence Alignment, Protein Structure, Secondary, Protein Structure, Tertiary, Protein Folding
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