AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function
Open accessAbstract
AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.
AI snapshot
Written from the paper’s full text.
TL;DR
This Viewpoint argues that AlphaFold 2 (AF2) succeeds mainly as a sophisticated fold-recognition algorithm that exploits the near-completeness of the single-domain PDB structure library, plus learned local side-chain packing refinement. It also assesses AF2’s human-exome coverage and discusses implications and limits for inferring protein function, ligand binding, and phenotype.
Key findings
- AF2 often produced models with TM-score > 0.9, while MSA-based methods plateau around TM-score 0.85; TM-score > 0.85 indicates both global fold and details are correct.
- In human exome predictions, AF2 confidence score correlated with TM-score at 0.67. For low-quality models, only 10% had a closest template with TM-score ≥ 0.55; for good models, 53%; for high-accuracy models, 86%. These trends held with a current PDB library of almost 90,000 structures clustered at 70% sequence identity.
- Comparing 26,996 consensus human exome sequences, TASSER-VMT predicted at least one domain with TM-score > 0.4 for 89% of sequences; AF2 had confidence >70 for 67.4% and >60 for 86.9%. AF2 added 8.7% of human sequences with confidence >60, and the union provided confident models for at least one domain for 97.5% of human sequences.
- For ligand homology modeling, low-resolution structures are adequate, with at most a 20% improvement in enrichment factor going from lower resolution (TM-score > 0.4) to crystal structures, because binding sites are generally the most accurately predicted parts of a protein’s structure.
Methods
- This is a Viewpoint/perspective, not a new AF2 training study. It interprets AF2’s mechanism using prior deep-learning work and structural analyses.
- The authors used TM-align to generate structural alignments of AF2-predicted human proteins to a 41,000-member PDB library from 2016, and also examined results with a current PDB library of almost 90,000 structures clustered at 70% sequence identity.
- They analyzed correlations between AF2 confidence scores and TM-score/contact order, binned confidence as <70, 70–90, and >90, and compared AF2 coverage with TASSER-VMT on 26,996 consensus human exome sequences.
Limitations
- The paper states that structure alone does not provide biochemical function and cannot predict phenotypical function, which often depends on complex interactions among proteins, RNA, DNA, and metabolites.
- The paper states that AF2 predicts a particular conformational state; if an active conformation is predicted, it may not work for targeting the inactive state. It also notes that AF2 was trained on proteins with particular ions and might not accurately represent binding pockets under other conditions.
- The paper states that much work remains to establish AF2’s strengths and limitations, including whether it can predict effects of missense mutations, splice variants, chameleon sequences, cryptic pockets, and allosteric transitions. Evident from the text: the analysis is a perspective focused on the human exome and single-domain PDB completeness, and multimeric/multi-domain prediction is discussed as likely but not fully established.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 33 cited sources checked · 0 supported · 0 flagged
- Findingnone given
For some multiple domain CASP14 targets, AF2 made high quality structure predictions.
“For some multiple domain CASP14 targets, AF2 made high quality structure predictions.”
Quote in the paperSubstantial claim - Findingnone given
The Pearson correlation coefficient of the average confidence score per protein with contact order is 0.28.
“Interestingly, the Pearson correlation coefficient of the average confidence score per protein with contact order is 0.28—a weak correlation of model quality with whether the fold has a sequential or nonsequential arrangement of secondary structural elements.”
Quote in the paperSubstantial claim - Findingnone given
AF2 sometimes refines the structure to experimental accuracy, with TM-scores greater than 0.9.
“Having selected an approximate fold, AF2 sometimes refines the structure to experimental accuracy, with TM-scores greater than 0.9.”
Quote in the paperNot a substantial claim - Findingown data
These results are preserved when the current PDB library of almost 90,000 structures clustered at a sequence identity cutoff of 70% is used, with a shift toward higher TM-scores for the various prediction confidence levels.
“These results are preserved when the current PDB library of almost 90,000 structures clustered at a sequence identity cutoff of 70% is used, with a shift toward higher TM-scores for the various prediction confidence levels.”
Quote in the paperSubstantial claim - Findingown data
The correlation coefficient of AF2's confidence score with TM-score is 0.67.
“The correlation coefficient of AF2’s confidence score with TM-score is 0.67.”
Quote in the paperSubstantial claim
- Findingown data
For low quality predicted structures, only 10% of proteins have a closest template in the template library with a TM-score ≥ 0.55.
“then for low quality predicted structures only 10% of proteins have a closest template in the template library with a TM-score ≥ 0.55.”
Quote in the paperSubstantial claim - Findingcitation
There are 26,996 consensus human exome sequences between AF2 and a previously predicted set using TASSER-VMT.
“There are 26,996 consensus human exome sequences between AF2 and a previously predicted set using TASSER-VMT.44”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [44] source
Not checked in the free run.
- Findingreasoning
Using the same domain partitioning as TASSER-VMT, AF2 provides an additional 8.7% of human sequences with a confidence score greater than 60.
“Using the same domain partitioning as TASSER-VMT, AF2 provides an additional 8.7% of human sequences with a confidence score greater than 60.”
Quote in the paperSubstantial claim - Findingreasoning
The union of approaches provides confident models for at least one domain for 97.5% of human sequences.
“The union of approaches provides confident models for at least one domain for 97.5% of human sequences.”
Quote in the paperSubstantial claim - Findingcitation
AF2 generates significantly better structure predictions than existing methods on average.
“While AF2 generates significantly better structure predictions than existing methods on average, for the human exome, it is worthwhile to examine how many additional sequences have confident models compared to the prior art.44”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [44] source
Not checked in the free run.
- Interpretationown data
AF2 learned information about the relationship of a sequence to its fold regardless of its contact order.
“In contrast, AF2 learned information about the relationship of a sequence to its fold regardless of its contact order.”
Quote in the paperSubstantial claim - Premisecitation
The library of single domain protein structures is essentially complete.
“The key to why AF211 works is the fact the library of single domain protein structures is essentially complete.2,12,13”
- Also stated in: § Abstract · boosted
“However, by 2005, the library of single domain protein structures was shown to be essentially complete.2,12,13”
Quote in the paperSubstantial claim0 of 3 sources checked - Also stated in: § Abstract · boosted
- Premisecitation
In 2005, the authors pointed out that if one had an algorithm that maps a protein's sequence to the correct template structure in the existing PDB library independent of evolutionary relationship, the folding problem could be solved.
“In 2005,2 we pointed out that if one had an algorithm that maps a protein’s sequence to the correct template structure in the existing PDB14 library (independent of their evolutionary relationship), the folding problem could be solved.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] source
Not checked in the free run.
- Premisecitation
Deep learning with residue covariation significantly improved structure prediction.
“Deep learning with residue covariation significantly improved structure prediction.18,19,23–25,33”
Quote in the paperSubstantial claim0 of 6 sources checked - Premisenone given
The inability to match evolutionarily distant or unrelated proteins to their closest template PDB structure was the problem preventing fold recognition from being solved in 2005.
“Why was not fold recognition solved in 2005? The problem was the inability to match evolutionarily distant or unrelated proteins to their closest template PDB structure.”
Quote in the paperSubstantial claim - Interpretationnone given
AF2's individual per residue networks learned local, residue specific packing details.
“Its individual per residue networks learned local, residue specific packing details.”
Quote in the paperNot a substantial claim - Interpretationnone given
AF2 has learned local side chain packing rearrangements that enable it to refine proteins to high resolution.
“It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution.”
Quote in the paperSubstantial claim - Interpretationreasoning
AF2 appears to have solved the fold recognition problem.
“Since AF2 recapitulates the trends of the SAdLSA self-alignment study, albeit with better model accuracy, it appears to have solved the fold recognition problem.”
Quote in the paperSubstantial claim - Premisecitation
AF2 predicted the structures of many difficult protein targets at or near experimental resolution in CASP14.
“Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] 14th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction. Protein Structure Prediction Center. https://predictioncenter.org/casp14/ (accessed September 2021).
Not checked in the free run.
- Premisereasoning
SAdLSA trained on purely α-helical proteins can align the sequences of distantly related pure β-proteins.
“SAdLSA trained on purely α-helical proteins can align the sequences of distantly related pure β-proteins.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [35] source
Not checked in the free run.
- Interpretationcitation
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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [37] source
Not checked in the free run.
- Interpretationreasoning
If an active conformation is predicted by AF2, then using it to target the inactive state will not work.
“If an active conformation is predicted by AF2, then using it to target the inactive state will not work.”
Quote in the paperSubstantial claim - Interpretationnone given
The structure of a protein can be used to infer its biochemical function.
“However, the structure of a protein can be used to infer its biochemical function.”
Quote in the paperNot a substantial claim - Interpretationreasoning
These additional structures probably have a TM-score to the native ≥ 0.40.
“These structures probably have a TM-score to the native ≥ 0.40.”
Quote in the paperNot a substantial claim - Premisecitation
A protein can exist in one conformation in its inactive state and another in its active state.
“However, a protein can exist in one conformation in its “inactive” state and another in its “active” state.53–56”
Quote in the paperSubstantial claim0 of 4 sources checked - Premisecitation
TASSER-VMT predicts that at least one domain has a TM-score greater than 0.4 for 89% of sequences.
“TASSER-VMT predicts that at least one domain has a TM-score greater than 0.4 (indicating global fold similarity to the native structure) for 89% of sequences.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [44] source
Not checked in the free run.
- May combine two claims. 11
- Classified as probably not a substantial claim. 1
May combine two claims.
Findingown data53% of proteins with good models and 86% with high accuracy models have a TM-score ≥ 0.55.
“In contrast, 53% of proteins with good models and 86% with high accuracy models have a TM-score ≥ 0.55.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAF2 has a confidence score greater than 70 for 67.4% of sequences, while 86.9% have a confidence score greater than 60.
“AF2 has a confidence score greater than 70 for 67.4% of sequences, while 86.9% have a confidence score greater than 60.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe structural space of protein-protein interfaces is small (<1000 structurally distinct interfaces), with nothing special about protein-protein interfaces.
“In practice, the structural space of protein–protein interfaces is small (<1000 structurally distinct interfaces),41,42 with nothing special about protein–protein interfaces.43”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationDESTINI CNN deep learning learned contact map patterns of secondary structure packing and eliminated nonphysical, isolated residue pairs.
“The DESTI-NI convolutional neural network (CNN) deep learning approach to structure prediction23 demonstrated that deep learning learned the contact map patterns of secondary structure packing and eliminated nonphysical, isolated residue pairs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] source
Not checked in the free run.
May combine two claims.
Interpretationnone givenAF2 is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures.
“Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe reason SAdLSA trained on helical proteins works for β-proteins is that the global topology of β-proteins is in the training set; the chain contour of helical proteins provides a globally correct fold for β-proteins.
“The reason that SAdLSA trained on helical proteins works for β-proteins is that the global topology of β-proteins is in the training set;2,12,13 the chain contour of helical proteins provides a globally correct fold for β-proteins.13”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
InterpretationreasoningStructure alone does not provide the biochemical function of the protein nor can AF2 predict phenotypical function.
“First, structure alone does not provide the biochemical function of the protein nor can AF2 predict phenotypical function which is often the result of the complex interplay of proteins, RNA, DNA, and metabolites.”
Quote in the paperSubstantial claimMay combine two claims.
PremisereasoningUsing ligand homology modeling, low resolution structures are adequate for virtual ligand screening, with at most a 20% improvement in enrichment factor on going from lower resolution (TM-score > 0.4) to crystal structures.
“Contrary to popular belief that high resolution structures are required, using ligand homology modeling,45–48 low resolution structures are adequate, with at most a 20% improvement in enrichment factor on going from lower resolution (TM-score > 0.4) to crystal structures.”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [45] source
Not checked in the free run.
- cites [46] source
Not checked in the free run.
- cites [47] source
Not checked in the free run.
- cites [48] source
Not checked in the free run.
- Rests on an assumption: Binding sites are generally the most accurately predicted parts of a protein's structure.
Classified as probably not a substantial claim. May combine two claims.
InterpretationcitationHow inclusion of higher quality structures improves the ability to predict drug side effects, efficacy and the role of missense variations in non-Mendelian diseases depends on accurately characterizing the interplay of many molecular players.
“How inclusion of higher quality structures improves the ability to predict drug side effects, efficacy64 and the role of missense variations in non-Mendelian diseases depends on accurately characterizing the interplay of many molecular players.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [64] source
Not checked in the free run.
May combine two claims.
Interpretationnone givenAF2 can accurately predict backbone and side chain conformations, but it is for a particular conformational state.
“Even though AF2 can accurately predict backbone and side chain conformations, it is for a particular conformational state.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationKnowledge of a protein's structure alone does not provide its biochemical, much less phenotypical effects, but structure can be a component.
“Importantly, while knowledge of a protein’s structure alone does not provide its biochemical, much less, phenotypical effects, structure can be a component.64”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [64] source
Not checked in the free run.
The paper relies on this without support.
Binding sites are generally the most accurately predicted parts of a protein's structure.
“The reason is simple; binding sites are generally the most accurately predicted parts of a protein’s structure.”
The paper relies on this without support.
AF2 was trained on proteins having particular ions.
“Since AF2 was trained on proteins having particular ions, for example, it might not provide an accurate representation of the binding pocket under other conditions.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Two key factors contributing to AF2's success are the completeness of the structural space of single-domain proteins and an abundance of sequences in sequence databases.
“two key factors contributing to the success of AF2 are the completeness of the structural space of single-domain proteins and an abundance of sequences in sequence databases3.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [3]
It is very likely that neural network models trained for single-chain structure prediction have already learned the representations necessary to model protein complexes made of multiple single-chain proteins.
“Taken together, it is very likely that the neural network models trained for single-chain structure prediction have already learned the representations necessary to model protein complexes made of multiple single-chain proteins3.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [3]
Cites
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 · see citing works
I-TASSER: a unified platform for automated protein structure and function prediction
Roy, A., Küçükural, A., & Zhang, Y. (2010) · Nature Protocols · doi:10.1038/nprot.2010.5 · Cited by 6,093 · see citing works
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 5,542 · see citing works
I-TASSER server for protein 3D structure prediction
Zhang, Y. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-40 · Cited by 5,124 · see citing works
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774 · 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
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · 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
Database of homology‐derived protein structures and the structural meaning of sequence alignment
Sander, C., & Schneider, R. (1991) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340090107 · Cited by 1,638 · see citing works
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 · see citing works
The Molecular Basis of G Protein–Coupled Receptor Activation
Weis, W. I., & Kobilka, B. K. (2018) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-060614-033910 · Cited by 1,227 · 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
Protein structure prediction from sequence variation
Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622 · see citing works
Zhang, C., Freddolino, L., & Zhang, Y. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx366 · Cited by 606 · 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
Defining a new nomenclature for the structures of active and inactive kinases
Modi, V., & Dunbrack, R. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1814279116 · Cited by 367 · see citing works
A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation
Brylinski, M., & Skolnick, J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707684105 · Cited by 333 · see citing works
Self-citationTHE CLASSIFICATION AND ORIGINS OF PROTEIN FOLDING PATTERNS
Chothia, C., & Finkelstein, A. V. (1990) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.59.070190.005043 · Cited by 311 · see citing works
The protein structure prediction problem could be solved using the current PDB library
Zhang, Y., & Skolnick, J. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0407152101 · Cited by 297 · see citing works
Self-citationConformational states dynamically populated by a kinase determine its function
Xie, T., Saleh, T., Rossi, P., & Kalodimos, C. G. (2020) · Science · doi:10.1126/science.abc2754 · Cited by 266 · see citing works
MONSSTER: a method for folding globular proteins with a small number of distance restraints
Skolnick, J., Koliński, A., & Ortíz, Á. R. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0720 · Cited by 258 · see citing works
Self-citationGoodsell, D. S., Sanner, M. F., Olson, A. J., & Forli, S. (2020) · Protein Science · doi:10.1002/pro.3934 · Cited by 239 · see citing works
Cryptic binding sites on proteins: definition, detection, and druggability
Vajda, S., Beglov, D., Wakefield, A., Egbert, M. E., & Whitty, A. (2018) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2018.05.003 · Cited by 209 · see citing works
Olmea, O., & Valencia, A. (1997) · Folding and Design · doi:10.1016/s1359-0278(97)00060-6 · Cited by 204 · 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-citationLi, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz291 · Cited by 158 · see citing works
Gao, M., & Skolnick, J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1012820107 · Cited by 139 · see citing works
Self-citationFr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score
Pandit, S. B., & Skolnick, J. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-531 · Cited by 138 · see citing works
Self-citationComprehensive prediction of drug-protein interactions and side effects for the human proteome
Zhou, H., Gao, M., & Skolnick, J. (2015) · Scientific Reports · doi:10.1038/srep11090 · Cited by 138 · see citing works
Self-citationA search for the most stable folds of protein chains
Finkelstein, A. V., & Reva, B. A. (1991) · Nature · doi:10.1038/351497a0 · Cited by 136 · see citing works
Protein Conformational Populations and Functionally Relevant Substates
Ramanathan, A., Savol, A., Burger, V., Chennubhotla, C. S., & Agarwal, P. K. (2014) · Accounts of Chemical Research · doi:10.1021/ar400084s · Cited by 110 · see citing works
iAlign: a method for the structural comparison of protein–protein interfaces
Gao, M., & Skolnick, J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq404 · Cited by 98 · see citing works
Self-citationAb Initio Protein Structure Prediction Using Chunk-TASSER
Zhou, H., & Skolnick, J. (2007) · Biophysical Journal · doi:10.1529/biophysj.107.109959 · Cited by 89 · see citing works
Self-citationLakkaraju, S. K., Yu, W., Raman, E. P., Hershfeld, A. V., Fang, L., Deshpande, D. A., & MacKerell, A. D. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500729k · Cited by 66 · see citing works
How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs
Torrens‐Fontanals, M., Stępniewski, T. M., Aranda-García, D., Morales-Pastor, A., Medel-Lacruz, B., & Selent, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21165933 · Cited by 64 · 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-citationChSeq : 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
Ortíz, Á. R., Koliński, A., & Skolnick, J. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.3.1020 · Cited by 56 · see citing works
Self-citationDeepDist: real-value inter-residue distance prediction with deep residual convolutional network
Wu, T., Guo, Z., Hou, J., & Cheng, J. J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-03960-9 · Cited by 54 · see citing works
Predicting the helix packing of globular proteins by self‐correcting distance geometry
Mumenthaler, C., & Braun, W. A. (1995) · Protein Science · doi:10.1002/pro.5560040506 · Cited by 52 · see citing works
Template‐based protein structure prediction in CASP11 and retrospect of I‐TASSER in the last decade
Yang, J., Zhang, W., He, B., Walker, S. E., Zhang, H., Govindarajoo, B., Virtanen, J., Xue, Z., Shen, H., & Zhang, Y. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24918 · Cited by 50 · see citing works
Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14
Anishchenko, I. V., Baek, M., Park, H., Hiranuma, N., Kim, D. E., Dauparas, J., Mansoor, S., Humphreys, I. R., & Baker, D. A. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26194 · Cited by 50 · see citing works
Recovering physical potentials from a model protein databank
Mullinax, J. W., & Noid, W. G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006428107 · Cited by 49 · see citing works
Knowledge-based potentials for protein folding: what can we learn from known protein structures?
Godzik, A. (1996) · Structure · doi:10.1016/s0969-2126(96)00041-x · Cited by 45 · see citing works
Chen, C., Wu, T., Guo, Z., & Cheng, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26052 · Cited by 44 · see citing works
Zhou, H., Cao, H., & Skolnick, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00309 · Cited by 39 · see citing works
Self-citationZhou, H., Cao, H., Matyunina, L., Shelby, M., Cassels, L., McDonald, J. F., & Skolnick, J. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b01248 · Cited by 39 · see citing works
Self-citationTemplate‐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-citationPredicting binding sites from unbound versus bound protein structures
Clark, J. J., Orban, Z. J., & Carlson, H. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-72906-7 · Cited by 36 · see citing works
Chen, J., Wang, W., Sun, H., Pang, L., & Bao, H. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104485 · Cited by 36 · see citing works
Skolnick, J., Zhou, H., & Brylinski, M. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp211052j · Cited by 35 · see citing works
Self-citationRCSB Protein Data Bank 1D tools and services
Segura, J., Rose, Y., Westbrook, J., Burley, S. K., & Duarte, J. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1012 · Cited by 32 · see citing works
FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening
Zhou, H., Cao, H., & Skolnick, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01160 · Cited by 32 · see citing works
Self-citationLiu, J., Wu, T., Guo, Z., Hou, J., & Cheng, J. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26186 · Cited by 32 · see citing works
Mohanty, D., Dominy, B. N., Koliński, A., Brooks, C. L., & Skolnick, J. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990601)35:4 · Cited by 31 · see citing works
Self-citationAre protein-protein interfaces special regions on a protein’s surface?
Tonddast-Navaei, S., & Skolnick, J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4937428 · Cited by 31 · see citing works
Self-citationNeural network system for the evaluation of side-chain packing in protein structures
Milik, M., Koliński, A., & Skolnick, J. (1995) · Protein Engineering Design and Selection · doi:10.1093/protein/8.3.225 · Cited by 21 · see citing works
Self-citationThe role of local versus nonlocal physicochemical restraints in determining protein native structure
Skolnick, J., & Gao, M. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.10.008 · Cited by 21 · 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-citationA 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-citation
Flagged first, then by citations
1 of the 62 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Before and after AlphaFold2: An overview of protein structure prediction
Bertoline, L. M. F., Lima, A. N., Krieger, J. E., & Teixeira, S. K. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1120370 · Cited by 309 · see citing works
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252 · see citing works
Self-citationProteins Plus : a comprehensive collection of web-based molecular modeling tools
Stierand, K., Diedrich, K., Ehrt, C., Flachsenberg, F., Graef, J., Sieg, J., Penner, P., Poppinga, M., Ungethüm, A., & Rarey, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac305 · Cited by 225 · see citing works
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 · see citing works
Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening
Blanes‐Mira, C., Fernández-Aguado, P., de Andrés-López, J., Fernández-Carvajal, A. M., Ferrer-Montiel, A. V., & Fernandez-Ballester, G. J. (2022) · Molecules · doi:10.3390/molecules28010175 · Cited by 106 · see citing works
A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction
Kresoja, K., Unterhuber, M., Wachter, R., Thiele, H., & Lurz, P. C. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-00982-7 · Cited by 75 · see citing works
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Chakravarty, D., Lee, M., & Porter, L. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102973 · Cited by 74 · see citing works
Predicting protein flexibility with AlphaFold
Ma, P., Li, D., & Brüschweiler, R. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26471 · Cited by 71 · see citing works
Villanueva‐Flores, F., Sanchez-Villamil, J. I., & Garcia-Atutxa, I. (2025) · npj Vaccines · doi:10.1038/s41541-025-01258-y · Cited by 65 · see citing works
Quality Assessment of Selected Protein Structures Derived from Homology Modeling and AlphaFold
Binbay, F. A., Rathod, D., George, A. A. P., & Imhof, D. (2023) · Pharmaceuticals · doi:10.3390/ph16121662 · Cited by 62 · see citing works
Vincenzi, M., Mercurio, F. A., & Leone, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031798 · Cited by 60 · see citing works
Medical Ethics of Large Language Models in Medicine
Ong, J. C. L., Chang, Y., Wasswa, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi‐Velez, F., Lu, W., Savulescu, J., et al. (2024) · NEJM AI · doi:10.1056/aira2400038 · Cited by 59 · see citing works
Dlamini, Z., Skepu, A., Kim, N., Mkhabele, M., Khanyile, R., Molefi, T., Mbatha, S. Z., Damane, B. P., Mulaudzi, T. V., Mabongo, M., et al. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100965 · Cited by 57 · see citing works
Deep learning in bioinformatics
Yousef, M., & Allmer, J. (2023) · TURKISH JOURNAL OF BIOLOGY · doi:10.55730/1300-0152.2671 · Cited by 57 · see citing works
High-resolution cryo-EM structure of the Pseudomonas bacteriophage E217
Li, F., Hou, C.-F. D., Lokareddy, R. K., Yang, R., Forti, F., Briani, F., & Cingolani, G. (2023) · Nature Communications · doi:10.1038/s41467-023-39756-z · Cited by 52 · see citing works
HiSIF-DTA: A Hierarchical Semantic Information Fusion Framework for Drug-Target Affinity Prediction
Bi, X., Zhang, S., Ma, W., Jiang, H., & Wei, Z. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3334239 · Cited by 47 · see citing works
Xia, Y., Zhao, K., Liu, D., Zhou, X., & Zhang, G. (2023) · Communications Biology · doi:10.1038/s42003-023-05610-7 · Cited by 47 · see citing works
Zhao, K., Xia, Y., Zhang, F., Zhou, X., Li, S. Z., & Zhang, G. (2023) · Communications Biology · doi:10.1038/s42003-023-04605-8 · Cited by 46 · see citing works
Gomes, P. D. S. F. C., Gomes, D. E. B., & Bernardi, R. C. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.983306 · Cited by 42 · see citing works
Ghaffar, S. A., Tahir, H., Muhammad, S., Shahid, M., Naqqash, T., Faisal, M., Albekairi, T. H., Alshammari, A., Albekairi, N. A., & Manzoor, I. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1380732 · Cited by 40 · see citing works
Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily
Koludarov, I., Senoner, T., Jackson, T., Dashevsky, D., Heinzinger, M., Aird, S. D., & Rost, B. (2023) · Nature Communications · doi:10.1038/s41467-023-40550-0 · Cited by 38 · see citing works
Mapiour, M., & Abdelrasoul, A. (2023) · Journal of Composites Science · doi:10.3390/jcs7010028 · Cited by 37 · see citing works
Agajanian, S., Alshahrani, M., Bai, F., Tao, P., & Verkhivker, G. M. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01634 · Cited by 37 · see citing works
Rational and mechanistic approaches for improving biocatalyst performance
Phintha, A., & Chaiyen, P. (2022) · Chem Catalysis · doi:10.1016/j.checat.2022.09.026 · Cited by 36 · see citing works
LRRK2 Structure-Based Activation Mechanism and Pathogenesis
Zhang, X., & Kortholt, A. (2023) · Biomolecules · doi:10.3390/biom13040612 · Cited by 34 · see citing works
Puente-Lelièvre, C., Malik, A. J., Douglas, J., Ascher, D. B., Baker, M. A. B., Allison, J. R., Poole, A. M., Lundin, D., Fullmer, M. S., Bouckaert, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.12.571181 · Cited by 34 · see citing works
Natural Language Processing Methods for the Study of Protein–Ligand Interactions
Michels, J., Bandarupalli, R., Akbari, A. A., Le, T., Xiao, H. Y., Li, J., & Hom, E. F. Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01907 · Cited by 33 · see citing works
Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
Patne, A., Dhulipala, S. M., Lawless, W., Prakash, S., Mohapatra, S., & Mohapatra, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252212233 · Cited by 25 · see citing works
Lazou, M., Khan, O., Nguyen, T., Padhorny, D., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412719121 · Cited by 31 · see citing works
Protein multiple conformations prediction using multi-objective evolution algorithm
Hou, M.-H., Jin, S.-R., Cui, X.-Y., Peng, C.-X., Zhao, K.-L., Song, L., & Zhang, G.-J. (2023) · doi:10.1101/2023.04.21.537776 · Cited by 29 · see citing works
Dynamics of the mammalian pyruvate dehydrogenase complex revealed by in-situ structural analysis
Wang, C., Ma, C., Xu, Y., Chang, S., Wu, H., Yan, C., Chen, J., Wu, Y., An, S., Xu, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56171-8 · Cited by 27 · see citing works
Wang, J., Zhang, M., Cui, X., Gao, X., Sun, W., Ge, X., Zhang, Y., Guo, X., Han, J., Zhou, L., et al. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.03084-22 · Cited by 27 · see citing works
Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development
Hsieh, C., Giannakoulias, S. G., Petersson, E. J., & Mach, R. H. (2023) · Pharmaceuticals · doi:10.3390/ph16020317 · Cited by 27 · see citing works
Edmunds, N. S., Genc, A. G., & McGuffin, L. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae491 · Cited by 26 · see citing works
Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross‐Linking
Lu, H., Zhu, Z., Fields, L. G., Zhang, H., & Li, L. (2024) · Mass Spectrometry Reviews · doi:10.1002/mas.21908 · Cited by 26 · see citing works
Large language models facilitating modern molecular biology and novel drug development
Liu, X., Lü, Z., Wang, T., & Liu, F. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1458739 · Cited by 26 · see citing works
Wang, X., Chen, X., Wang, Z.-J., Zhuang, M., Zhong, L., Fu, C., Garcia, R., Müller, R., Zhang, Y., Yan, J., et al. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c06014 · Cited by 25 · see citing works
AI-driven innovation in antibody-drug conjugate design
Noriega, H. A., & Wang, X. S. (2025) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2025.1628789 · Cited by 25 · see citing works
Chen, P., Wang, R., Chen, G., An, B., Liu, M., Wang, Q., & Tao, Y. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153639 · Cited by 23 · see citing works
Bishop, Ö. T., Musyoka, T. M., & Barozi, V. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167610 · Cited by 23 · see citing works
Yan, W., Li, Z., Pian, C., & Wu, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac425 · Cited by 23 · see citing works
Assessment of AlphaFold2 for Human Proteins via Residue Solvent Exposure
Bæk, K. T., & Kepp, K. P. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00243 · Cited by 22 · see citing works
Sun, Y., Jia, X., Chen, D., Fu, Q., Chen, J., Yang, W., Yang, H., & Xu, X. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065762 · Cited by 22 · see citing works
Adiyaman, R., Edmunds, N. S., Genc, A. G., Alharbi, S. M. A., & McGuffin, L. J. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad078 · Cited by 22 · see citing works
AlphaFold2 in Molecular Discovery
Nunes‐Alves, A., & Merz, K. M. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01459 · Cited by 22 · see citing works
Machine Learning for Chemical Looping: Recent Advances and Prospects
Song, Y., Teng, S., Fang, D., Lu, Y., Chen, Z., Xiao, R., & Zeng, D. (2024) · Energy & Fuels · doi:10.1021/acs.energyfuels.4c02110 · Cited by 21 · see citing works
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works
AF3Complex yields improved structural predictions of protein complexes
Feldman, J. D., & Skolnick, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf432 · Cited by 20 · see citing works
Self-citationMani, H., Chang, C., Hsu, H., Yang, C.-H., Yen, J.-H., & Liou, J.-W. (2023) · Bioengineering · doi:10.3390/bioengineering10091004 · Cited by 20 · see citing works
Integrative structural analysis of Pseudomonas phage DEV reveals a genome ejection motor
Lokareddy, R. K., Hou, C.-F. D., Forti, F., Iglesias, S. M., Li, F., Pavlenok, M., Horner, D. S., Niederweis, M., Briani, F., & Cingolani, G. (2024) · Nature Communications · doi:10.1038/s41467-024-52752-1 · Cited by 18 · see citing works
Pullin, J., Rodríguez-Celma, J., Franceschetti, M., Mundy, J. E. A., Svistunenko, D. A., Bradley, J. M., Le Brun, N. E., & Balk, J. (2025) · Nature Communications · doi:10.1038/s41467-025-58853-9 · Cited by 18 · see citing works
Decentring the discoverer: how AI helps us rethink scientific discovery
Clark, E., & Khosrowi, D. (2022) · Synthese · doi:10.1007/s11229-022-03902-9 · Cited by 18 · see citing works
AlphaFold2 Update and Perspectives
Tourlet, S., Radjasandirane, R., Diharce, J., & de Brevern, A. G. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3020025 · Cited by 17 · see citing works
Tuly, J. A., Ma, H., Lee, H., Song, J., Parvez, A., Saqib, M. N., Yaseen, W., & Zhang, X. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2023.135854 · Cited by 16 · see citing works
TATDN2 resolution of R-loops is required for survival of BRCA1-mutant cancer cells
Jaiswal, A. S., Dutta, A., Srinivasan, G., Yuan, Y., Zhou, D., Shaheen, M. F., Sadideen, D. T., Kirby, A., Williamson, E. A., Gupta, Y. K., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad952 · Cited by 16 · see citing works
Huang, J., Tang, G. Q., Liu, N., Li, X., & Lu, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104466 · Cited by 15 · see citing works
Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2
Ng, T. K., Ji, J., Liu, Q., Yao, Y., Wang, W., Cao, Y., Chen, C., Lin, J., Dong, G., Cen, L., et al. (2023) · Biomolecules · doi:10.3390/biom14010014 · Cited by 15 · see citing works
Takaya, D. (2024) · Chemical and Pharmaceutical Bulletin · doi:10.1248/cpb.c23-00839 · Cited by 15 · see citing works
Xiao, X., Sarma, S., Menegatti, S., Crook, N., Magness, S. T., & Hall, C. K. (2021) · ACS Chemical Biology · doi:10.1021/acschembio.1c00743 · Cited by 15 · see citing works
Islam, S. I., Ahmed, S. S., Habib, N., Ferdous, M. A., Sanjida, S., & Mou, M. J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16383 · Cited by 14 · see citing works
Wang, F., Jiang, S., & Li, J. (2025) · Applied Sciences · doi:10.3390/app15105667 · Cited by 14 · see citing works
Using machine‐learning‐driven approaches to boost hot‐spot's knowledge
Rosário‐Ferreira, N., Bonvin, A. M. J. J., & Moreira, I. S. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1602 · Cited by 14 · see citing works
Novel computational pipelines in antiviral structure‑based drug design (Review)
Diakou, I., Papakonstantinou, E., Papageorgiou, L., Pierouli, K., Dragoumani, K., Spandidos, D. A., Bacopoulou, F., Chrousos, G. P., Eliopoulos, E., & Vlachakis, D. (2022) · Biomedical Reports · doi:10.3892/br.2022.1580 · Cited by 13 · see citing works
The physics-AI dialogue in drug design
Vargas-Rosales, P. A., & Caflisch, A. (2025) · RSC Medicinal Chemistry · doi:10.1039/d4md00869c · Cited by 13 · see citing works
Bakker, M. J., Sørensen, H. V., & Skepö, M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00971 · Cited by 13 · see citing works
Biophysical investigation of interactions between SARS‐CoV ‐2 spike protein and neuropilin‐1
Hou, D., Cao, W., Kim, S., Cui, X., Ziarnik, M., Im, W., & Zhang, X. H. (2023) · Protein Science · doi:10.1002/pro.4773 · Cited by 12 · see citing works
Enhanced thermostability of nattokinase by rational design of disulfide bond
Yu, K., Chen, L., Tang, Y., Ma, A., Zhu, W., Wang, H., Tang, X., Li, Y., & Li, J. (2025) · Microbial Cell Factories · doi:10.1186/s12934-025-02681-5 · Cited by 12 · see citing works
Impacts of a Zwitterionic Peptide on its Fusion Protein
McMullen, P., Fang, L., Qiao, Q., Shao, Q., & Jiang, S. (2022) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.2c00176 · Cited by 12 · see citing works
Hasan, M., Nishat, Z. S., Hasan, M. S., Hossain, T., & Ghosh, A. (2024) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2024.101677 · Cited by 11 · see citing works
Harihar, B., Saravanan, K. M., Gromiha, M. M., & Selvaraj, S. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01119-4 · Cited by 11 · see citing works
Regulation and induction of fungal secondary metabolites: a comprehensive review
Prakash, S., Kumari, H., Sinha, M., & Kumar, A. (2025) · Archives of Microbiology · doi:10.1007/s00203-025-04386-0 · Cited by 11 · see citing works
Structural insights into Beclin 1 interactions with it's regulators for autophagy modulation
Sarmadhikari, D., & Asthana, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.044 · Cited by 11 · see citing works
An agnostic analysis of the human AlphaFold2 proteome using local protein conformations
de Brevern, A. G. (2022) · Biochimie · doi:10.1016/j.biochi.2022.11.009 · Cited by 11 · see citing works
More than just pattern recognition: Prediction of uncommon protein structure features by AI methods
Herzberg, O., & Moult, J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221745120 · Cited by 11 · see citing works
Cryptic Oxidative Transamination of Hydroxynaphthoquinone in Natural Product Biosynthesis
Noguchi, T., Isogai, S., Terada, T., Nishiyama, M., & Kuzuyama, T. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.1c13074 · Cited by 11 · see citing works
de Rezende, S. B., de Lima, L. R., Macedo, M. L. R., Franco, O. L., & Cardoso, M. H. E. S. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230412080702 · Cited by 10 · see citing works
Miller, L. G., Demny, M., Tamamis, P., & Contreras, L. M. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.06.018 · Cited by 10 · see citing works
Cutting-edge deep-learning based tools for metagenomic research
Karin, E. L., & Steinegger, M. (2025) · National Science Review · doi:10.1093/nsr/nwaf056 · Cited by 10 · see citing works
Improving B-cell epitope prediction
Yu, H., Joseph‐McCarthy, D., & Vajda, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104489 · Cited by 11 · see citing works
Denkhaus, L., Siffert, F., & Einsle, O. (2023) · FEMS Microbiology Letters · doi:10.1093/femsle/fnad068 · Cited by 9 · see citing works
Alanazi, W. T., Meng, D., & Pollastri, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.04.005 · Cited by 9 · see citing works
Snake venom-derived peptides as anticancer candidates: Pioneering next-generation therapies
Almeida, J. R., Pinos-Tamayo, E. A., Mendes, B. D. S., Robles-Loaiza, A. A., Syahputra, R. A., Oliveira, A. G. S., & Ribeiro, R. I. M. D. A. (2025) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2025.189479 · Cited by 9 · see citing works
Yin, T., Chen, Y., Wang, Y., Su, H., Duan, C., & Liu, J. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1767821 · Cited by 9 · see citing works
Chakraborty, C., Bhattacharya, M., & Lee, S.-S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1739303 · Cited by 10 · see citing works
Cao, Y., Zhang, J., Wang, R., Sun, H., & Yan, Y. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231911104 · Cited by 9 · see citing works
Wang, X., Wang, Z.-J., Dong, Z.-Q., Yan, Y., Zhang, Y., & Huo, L. (2023) · ACS Chemical Biology · doi:10.1021/acschembio.3c00135 · Cited by 9 · see citing works
Ko, S., Kim, J., Lim, J., Lee, S., Park, J. Y., Woo, J., Scott-Nevros, Z. K., Kim, J. R., Yoon, H., & Kim, D. (2023) · mSystems · doi:10.1128/msystems.00943-23 · Cited by 8 · see citing works
Artificial intelligence in protein-based detection and inhibition of AMR pathways
Sadhukhan, S., Bhattacharya, R., Bhattcharya, D., Sahana, S., Pradhan, B., Pandit, S., Gill, H. S., Rajeev, M., Nag, M., & Lahiri, D. (2025) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-025-00710-4 · Cited by 8 · see citing works
Gene duplication captures morph-specific promoter usage in the evolution of aphid wing dimorphisms
Ziabari, O. S., Liu, F., Deem, K. D., Liu, X., Kholwadwala, A., & Brisson, J. A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420893122 · Cited by 8 · see citing works
Arenas, N. E., Pieffet, G. P., Rocha‐Roa, C., & Guerrero, M. I. (2022) · Memórias do Instituto Oswaldo Cruz · doi:10.1590/0074-02760220025 · Cited by 8 · see citing works
Dhiman, A., & Purohit, R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106951 · Cited by 8 · see citing works
High-resolution Cryo-EM Analysis of the Therapeutic Pseudomonas Phage Pa223
Hou, C.-F. D., Bellis, N., Lokareddy, R. K., Branston, S., Reid, J., Geier, R., Soriaga, A., Sim, L., Kyme, P., Birx, D. L., et al. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169386 · Cited by 7 · see citing works
Ma, X., & Si, D. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108494 · Cited by 7 · see citing works
Neuropsychopharmacology in the era of artificial intelligence and biomolecule prediction software
Garcia-Reyes, R. A., Massó-Quiñones, L. N., Ruy, H., & Castro, D. C. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-025-00038-9 · Cited by 7 · see citing works
Allemailem, K. S. (2025) · Pharmaceuticals · doi:10.3390/ph18091258 · Cited by 7 · see citing works
Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes
Yu, H., Bekar-Cesaretli, A. A., Lazou, M., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2530709123 · Cited by 7 · see citing works
Murmu, S., & Archak, S. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2228907 · Cited by 7 · see citing works
Khatua, S., Roy, A., Sen, P., & Ray, S. (2023) · Gene · doi:10.1016/j.gene.2023.147820 · Cited by 7 · see citing works
- Authors
- Skolnick, J., Gao, M., Zhou, H., & Singh, S.
- Affiliations
- Georgia Institute of Technology (US)
- Twilight Design, 4 Adams Road, Kendall Park, New Jersey 08824, United States
- Corresponding author
- Skolnick, Jeffrey
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-09-29
- Volume
- 61
- Issue
- 10
- Pages
- 4827-4831
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC8592092
- OpenAlex
- W3201869122
- Semantic Scholar
- 34ab2f845e0b6c1693a4fc92bf9593b32be0d803
- Cited by
- 251
- References
- 62
- Citations by year
- 2026: 28 · 2025: 58 · 2024: 64 · 2023: 66 · 2022: 31 · 2021: 2 · 2012: 1
- FWCI
- 12.86
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health and Human Services (GM-118039)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold2, protein structure prediction, CASP14, deep learning, protein fold recognition, side-chain packing, sequence-structure-function relationships
- MeSH terms
- Algorithms, Amino Acid Sequence, Proteins, Protein Folding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
