ArticleComputational and structural biotechnology journal2022
Obtaining protein foldability information from computational models of AlphaFold2 and RoseTTAFold.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 29 citations in OpenAlex.
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Integrative frameworks for plastic biodegradation in insect-microbiome systems: mechanistic insights, emerging multi-omics and enzyme engineering perspectives.Biodegradation · 2026Review
- Integrating AlphaFold2, RoseTTAFold2, and HADDOCK to refine Protein-Protein Interaction (PPI) candidate selection: A case study with ZWINT.Biochemistry and biophysics reports · 2026Article
- The multidimensional significance of virus-host protein interactions and their implications in the antiviral defense of plants.Stress biology · 2026Review
- Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.Protein science : a publication of the Protein Society · 2026Article
- Beyond AlphaFold: Charting the Future of AI in Protein Structure Prediction.Current drug targets · 2026Article
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- From Molecules to Medicines: The Role of AI-Driven Drug Discovery Against Alzheimer's Disease and Other Neurological Disorders.Pharmaceuticals (Basel, Switzerland) · 2025Review
- The road toward AAV-mediated gene therapy of Duchenne muscular dystrophy.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
- Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses.Communications biology · 2024Article
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- Advanced computational approaches to understand protein aggregation.Biophysics reviews · 2024Review
- Next generation of multispecific antibody engineering.Antibody therapeutics · 2024Review
- Artificial Intelligence-based database for prediction of protein structure and their alterations in ocular diseases.Database : the journal of biological databases and curation · 2023Article
- AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.Drug discovery today · 2023Review
- Effect of Non-Lethal Selection on Spontaneous Revertants of Frameshift Mutations: The Escherichia coli hisF CaseMicroorganisms · 2022Article
- An integrated protein structure fitness scoring approach for identifying native-like model structures.Computational and structural biotechnology journal · 2022Article
- Challenges in describing the conformation and dynamics of proteins with ambiguous behavior.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent breakthrough from AlphaFold2 and RoseTTAFold set a profound milestone for solving the protein folding problem, but they were not explicitly trained to predict protein foldability, i.e., if a protein can really fold into the predicted 3D structure. We wondered if the computational models from AlphaFold2 and RoseTTAFold might carry protein foldability information. Therefore, we predicted the structural models of 159 circular permutants and 158 alanine insertion mutants of the 159-residue dihydrofolate reductase. Our data showed that although AlphaFold2 and RoseTTAFold cannot directly identify unfoldable proteins, the RMSD values of computational models are correlated with protein foldability, with higher RMSD values indicating lower protein foldability. Furthermore, this correlation is independent of secondary structures, and the RMSD values of computational models are quantitatively correlated with protein foldability but not protein functions. Additionally, using a dataset of 129 de novo designed proteins, we showed that inter-model RMSD values between AlphaFold2 models and RoseTTAFold models are a good indicator of protein foldability. At last, we showed that inter-model RMSD values are also useful for evaluating protein solubility by modeling 1664 natural proteins. Our work could be of great value to the design of novel proteins and the prediction of protein foldability.
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