ReviewThe Journal of biological chemistry2021
Toward the solution of the protein structure prediction problem.
Review in The Journal of biological chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed, 122 citations in OpenAlex.
- NEFFy: a versatile tool for computing the number of effective sequences.Bioinformatics (Oxford, England) · 2026Article
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Bridging Scales: Coarse-Grained Protein Models in Computational Biology.Advances in experimental medicine and biology · 2026Review
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Multiple conformational states assembly of multidomain proteins using evolutionary algorithm based on structural analogues and sequential homologues.Fundamental research · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Integrating diverse experimental information to assist protein complex structure prediction by GRASP.Nature methods · 2025Article
- A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.Briefings in bioinformatics · 2025Article
- Comprehensive engineering of novel glycosyltransferase for efficient, donor-promiscuous, and regioselective glycosylation of flavonoids.Science advances · 2025Article
- Metabolic reprogramming and computation-aided protein engineering for high-level de novo biosynthesis for 2-phenylethanol inSynthetic and systems biotechnology · 2025Article
- Protein Catalysis Through Structural Dynamics: A Comprehensive Analysis of Energy Conversion in Enzymatic Systems and Its Computational Limitations.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Structural Biology in the AlphaFold Era: How Far Is Artificial Intelligence from Deciphering the Protein Folding Code?Biomolecules · 2025Review
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- Evolving ω-amine transaminaseApplied and environmental microbiology · 2024Article
- Petabase-Scale Homology Search for Structure Prediction.Cold Spring Harbor perspectives in biology · 2024Review
- iBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors.International journal of molecular sciences · 2024Article
- Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus.Journal, genetic engineering & biotechnology · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.Nature methods · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.