ReviewGenomics, proteomics & bioinformatics2023
Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.
Review in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- MultilevelCurrent research in structural biology · 2026Article
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Evaluation of AI-Predicted GH11 Xylanase Models Against a Previously Unreported Experimental Structure: Implications for Conformational Accuracy and Ligand Binding.International journal of molecular sciences · 2026Article
- AI meets RNA: revolutionizing structure prediction.Science China. Life sciences · 2025Article
- TruMPET: A New Method for Protein Secondary Structure Prediction Using Neural Networks Trained on Multiple Pre-Selected Physicochemical and Structural Features.International journal of molecular sciences · 2025Article
- X-ray crystallography reveals insulin lisargine structure and mechanisms of glucose regulation.Scientific reports · 2025Article
- Deep-learning structure elucidation from single-mutant deep mutational scanning.Nature communications · 2025Article
- Article
- Characterization of soil-derived Bacillus subtilis metabolites against breast cancer: In vitro and in silico studies.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
- Enhancing allergenicity risk assessment for novel foods in the EU: insights from the updated EFSA guidance - requirements, knowledge gaps and research needs.Frontiers in toxicology · 2025Review
- Balancing speed and precision in protein folding: a comparison of AlphaFold2, ESMFold, and OmegaFold.Frontiers in genetics · 2025Article
- An overview on olfaction in the biological, analytical, computational, and machine learning fields.Archiv der Pharmazie · 2025Review
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Research square · 2024Article
- Unraveling the metabolic potential of biocontrol fungi through omics data: a key to enhancing large-scaleapplication strategies.Acta biochimica et biophysica Sinica · 2024Review
- Exploring DNA Damage and Repair Mechanisms: A Review with Computational Insights.Biotech (Basel (Switzerland)) · 2024Review
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
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- Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem - finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.
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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.