ArticlePhysical review letters2022
State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold.
Article in Physical review letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers.
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Who cites it
143 citing papers in PubMed.
- Sexual Dimorphism and Intraspecific Toxin Variation in Crotalus durissus terrificus Venom: Insights from Proteomics, Peptidomics, and Structural Modeling.Journal of proteome research · 2026Article
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Synthetic sequence alignments as programmable probes of learned conformational landscapes in deep learning protein structure predictors.Bioinformatics (Oxford, England) · 2026Article
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- Discovering multiscale deep formulas in complex systems via neural-guided lambda calculus.Nature communications · 2026Article
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- Distance-Restraint-Guided Diffusion Models for Sampling Protein Conformational Changes and Ligand Dissociation Pathways.Journal of chemical theory and computation · 2026Article
- Residue burial encodes a protein's fold.bioRxiv : the preprint server for biology · 2026Article
- Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence.The journal of physical chemistry letters · 2026Article
- Exploration of the structural and functional diversity in the metamorphic RfaH subfamily.bioRxiv : the preprint server for biology · 2026Article
- De novo design of GPCR exoframe modulators.Nature · 2026Article
- AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.Nature methods · 2026Article
- Disentangling coevolutionary constraints for modeling protein conformational heterogeneity.Communications chemistry · 2026Article
- Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins.bioRxiv : the preprint server for biology · 2026Article
- Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.ArXiv · 2026Article
- Article
- Prediction of pre- and postfusion conformations of class I fusion proteins with AlphaFold2.PloS one · 2026Article
- CASP16 Protein Monomer Structure Prediction Assessment.Proteins · 2026Article
- Article
83 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
The problem of predicting a protein's 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like alphafold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models' accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a protein's structure from only its primary sequence by learning an approximate biophysical energy function. We provide evidence that alphafold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation. We demonstrate that alphafold'slearned energy function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data. Finally, we explore several applications of this energy function, including the prediction of protein structures without multiple sequence alignments.
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