ArticlePLoS computational biology2022
The structural coverage of the human proteome before and after AlphaFold.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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62 citing papers in PubMed.
- Structure-Based Network Analysis of AlphaFold Structure Predictions Identifies Putative Causative Variants of Inherited Retinal Disease.Investigative ophthalmology & visual science · 2026Article
- Benchmarking protein sequence and structure search methods for remote homology detection.Genome biology · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Article
- Structural genomics sheds light on protein functions and remote homologs across the insect tree of life.Cell research · 2026Article
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Elucidating the protein interaction network of one of the largest icosahedral capsids in the virosphere.The EMBO journal · 2026Article
- Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Assessing the relation between protein phosphorylation, AlphaFold3 models, and conformational variability.Protein science : a publication of the Protein Society · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Protein structure shapes natural genetic variation andbioRxiv : the preprint server for biology · 2025Article
- Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic disease phenotypes.Nature communications · 2025Article
- Oncodrive3D: fast and accurate detection of structural clusters of somatic mutations under positive selection.Nucleic acids research · 2025Article
- MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling.Acta pharmacologica Sinica · 2025Article
- Structural biology in variant interpretation: Perspectives and practices from two studies.American journal of human genetics · 2025Review
- Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- Deep Learning Approaches for the Prediction of Protein Functional Sites.Molecules (Basel, Switzerland) · 2025Review
- Structural and functional prediction, evaluation, and validation in the post-sequencing era.Computational and structural biotechnology journal · 2024Review
2 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.
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