ReviewCurrent opinion in structural biology2025
Leveraging protein structural information to improve variant effect prediction.
Review in Current opinion in structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- ProteoCast: a web server to predict, validate, and interpret missense variant effects.Journal of molecular biology · 2026Article
- Why variant effect predictors and multiplexed assays agree and disagree.Nature communications · 2026Article
- ARID1B damaging variants from more than one million genomes, cause human diseases by impairing protein-protein interactions, stability, and regulation.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAEpilepsia open · 2026Review
- Deep mutational scan of the pore of the cold-sensing TRPM8 channel.bioRxiv : the preprint server for biology · 2026Article
- Feed-Forward Deep Neural Networks Predict Substrate-Specific Effects of Transporter Variants to Explain Drug Response Variability.Clinical and translational science · 2026Article
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- Systematic structure-based analysis of RET variants in MEN2A and Hirschsprung's disease, and the paradoxical co-occurrence of both conditions.Disease models & mechanisms · 2026Article
- DEX: a consensus-based amino acid exchangeability measure for improved codon substitution modelling.bioRxiv : the preprint server for biology · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- Predicted protein 3D structures provide essential insights into the genetic architecture underlying phenotypic diversity in maize.Genome research · 2026Article
- DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins.Frontiers in bioinformatics · 2026Article
- Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation.bioRxiv : the preprint server for biology · 2025Article
- Structural biology in variant interpretation: Perspectives and practices from two studies.American journal of human genetics · 2025Review
- Complementary Roles of Structure and Variant Effect Predictors in RyR1 Clinical Interpretation.Human mutation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Despite massive sequencing efforts, understanding the difference between human pathogenic and benign variants remains a challenge. Computational variant effect predictors (VEPs) have emerged as essential tools for assessing the impact of genetic variants, although their performance varies. Initially, sequence-based methods dominated the field, but recent advances, particularly in protein structure prediction technologies like AlphaFold, have led to an increased utilization of structural information by VEPs aimed at scoring human missense variants. This review highlights the progress in integrating structural information into VEPs, showcasing novel models such as AlphaMissense, PrimateAI-3D, and CPT-1 that demonstrate improved variant evaluation. Structural data offers more interpretability, especially for non-loss-of-function variants, and provides insights into complex variant interactions in vivo. As the field advances, utilizing biomolecular complex structures will be pivotal for future VEP development, with recent breakthroughs in protein-ligand and protein-nucleic acid complex prediction offering new avenues.
Indexed as
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Registered trials
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.