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AI Tool
AlphaMissense Review
AlphaMissense is a deep learning method developed by DeepMind for predicting the pathogenicity of missense variants in human proteins.
1AlphaMissense classifies over 216 million possible single amino acid changes across 19,233 human proteins.
2The model predicts 57% of these variants as likely benign and 32% as likely pathogenic.
3It achieves 90% precision in classifying variants from the ClinVar database.
4AlphaMissense predictions became available under a CC BY v. 4 license in March 2024, allowing free commercial and research use.
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AlphaMissense at a Glance
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overview
What is AlphaMissense?
AlphaMissense is a deep learning method developed by DeepMind that enables researchers, clinicians, and scientists to predict the pathogenicity of missense variants in human proteins. It classifies over 216 million possible single amino acid changes across 19,233 human proteins. Developed by Google DeepMind, AlphaMissense is an AI model adapted from AlphaFold, fine-tuned on human and primate variant population frequency databases. It combines structural context and evolutionary conservation to classify variants as likely benign or likely pathogenic, assigning a pathogenicity score between 0 (benign) and 1 (pathogenic). The model categorizes 89% of these variants, with 11% remaining uncertain. This tool helps fill knowledge gaps for "variants of unknown significance" (VUS) where experimental or clinical data is limited.
quick facts
Quick Facts
| Attribute | Value |
|---|---|
| Developer | DeepMind (Google DeepMind) |
| Business Model | Freemium |
| Pricing | Core predictions free under CC BY v. 4 license |
| Platforms | Web |
| API Available | No |
| Integrations | Ensembl, UniProt, ProtVar, AlphaFold Database |
| Training on User Data | Never |
features
Key Features of AlphaMissense
AlphaMissense provides a comprehensive set of features for the analysis and classification of missense genetic variants, leveraging advanced AI and biological data.
- 1Predicts pathogenicity of missense variants in human proteins.
- 2Classifies variants as likely benign or likely pathogenic with a score between 0 and 1.
- 3Combines structural context for prediction, adapted from AlphaFold's protein structure capabilities.
- 4Utilizes evolutionary conservation from related protein sequences for prediction.
- 5Fine-tuned on human and primate variant population frequency databases.
- 6Analyzes over 216 million possible single amino acid changes across 19,233 human proteins.
- 7Identifies mutational hot/cold spots within protein structures.
- 8Supports analysis, visualization, validation, and benchmarking of missense mutation predictions.
use cases
Who Should Use AlphaMissense?
AlphaMissense is designed for professionals in genetics, molecular biology, and clinical diagnostics who require accurate and scalable predictions for missense variant pathogenicity.
- 1Researchers: To accelerate disease research, uncover previously unknown pathogenic mutations, and understand the molecular effects of specific amino acid changes.
- 2Clinicians: For enhancing rare disease diagnostics, prioritizing variants for testing in patients, and aiding in patient diagnosis and treatment development for genetic disorders.
- 3Scientists in molecular biology, clinical, and statistical genetics: To prioritize resources, accelerate studies, and investigate missense mutations in human protein-coding genes.
- 4Geneticists: For informing studies of complex traits influenced by multiple genetic factors and addressing the vast number of 'variants of unknown significance' (VUS).
pricing
AlphaMissense Pricing & Plans
AlphaMissense operates on a freemium model. As of March 2024, AlphaMissense predictions are freely available for both commercial and research use under a CC BY v. 4 license. This allows broad access to its core functionality without direct cost for variant pathogenicity predictions.
- 1Core Predictions: Free under CC BY v. 4 license for commercial and research use.
competitors
AlphaMissense vs Competitors
AlphaMissense builds upon the structural prediction capabilities of AlphaFold, integrating evolutionary constraints and structural context. It has demonstrated high accuracy in distinguishing disease-causing variants from benign ones, often outperforming many other computational tools.
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PrimateAI-3D↗
Quantifies missense variant pathogenicity in humans using a deep-learning network trained on genetic variants from 233 primate species.
PrimateAI-3D is directly compared to AlphaMissense, with some studies suggesting it outperforms AlphaMissense in real-world cohorts, while AlphaMissense might perform slightly better on ClinVar benchmarks. Both are deep learning models for missense variant pathogenicity, but PrimateAI-3D's training on primate evolution offers a distinct approach.
2
3Cnet (by 3billion)↗
Achieves robust variant pathogenicity prediction and classification by learning from clinical data, common variant data, and conservation data, with high sensitivity.
3Cnet claims higher sensitivity compared to other pathogenicity prediction tools, potentially reclassifying more Variants of Uncertain Significance (VUS) as pathogenic/likely pathogenic. Like AlphaMissense, it's an AI tool for pathogenicity prediction, but 3Cnet's training data and claimed sensitivity are key distinctions.
3
PathoPredictor↗
An interpretable machine-learning framework designed to distinguish pathogenic from benign missense variants using curated clinical variant data and functional annotations.
PathoPredictor emphasizes interpretability, which is a key differentiator from many complex AI models like AlphaMissense that often produce numerical predictions without explicit explanations. Both aim to classify missense variant pathogenicity, but PathoPredictor prioritizes understanding the model's decision-making.
4
DAVE (MOLGENIS DAVE)↗
Predicts pathogenicity of missense variants and provides interpretable insights via contributions of functionally relevant features based on protein modeling, prioritizing physical explainability.
DAVE explicitly prioritizes physical explainability over pure predictive power, offering unique and complementary evidence for variant classification, unlike AlphaMissense which primarily provides numerical predictions. Both use AI for missense variant pathogenicity, but DAVE focuses on providing mechanistic understanding.
5
Enigma AI Variant Interpretation Tool (by Enigma Genomics)↗
Provides accurate genetic analysis and informed insights by integrating various clinical databases and offering ACMG classification using AI.
While AlphaMissense focuses specifically on predicting missense variant pathogenicity, Enigma AI appears to be a broader variant interpretation tool that includes ACMG classification, a standard for clinical variant interpretation. Its scope might be wider, but it directly addresses variant interpretation using AI.
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Frequently Asked Questions
+What is AlphaMissense?
AlphaMissense is a deep learning method developed by DeepMind that enables researchers, clinicians, and scientists to predict the pathogenicity of missense variants in human proteins. It classifies over 216 million possible single amino acid changes across 19,233 human proteins.
+Is AlphaMissense free?
Yes, AlphaMissense operates on a freemium model. As of March 2024, its core predictions are freely available for both commercial and research use under a CC BY v. 4 license.
+What are the main features of AlphaMissense?
AlphaMissense predicts the pathogenicity of missense variants in human proteins, classifying them as likely benign or likely pathogenic. It combines structural context from AlphaFold and evolutionary conservation, fine-tuned on human and primate variant population frequency databases. It analyzes over 216 million possible single amino acid changes and provides pathogenicity scores.
+Who should use AlphaMissense?
AlphaMissense is primarily intended for researchers, clinicians, and scientists working to understand genetic variation and disease. This includes those involved in accelerating disease research, enhancing rare disease diagnostics, understanding molecular effects of mutations, and investigating missense mutations in human protein-coding genes.
+How does AlphaMissense compare to alternatives?
AlphaMissense leverages AlphaFold's structural modeling and achieves high accuracy (90% precision on ClinVar). It differs from tools like PrimateAI-3D by its specific training data and approach, from 3Cnet by its claimed sensitivity, and from PathoPredictor and DAVE by its focus on predictive power over explicit interpretability. Compared to broader tools like Enigma AI, AlphaMissense specializes specifically in missense variant pathogenicity.