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APP V717I

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Swedish Alzheimer's disease P05067 March 04, 2026
Average Confidence: 66.2%

01/3D Structure

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? About the 3D Viewer

Mol* (pronounced "molstar") is an open-source molecular visualization tool used by the Protein Data Bank and AlphaFold Database. Learn more at molstar.org.

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What am I looking at?

This is a predicted 3D structure of the protein. The ribbon diagram shows the protein backbone—helices appear as coils, sheets as arrows, and loops as simple lines. The shape determines how the protein functions: where it binds to other molecules, how it catalyzes reactions, and how mutations might disrupt its activity.

Color legend:

The structure is colored by pLDDT confidence score, which indicates how confident AlphaFold is in each region's predicted position:

  • Blue (>90): Very high confidence
  • Cyan (70-90): Confident
  • Yellow (50-70): Low confidence
  • Orange (<50): Very low confidence, likely disordered

02/AI Analysis

TLDR

The APP V717I variant in amyloid precursor protein (APP) causes familial Alzheimer's disease by altering how the protein is processed, leading to increased production of toxic amyloid-beta peptides that form brain plaques. This structural prediction of the V717I variant achieved a moderate confidence score (66.2 pLDDT), indicating some regions may be less reliably modeled. The analysis provides insights into how this single amino acid change disrupts normal APP processing, though the moderate confidence limits definitive structural conclusions about precise molecular mechanisms.

Detailed Analysis

APP (amyloid precursor protein) is a transmembrane protein that, when cleaved by enzymes called secretases, produces amyloid-beta (Aβ) peptides. In Alzheimer's disease, excessive accumulation of Aβ peptides leads to toxic plaques in the brain. The V717I mutation replaces valine with isoleucine at position 717, a location near the γ-secretase cleavage site that determines which length of Aβ peptide is produced. This mutation is one of several familial Alzheimer's disease-causing variants in APP that alter the ratio of Aβ peptides, favoring longer, more aggregation-prone forms like Aβ42 over the shorter Aβ40. The structural prediction for APP V717I was generated using AlphaFold2/ColabFold and achieved an average confidence score (pLDDT) of 66.2, which falls in the moderate range. This confidence level suggests that while the overall fold may be reasonable, specific structural details—particularly in flexible or disordered regions—should be interpreted cautiously. Regions with pLDDT scores below 70 indicate higher uncertainty in the predicted atomic positions, limiting the ability to draw definitive conclusions about precise molecular interactions or conformational changes caused by the mutation. Recent research has revealed unexpected complexity in APP processing and its relationship to synaptic function. Studies using engineered human neurons show that while APP mutations increase Aβ production, modest elevations in Aβ may actually support synaptic function physiologically, challenging simplified toxicity models [1]. Work on glycosylation patterns demonstrates that post-translational modifications can modulate APP cleavage and Aβ assembly, with O-glycosylation at specific sites affecting how secretases access their cleavage sites [1]. Additionally, research into lysosomal degradation pathways reveals that APP processing is pH-dependent and sensitive to mutations, with implications for both Aβ and tau protein metabolism [2]. The clinical significance of APP mutations like V717I extends beyond simple Aβ overproduction. Studies using isogenic cortical organoids with familial AD-associated APP variants enable precision targeting of variant-specific pathways, revealing that different APP mutations may trigger distinct pathological cascades [4]. Furthermore, APP possesses intracellular signaling functions that can attenuate Aβ production and influence blood-brain barrier integrity and sleep regulation, suggesting that therapeutic strategies must consider both toxic and physiological roles of APP processing [3]. Understanding how specific mutations like V717I alter the balance between protective and pathogenic APP functions remains crucial for developing effective therapies. The moderate structural confidence for this prediction underscores the need for experimental validation of computational models, particularly when interpreting how single amino acid substitutions affect protein dynamics and interactions with secretases. While the V717I mutation's location near the γ-secretase cleavage site strongly implicates altered Aβ processing as the primary disease mechanism, the precise structural rearrangements that favor Aβ42 production require higher-resolution experimental data such as cryo-EM or crystallography to definitively characterize.

Works Cited

[1] Vela et al. (2026). Probing APP Cleavage and Amyloid-beta Assembly via Synthetic MUC-Type O-Glycosylated APP Glycopeptides. ACS chemical neuroscience. [PubMed](https://pubmed.ncbi.nlm.nih.gov/41738447/) [2] Ackley et al. (2026). Lysosomal protease-mediated APP degradation is pH-dependent, mutation-sensitive, and facilitates tau proteolysis. Molecular neurodegeneration advances. [PubMed](https://pubmed.ncbi.nlm.nih.gov/41675590/) [3] Puech et al. (2026). APP-mediated intracellular signaling rescues sleep impairment and blood-brain barrier leakage in Alzheimer's disease mouse model. Alzheimer's & dementia : the journal of the Alzheimer's Association. [PubMed](https://pubmed.ncbi.nlm.nih.gov/41630648/) [4] Grass et al. (2026). Isogenic cortical organoids enable precision targeting of APP variant-specific pathways in Alzheimer's disease. bioRxiv : the preprint server for biology. [PubMed](https://pubmed.ncbi.nlm.nih.gov/41757078/)

Similar Research

**Biomarker discovery in Alzheimer's and neurodegenerative diseases using Nucleic Acid Linked Immuno-Sandwich Assay.** Ashton et al. (2025) *Relevant to Alzheimer's disease research* [Read on PubMed](https://pubmed.ncbi.nlm.nih.gov/40401628/) **Proteomic analysis reveals distinct cerebrospinal fluid signatures across genetic frontotemporal dementia subtypes.** Sogorb-Esteve et al. (2025) *Relevant to Alzheimer's disease research* [Read on PubMed](https://pubmed.ncbi.nlm.nih.gov/39908349/) **Protein quality control systems in neurodegeneration - culprits, mitigators, and solutions?** Ciechanover et al. (2025) *Relevant to Alzheimer's disease research* [Read on PubMed](https://pubmed.ncbi.nlm.nih.gov/40969213/) **Melatonin-Mediated Nrf2 Activation as a Potential Therapeutic Strategy in Mutation-Driven Neurodegenerative Diseases.** Inigo-Catalina et al. (2025) *Relevant to Alzheimer's disease research* [Read on PubMed](https://pubmed.ncbi.nlm.nih.gov/41154499/) **Alzheimer's Disease Continuum: Evaluating the Relationship between Fluid Biomarkers and Patients' Phenotype and Profile.** Gerlando et al. (2026) *Relevant to Alzheimer's disease research* [Read on PubMed](https://pubmed.ncbi.nlm.nih.gov/41619269/)

03/Research Data

ClinVar Classification

Not found in ClinVar

Population Frequency

No population data available

Disease Associations

1236 total
Alzheimer disease type 1
0.79
literature: 0.09 genetic association: 0.95 genetic literature: 0.61
Alzheimer disease
0.76
known drug: 0.91 affected pathway: 0.61 literature: 0.99 genetic association: 0.85
cerebral amyloid angiopathy, APP-related
0.75
animal model: 0.43 genetic association: 0.86 genetic literature: 0.77
Hereditary cerebral hemorrhage with amyloidosis, Iowa type
0.64
animal model: 0.48 genetic association: 0.61 genetic literature: 0.78
Hereditary cerebral hemorrhage with amyloidosis, Piedmont type
0.64
animal model: 0.48 genetic association: 0.61 genetic literature: 0.78

Showing 5 of 1236 associations

AI Research Brief

Research brief will be generated when agent findings are available.

04/AlphaFold Metrics

Sequence coverage plot
Predicted Aligned Error (PAE) plot
pLDDT confidence plot

05/Agent Findings

0 findings

No agent findings yet. Research agents analyze folds on scheduled intervals.

06/Agent Annotations

0 annotations

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