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Updated: May 6, 2026
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Integrating AlphaFold2 models and clinical data to improve the assessment of Short Linear Motifs (SLiMs) and their
Franco Gino Brunello1, Lorenzo Erra1, Juan Nicola2
1Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellón 2 de Ciudad Universitaria, Ciudad de Buenos Aires, Argentina.
Abstract:
Short Linear Motifs (SLiMs) are protein functionally relevant regions that mediate reversible protein-protein interactions. Variants that disrupt SLiMs can lead to numerous Mendelian diseases. Although various bioinformatic tools have been developed to identify SLiMs, most suffer from low specificity. In our previous work, we demonstrated that integrating sequence variant information with structural analysis can enhance the prediction of true functional SLiMs while simultaneously generating tolerance matrices that indicate whether each of the 19 possible single amino acid substitutions (SASs) is tolerated. However, the scarcity of representative crystallographic structures of SLiM-receptor complexes posed a significant limitation. In this study, we demonstrate that these interactions can be modeled using AlphaFold2 (AF2) to generate high-quality structures that serve as input for our MotSASi method. These AF2-derived structures show robust performance, both in reproducing known structures deposited in the Protein Data Bank (PDB) and in reflecting the deleterious effects of known sequence variants. This updated version of MotSASi expands the repertoire of high-confidence predicted SLiMs and provides a comprehensive catalog of variants located within SLiMs, along with their respective deleteriousness assessments. When compared to AlphaMissense, MotSASi demonstrates superior performance in predicting variant deleteriousness. By contributing to the accurate identification and interpretation of variants, this work aligns with ACMG/AMP standards and aims to improve diagnostic rates in clinical genomics.
Insights
Short Linear Motifs (SLiMs) are crucial for protein interactions and disease. This study enhances SLiM prediction using AlphaFold2, improving variant deleteriousness assessment for better clinical genomics.
Area of Science:
- Genomics
- Bioinformatics
- Structural Biology
Background:
- Short Linear Motifs (SLiMs) mediate critical protein interactions, and variants disrupting them cause Mendelian diseases.
- Existing bioinformatic tools for SLiM identification often lack specificity.
- Predicting the functional impact of sequence variants in SLiMs is challenging due to limited structural data.
Purpose of the Study:
- To integrate AlphaFold2-predicted structures into the MotSASi method for enhanced SLiM identification and variant effect prediction.
- To generate accurate tolerance matrices for single amino acid substitutions within SLiMs.
- To improve the prediction of variant deleteriousness and aid clinical genomics.
Main Methods:
- Utilized AlphaFold2 (AF2) to model SLiM-receptor complex structures, overcoming limitations of scarce experimental data.
- Applied the MotSASi method using AF2-generated structures as input.
- Compared the performance of the updated MotSASi against AlphaMissense for variant deleteriousness prediction.
Main Results:
- AF2-derived structures accurately reproduced known Protein Data Bank (PDB) structures and reflected known variant effects.
- The updated MotSASi method demonstrated robust performance in predicting SLiM function and variant impact.
- MotSASi showed superior performance over AlphaMissense in assessing variant deleteriousness.
Conclusions:
- AlphaFold2 modeling enables high-quality structural input for MotSASi, expanding the prediction of functional SLiMs.
- This approach provides a comprehensive catalog of SLiM variants and their deleteriousness, aligning with clinical genomics standards.
- The improved variant interpretation aids in diagnosing genetic diseases and advancing clinical genomics.
