Minmotif Miner

Minmotif Miner identifies and analyzes short contiguous peptide minimotifs within protein sequences to predict functions such as post-translational modifications (PTMs), protein–protein interactions, and protein trafficking.

Key Features:

• Extensive Motif Database: Contains approximately 300,000 minimotifs in release 3, expanded from about 5,000 motifs in earlier versions.
• Functional Prediction and Ranking: Predicts motif-based functions in user-supplied protein sequences and applies ranking approaches to prioritize predicted motifs.
• Validation on Confirmed Examples: Validated using thousands of confirmed examples, including successful prediction of previously unidentified 14-3-3 motifs.
• False-positive Filters and Linear Regression Scoring: Incorporates false-positive filters and linear regression scoring techniques to improve prediction accuracy and reduce false positives.
• SNP Analysis Capabilities: Includes expanded single nucleotide polymorphism (SNP) analysis to assess the impact of sequence variants on minimotifs.
• Semantic Model and Trained Algorithms: Employs a rich semantic model and trained algorithms for motif detection and functional assignment.

Scientific Applications:

• Protein Function Prediction: Predicts new functions in proteins based on detected minimotifs.
• Disease Variant Interpretation: Identifies potential causes of disease linked to motif-altering sequence variations.
• Study of PTMs and Interactions: Supports analysis of post-translational modifications and protein–protein interactions mediated by minimotifs.

Methodology:

Utilizes a rich semantic model and trained algorithms to analyze protein sequences for minimotifs, leverages the motif database, and applies ranking approaches, false-positive filters, and linear regression scoring to reduce false positives.