MPBind

MPBind predicts the binding potential of SELEX-derived aptamers using a meta-motif-based statistical framework that computes four one-sided p-values per motif to identify high-affinity aptamers and mitigate biases such as PCR amplification bias and incomplete sequencing.

Key Features:

• Meta-motif statistical framework: Implements a meta-motif–based statistical model and pipeline for analysis of SELEX-derived sequence data.
• Motif-level p-values: Calculates four distinct one-sided p-values for each motif representing features relevant to aptamer binding efficacy.
• Prediction model: Combines motif-level statistics into a prediction model to identify high-affinity aptamers from SELEX rounds.
• Bias resilience: Demonstrates robustness to common confounders such as PCR amplification bias and incomplete sequencing of aptamer pools.
• SELEX-Seq validation: Validated on human embryonic stem cell whole-cell SELEX-Seq data with reported high accuracy.

Scientific Applications:

• Aptamer candidate prioritization: Prioritizes enriched sequences from SELEX rounds for downstream experimental validation based on predicted binding potential.
• SELEX-Seq analysis: Analyzes SELEX-Seq datasets, including whole-cell SELEX from human embryonic stem cells, to detect binding-relevant motifs.
• Bias assessment and correction: Identifies motifs whose significance is resilient to PCR amplification bias and incomplete sequencing, reducing false positives.
• High-affinity aptamer discovery: Facilitates selection of high-affinity aptamers from high-throughput SELEX experiments.

Methodology:

Computes four one-sided p-values per motif within a meta-motif-based statistical framework applied to SELEX-derived sequence data to generate a binding-potential prediction model.