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Related Experiment Video

Updated: May 15, 2026

A Protocol for Phage Display and Affinity Selection Using Recombinant Protein Baits

A Protocol for Phage Display and Affinity Selection Using Recombinant Protein Baits

Published on: February 16, 2014

Semiparametric Bayesian inference for phage display data.

Luis G León-Novelo¹, Peter Müller, Wadih Arap

¹Department of Mathematics, University of Louisiana at Lafayette, Lafayette, Louisiana 70504-1010, USA. luis@louisiana.com

|January 24, 2013

Summary

This summary is machine-generated.

This study introduces novel statistical methods for analyzing human phage display experiments to identify high-affinity ligands. The research focuses on inferring monotonic increases in peptide-tissue binding over experimental stages, addressing multiplicity issues with two distinct approaches.

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Area of Science:

Biostatistics
Molecular Biology
Bioinformatics

Background:

Phage display experiments are crucial for identifying high-affinity ligands for specific tissues.
Analyzing tripeptide counts across multiple stages presents statistical challenges, particularly in identifying significant binding trends.

Purpose of the Study:

To develop and evaluate statistical methods for inferring monotonic increases in mean counts over experimental stages in human phage display data.
To identify peptide-tissue pairs exhibiting significant binding affinity increases.
To address the multiplicity problem inherent in analyzing large-scale screening data.

Main Methods:

Utilized a semiparametric Dirichlet process mixture of Poisson model for data analysis.
Formulated the research question as inference on the monotonicity of mean counts across experimental stages.
Proposed two distinct statistical approaches to manage the multiplicity issue: one based on controlling the posterior expected false discovery rate and another incorporating a utility function that weights the magnitude of increase.

Main Results:

The semiparametric model provides a posterior distribution suitable for inferring monotonicity.
The first multiplicity control approach, while effective, may overlook the relative size of binding increases.
The second approach, using a utility function, offers a more nuanced selection by considering the magnitude of observed increases, leading to a more informative list of peptide-tissue pairs.

Conclusions:

The study presents a robust statistical framework for analyzing human phage display data to identify tissue-specific ligands.
Two novel methods are proposed to effectively handle the multiplicity problem in identifying significant peptide-tissue interactions.
The utility function-based approach is recommended for its ability to prioritize ligands based on both significance and the magnitude of binding affinity increase.