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Updated: Dec 23, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning.

Xinmeng Li¹, James A Van Deventer^2,3, Soha Hassoun^1,2

¹Department of Computer Science, Tufts University, Massachusetts, United States of America.

Plos Computational Biology

|April 28, 2020

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We developed a new computational pipeline to identify antibody features linked to inhibitor functions. This tool aids in designing effective antibody-based therapeutics by analyzing antibody sequences for distinct characteristics.

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Antibodies are crucial for targeted therapies, but linking their sequences to inhibitory functions remains challenging.
Understanding the sequence-function relationship is key for developing novel antibody-based drugs.

Purpose of the Study:

To develop and validate a computational pipeline (ASAP-SML) for identifying distinguishing features of antibody sequences with specific inhibitory functions.
To enable the rational design of antibody inhibitors by uncovering sequence-based predictors of function.

Main Methods:

The Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML) was developed.
The pipeline extracts feature fingerprints (germline, CDR canonical structure, isoelectric point, positional motifs) from antibody sequences.

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Related Experiment Videos

Last Updated: Dec 23, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

Analysis of Histone Antibody Specificity with Peptide Microarrays

Analysis of Histone Antibody Specificity with Peptide Microarrays

Published on: August 1, 2017

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Machine learning and statistical testing were employed to identify distinguishing features and their combinations.

Main Results:

ASAP-SML successfully identified distinct feature values and combinations differentiating antibody sequences targeting matrix metalloproteinases (MMPs) from non-targeting sequences.
The pipeline demonstrated its capability to analyze antibody sequences for specific functional attributes.

Conclusions:

ASAP-SML provides a powerful computational approach to identify sequence features critical for antibody inhibitory activity.
This method can significantly advance the development of targeted antibody therapeutics, particularly for diseases involving MMPs.