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Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
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Updated: Jul 24, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters.

Andreas Evers¹, Shipra Malhotra², Wolf-Guido Bolick³

¹Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany. Andreas.Evers@merckgroup.com.

Methods in Molecular Biology (Clifton, N.J.)

|July 5, 2023

Summary

This summary is machine-generated.

This study introduces an in silico method to assess antibody and VHH sequences for developability risks beyond binding affinity. The approach aids in selecting optimal candidates for therapeutic development by predicting properties and identifying optimization starting points.

Keywords:

Antibody Developability Humanization Immunogenicity In silico sequence assessment Multi-parameter optimization Protein engineering VHH (variable domain of the heavy chain of heavy chain-only antibodies)

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Area of Science:

Biotechnology
Immunology
Computational Biology

Background:

Selecting promising antibody and VHH (variable domain of the heavy chain) sequences from display campaigns requires evaluating properties beyond binding signals.
Developability risk, sequence diversity, and optimization complexity are crucial for effective hit selection and subsequent therapeutic development.

Purpose of the Study:

To describe an in silico approach for assessing the developability of antibody and VHH sequences.
To enable ranking and filtering of sequences based on predicted developability properties and diversity.
To provide rationales and starting points for multi-parameter sequence optimization by visualizing problematic regions.

Main Methods:

Development of an in silico pipeline for developability assessment of antibody and VHH sequences.
Utilizing computational methods to predict developability properties and assess sequence diversity.
Implementing visualization tools to highlight sequence and structural features associated with developability risks.

Main Results:

The in silico method allows for effective ranking and filtering of antibody and VHH sequences based on predicted developability.
The approach identifies and visualizes potentially problematic sequence and structural features, aiding in risk assessment.
The system provides rationales for sequence selection and offers starting points for optimization strategies.

Conclusions:

In silico developability assessment is a valuable tool for selecting promising antibody and VHH candidates from screening campaigns.
This approach enhances the efficiency of therapeutic antibody development by integrating developability criteria early in the selection process.
The method facilitates rational, multi-parameter optimization of antibody and VHH sequences for improved developability profiles.