SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters

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.