LAP

LAP identifies and prioritizes chemical liability motifs within antibody sequences to support development and optimization of antibody-based therapeutics.

Key Features:

• Comprehensive Data Analysis: Analyzes sequences from existing therapeutics, patents, GenBank entries, scientific literature, and next-generation sequencing outputs to assess liability motifs across diverse antibody datasets.
• Identification of Liability Motifs: Detects chemical liability motifs within antibody paratopes and reports that antibodies commonly contain an average of 3-4 such motifs.
• Computational Flags for Prioritization: Uses three computational flags—Germline Flag, Therapeutic Flag, and Surface Flag—to prioritize motifs based on occurrence in germline, presence in existing therapeutic antibodies, and structural surface accessibility.
• Reduction of Over-Prediction: Annotates approximately 60% of identified motifs as benign to reduce over-prediction and lower the predicted likelihood of degradation processes such as deamidation, isomerization, and oxidation.

Scientific Applications:

• De-risking Lead Biotherapeutics: Identifies and prioritizes liability motifs that could compromise drug stability or efficacy during early-stage development.
• Optimization of Antibody Design: Supports sequence- and structure-informed modifications by distinguishing naturally occurring and therapeutic-associated motifs to reduce chemical liabilities.
• Experimental Validation Support: Benchmarks predictions against experimental datasets for degradation processes to inform validation and engineering decisions.

Methodology:

Integrates sequence and structural mapping techniques and applies computational flags to distinguish high-risk from benign liability motifs.