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Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Updated: Sep 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.

Jonathan Parkinson^1,2, Ryan Hard¹, Young Su Ko¹

¹Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093-0359, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|September 5, 2025

Summary

We developed RESP2, a new computational pipeline for discovering therapeutic antibodies. It efficiently finds antibodies that bind strongly to multiple target variants, outperforming current AI methods.

Keywords:

AI drug discovery antibody discovery bioinformatics drug resistance uncertainty‐aware machine learning

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Area of Science:

Computational biology
Immunology
Protein engineering

Background:

Therapeutic antibody discovery for infectious diseases faces challenges due to pathogen mutation.
Antibodies require high affinity for multiple antigen variants and optimal developability properties.

Purpose of the Study:

To introduce RESP2, an enhanced computational pipeline for discovering therapeutic antibodies.
To enable simultaneous optimization of antibody binding affinity and developability against evolving targets.

Main Methods:

In silico evaluation of the RESP2 pipeline using the Absolut! antibody-antigen docking database.
Case study using the COVID-19 spike protein receptor binding domain (RBD) and its variants.

Main Results:

RESP2 identified antibody sequences with significantly higher binding affinity to target antigen groups compared to training data (≥ 85% success rate).
RESP2 outperformed popular generative AI techniques, achieving success rates ≤ 1.5%.
A novel human antibody was discovered with mid-to-high affinity binding to at least eight COVID-19 RBD variants.

Conclusions:

The RESP2 pipeline offers a powerful advantage for discovering antibodies against rapidly evolving infectious disease targets.
RESP2 facilitates the development of broadly protective therapeutic antibodies.
A publicly available Python package for RESP2 is provided for broader research use.