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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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Related Experiment Video

Updated: Jul 14, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Protein Language Model-Based Fitness Estimates Facilitate Resistance Mutation Identification.

Dominik Schwarz¹, Sven H Giese¹, Akansha Gupta²

¹Bayer AG, Machine Learning Research, Research & Development, Pharmaceuticals, 13353 Berlin, Germany.

Journal of Chemical Information and Modeling

|July 13, 2026

Summary

Predicting cancer drug resistance mutations is crucial. Protein fitness estimates, combined with physics-based methods, can efficiently identify mutations that confer resistance, improving cancer therapy strategies.

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Last Updated: Jul 14, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

Area of Science:

Computational biology
Drug discovery
Genomics

Background:

Drug resistance in cancer is a significant clinical challenge, leading to treatment failure and recurrence.
Identifying resistance mutations early can guide treatment decisions and accelerate the development of new therapies.
On-target mutations conferring drug resistance are key to understanding treatment failure.

Purpose of the Study:

To evaluate the combination of physics-based free energy perturbation (FEP) and protein language model-based fitness estimates for improved in silico identification of cancer drug resistance mutations.
To assess the utility of these computational methods in predicting mutations that confer resistance to targeted cancer therapies.

Main Methods:

Utilized free energy perturbation (FEP) for affinity estimates and protein language models for protein fitness predictions.
Validated computational predictions against deep mutational scanning (DMS) experimental data for resistance mutations.
Tested the approach on public ERK2 inhibitor resistance data and internal EGFR exon 20 mutant data.

Main Results:

Protein fitness estimates effectively filtered out mutations that were not experimentally validated as resistant.
FEP flagged some mutations as potentially resistant due to decreased affinity, but these were correctly excluded by fitness estimates.
The combined approach demonstrated improved accuracy in identifying true resistance mutations.

Conclusions:

Protein language model-based fitness estimates offer a computationally efficient method for filtering potential resistance mutations.
This approach can help avoid costly and error-prone modeling of protein function and stability.
Accurate in silico prediction of drug resistance mutations can significantly impact clinical trial design and patient treatment.