Applying Machine Learning Principles to Translation Efficiency

Overview

Translation is the process of translating mRNA transcripts into proteins and serves as the rate-limiting factor for protein synthesis. Translation efficiency (TE) measures how fast proteins are translated off an mRNA transcript, having an outsize importance on the rate at which proteins are produced. However, the overall regulatory mechanisms behind translation efficiency are poorly understood, leading to inefficiencies in protein production.

The Challenge

Regulation of mRNA translation remains a significant challenge in molecular biology. There is no singular set of mechanisms that can account for all factors influencing translation efficiency. The vast amounts of quantitative data generated by translation processes are time-consuming and sometimes inaccurate to analyze using current approaches, creating barriers to understanding protein production dynamics.

Traditional translation efficiency models focus on isolated features, miss sequence context and codon interactions, are often hard to interpret biologically, and tend to be species-specific based on outdated datasets.

Our Solution

We developed a machine learning approach using an Elastic Net CV model that can accurately predict changes in translation efficiency and uncover the mechanisms behind translation efficiency regulation. Our model is designed to be easy to understand and run on a wide range of computational systems.

Key Features of Our Approach

• White-box model that allows for interpretation of the relationships

• Handles correlated features through combining LASSO and Ridge regression

• Multiple predictive features including:

  • mRNA Secondary Structure Free Energy

  • Codon Bias (CAI, TAI)

  • Gene Length

  • GC Content

  • Ribosome Binding Site (RBS) analysis

  • tRNA abundance

Project Goals

1. Accurate prediction of how translation efficiency changes with respect to certain features

2. Understanding mechanisms of translation efficiency regulation

3. Making the model transparent and interpretable for biological insights

4. Promoting accessibility by creating models efficient enough to run on lower-spec computers

Applications and Impact

Our work enables more efficient artificial protein production in biotechnology and has wide-ranging applications:

• Protein Production Optimization for biotech and pharmaceutical industries

• Accelerating drug manufacturing and development

• Enhancing mRNA vaccine design and efficiency

• Improving crop genetic engineering

• Helping researchers explain why certain sequences are better expressed

With the biologics market projected to reach USD 699.5 billion by 2032, our machine learning approach addresses the increased demand for biologics while reducing production costs and improving drug accessibility.