TIR Predictor and Optimization

TIR Predictor allows user to predict Translation Initation Rate in Saccharomyces cerevisiae using mRNA features using Machine Learning methods.

Introduction:

Translation initiation, which is the rate-limiting step in protein synthesis, can vary significantly and have a profound impact on cellular protein levels. Multiple molecular factors, such as mRNA structure stability, coding sequence length, and specific motifs in mRNA, influence the translation initiation rate, allowing precise control of protein synthesis. Despite the crucial role of translation initiation rate, accurately predicting its absolute values based on mRNA sequence features remains challenging. To address this issue, we developed a machine learning model specifically trained to predict the in vivo initiation rate in S. cerevisiae transcripts.

Further using this app the user can optimize the gene and achieve their desired target initiation rate using 2 methods:

1. Optimization with UTR
2. Optimization with UTR and codon

This has been developed on python 3.9

Credits:

• Built in Python using Streamlit by Sulagno Chakraborty, Inayat Ullah Irshad, Mahima and Ajeet K. Sharma
• The link to the software is as follows: [Read the Paper].