Predicting Oligomeric states of Fluorescent Proteins using Mamba

Agney K Rajeev, Joel Joseph K B, Subhankar Mishra

Keywords: Mamba, Fluorescent proteins, Oligomerization state

TL;DR: Use of deep learning model Mamba to predict the Oligomeric state of a protein from its amino acid sequence

Abstract: Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and prevent interference with cellular functions. Experimental methods to find the oligomeric state can be time-consuming and expensive. Most of the current computational model is CPU-based, limiting their speed and scalability. This paper studies the effectiveness of GPU-based deep-learning models in predicting the oligomeric states of fluorescent proteins directly from their amino acid sequences, specifically focusing on the Mamba architecture. Various protein-specific augmentations were also employed to enhance the model's generalizability. Our results indicate that the mamba-based model achieves accuracy and F1 score close to 90\% and an MCC value of 0.8 with in predicting the oligomeric states of fluorescent proteins directly from its amino acid sequence. The code used in this study is available at [GitHub repository](https://github.com/smlab-niser/FluorMamba).

Git: https://github.com/smlab-niser/FluorMamba

Submission Number: 50