Predicting Oligomeric states of Fluorescent Proteins using Mamba

Agney K Rajeev, Joel Joseph K B, Subhankar Mishra
Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), PMLR 265:204-212, 2025.

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).

Cite this Paper

BibTeX
@InProceedings{pmlr-v265-rajeev25a, title = {Predicting Oligomeric states of Fluorescent Proteins using Mamba}, author = {Rajeev, Agney K and B, Joel Joseph K and Mishra, Subhankar}, booktitle = {Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL)}, pages = {204--212}, year = {2025}, editor = {Lutchyn, Tetiana and Ramírez Rivera, Adín and Ricaud, Benjamin}, volume = {265}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v265/main/assets/rajeev25a/rajeev25a.pdf}, url = {https://proceedings.mlr.press/v265/rajeev25a.html}, 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).} }
Endnote
%0 Conference Paper %T Predicting Oligomeric states of Fluorescent Proteins using Mamba %A Agney K Rajeev %A Joel Joseph K B %A Subhankar Mishra %B Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL) %C Proceedings of Machine Learning Research %D 2025 %E Tetiana Lutchyn %E Adín Ramírez Rivera %E Benjamin Ricaud %F pmlr-v265-rajeev25a %I PMLR %P 204--212 %U https://proceedings.mlr.press/v265/rajeev25a.html %V 265 %X 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).
APA
Rajeev, A.K., B, J.J.K. & Mishra, S.. (2025). Predicting Oligomeric states of Fluorescent Proteins using Mamba. Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), in Proceedings of Machine Learning Research 265:204-212 Available from https://proceedings.mlr.press/v265/rajeev25a.html.

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