top of page


Nuclear Localization Signal (NLS) Prediction - NLSeer

Scott Auerbach, Ukamaka Nnyaba, Ming Zhang, Yingyi Guo, Hemaa Selvakumar, Cisil Karaguzel


The purpose of the project is to build a prediction tool that estimates the possibility of having nuclear localization signals inside a protein's sequence based on the significance of each amino acid. Nuclear localization signals (NLS) are segments of a protein sequence that direct it towards the nucleus and have been implicated in human diseases and play an important role in many biological pathways. We employed datasets including whole protein sequences with and without nuclear localization signals and trained both classifiers and neural networks to predict whether or not a protein contained a NLS. Using a random forest classifier, we developed a web app through Flask that can predict whether or not a given protein is likely to have a NLS, and if so, also estimate the likelihood of each amino acid contributing to a NLS.

Screen Shot 2022-06-03 at 11.31.35 AM.png
github URL
bottom of page