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ChrSLoc-Net: Machine Learning-Based Prediction of Channelrhodopsins Proteins within Plasma Membrane

Muhammad Nabeel Asim; Muhammad Ali Ibrahim; Muhammad Imran Malik; Andreas Dengel; Sheraz Ahmed
In: 2021 BHI Conference Proceedings. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI-2021), July 27-30, Pages 1-4, ISBN 978-1-6654-0358-0, IEEE, 2021.


There is a rising interest in investigating mechanisms and engineering of integral membrane proteins (MPs) which make crucial contribution in perceiving and controlling cellular response against different external signals. MPs need to be inserted, folded and expressed correctly in lipid bi-layer and transferred to appropriate cellular location to perform its diverse range of functions. Channelrhodopsins (ChRs), light gated ionchannel proteins belonging to microorganisms are imminent for diverse neurobiology applications where expression as well as localization to plasma membrane is a pre-condition for function. Developing robust computational methodologies to accurately identify ChRs localization is an active area of research. Existing computational approaches make use of one-hot-vector encoding or protein embeddings to encode MP sequences that are fed to Gaussian process regression model. These approaches lack to accurately predict the localization of MP proteins. The paper in hand proposes ChRsLoc-Net predictor that makes use of composition-transition-distribution (CTDC) physico-chemical properties based sequence encoder along with Hubber regressor. Over benchmark dataset, proposed ChrSLoc-Net approach outperforms state-of-the-art MP localization predictor with a significant margin of 9% in terms of mean absolute error. We anticipate that this study will largely assist biologist to comprehend diverse biological processes subject to localization patterns of MPs within plasma membrane.