Publication
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.
Abstract
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.