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Exploiting Protein Language Models for the Precise Classification of Ion Channels and Ion Transporters
  • Hamed Ghazikhani,
  • Gregory Butler
Hamed Ghazikhani
Concordia University Department of Computer Science and Software Engineering

Corresponding Author:[email protected]

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Gregory Butler
Concordia University Department of Computer Science and Software Engineering
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This study presents TooT-PLM-ionCT, a holistic framework that exploits the capabilities of six diverse Protein Language Models (PLMs) - ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters) - for precise classification of integral membrane proteins, specifically ion channels (ICs) and ion transporters (ITs). As these proteins play a pivotal role in the regulation of ion movement across cellular membranes, they are integral to numerous biological processes and overall cellular vitality. To circumvent the costly and time-consuming nature of wet lab experiments, we harness the predictive prowess of PLMs, drawing parallels with techniques in natural language processing. Our strategy engages six classifiers, embracing both conventional methodologies and a deep learning model, to segregate ICs and ITs from other membrane proteins, as well as differentiate ICs from ITs. Furthermore, we delve into critical factors influencing our tasks, including the implications of dataset balancing, the effect of frozen versus fine-tuned PLM representations, and the potential variance between half and full precision floating-point computations. Our empirical results showcase superior performance in distinguishing ITs from other membrane proteins and differentiating ICs from ITs, while the task of discriminating ICs from other membrane proteins exhibits results commensurate with the current state-of-the-art.
01 Sep 2023Submitted to PROTEINS: Structure, Function, and Bioinformatics
01 Sep 2023Review(s) Completed, Editorial Evaluation Pending
01 Sep 2023Submission Checks Completed
01 Sep 2023Assigned to Editor
27 Oct 2023Reviewer(s) Assigned
13 Nov 2023Editorial Decision: Revise Major
27 Mar 20241st Revision Received
02 Apr 2024Review(s) Completed, Editorial Evaluation Pending
08 Apr 2024Editorial Decision: Accept