Understanding Periperal Membrane Protein Interactions | BPS Thematic Meeting

Understanding Peripheral Membrane Protein Interactions: Structure, Dynamics, Function and Therapy

Poster Abstracts

4-POS Board 4 USING DEEP LEARNING AND LARGE PROTEIN LANGUAGE MODELS TO PREDICT PROTEIN-MEMBRANE INTERFACES OF PERIPHERAL MEMBRANE PROTEINS Zoe Cournia 1 ; Alexios Chatzigoulas 1 ; Dimitra Paranou 1 ; 1 Biomedical Research Foundation, Academy of Athens, Athens, Greece Characterizing interactions at the protein-membrane interface is crucial as abnormal peripheral protein-membrane attachment is involved in the onset of many diseases [1]. However, a limiting factor in studying and understanding protein-membrane interactions is that the membrane binding domains of peripheral membrane proteins are typically unknown. By applying Artificial Intelligence techniques in the context of Natural Language Processing (NLP), the accuracy and prediction time for protein-membrane interface analysis can be significantly improved compared to existing methods. Here, we assess whether NLP and protein language models (pLMs) can be used to predict membrane-interacting amino acids for peripheral membrane proteins. We utilize available experimental data and generate protein embeddings from two pLMs (ProtTrans [2] and ESM [3]) to train classifier models. Overall, the results demonstrate the first proof of concept study and the promising potential of using deep learning and pLMs to predict protein-membrane interactions for pe-ripheral membrane proteins faster, with similar accuracy and without the need for three-dimensional structural data, compared to existing tools [4].References[1] A. Chatzigoulas and Z. Cournia, “Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning”. Brief. Bioinform., bbab518, 2022.[2] A. Elnaggar et al., “ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning,” TPAMI / PAMI, vol. 14, no. 8, 2021.[3] R. Rao, J. Meier, T. Sercu, S. Ovchinnikov, and A. Rives, “Transformer protein language models are unsupervised structure learners,” bioRxiv. 2020.[4] D. Paranou, A. Chatzigoulas, Z. Cournia, “Using deep learning and large protein language models to predict protein-membrane interfaces of peripheral membrane proteins,” Bioinform. Adv., 2024.

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