Understanding Periperal Membrane Protein Interactions | BPS Thematic Meeting

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

Poster Abstracts

2-POS Board 2 PREDICTING PROTEIN-MEMBRANE INTERFACES OF PERIPHERAL MEMBRANE PROTEINS USING ENSEMBLE MACHINE LEARNING Zoe Cournia 1 ; Alexios Chatzigoulas 1 ; 1 Biomedical Research Foundation, Academy of Athens, Athens, Greece Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable [1]. A major obstacle in this drug design strategy is that the membrane binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data in the literature for training 21 machine learning classifiers and a voting classifier [2]. Evaluation of the ensemble classifier accuracy produced a macro-averaged F 1 score = 0.92 and an MCC = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. Predictions were further verified in independent test sets. The Python code is available at https://github.com/zoecournia/DREAMM.The allosteric modulation of peripheral membrane proteins by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy. We also present a drug design pipeline for drugging protein membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web-server [3]. DREAMM works in the back-end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of peripheral membrane proteins. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating residues in protein conformational ensembles provided by the user or generated by DREAMM. DREAMM has been made accessible via a web-server at https://dreamm.ni4os.eu. References [1] Cournia Z, Chatzigoulas A (2020) Allostery in Membrane Proteins, Curr Op Struct Biol, 2020, 62, 197-204, doi: 10.1016/j.sbi.2020.03.006[2] Chatzigoulas A, Cournia Z (2022) Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning. Brief Bioinform, 2022, 23, bbab518. doi: 10.1093/bib/bbab518[3] Chatzigoulas A, Cournia Z (2022) DREAMM: A web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design, Bioinformatics, 2023, 38, 5449–5451, doi: 10.1093/bioinformatics/btac680

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