RT Journal Article
SR Electronic
T1 Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.28.450157
DO 10.1101/2021.06.28.450157
A1 Chatzigoulas, Alexios
A1 Cournia, Zoe
YR 2021
UL http://biorxiv.org/content/early/2021/10/23/2021.06.28.450157.abstract
AB 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. A major obstacle in this drug design strategy is that the membrane binding domains of peripheral membrane proteins are usually not known. 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. Evaluation of the ensemble classifier accuracy produced a macro-averaged F1 score = 0.92 and an MCC = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of an independent test set. The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.Competing Interest StatementThe authors have declared no competing interest.