Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases

Marios G Krokidis; Georgios N Dimitrakopoulos; Aristidis G Vrahatis; Themis P Exarchos; Panagiotis Vlamos

doi:10.3389/fncom.2023.1323182

Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases

Front Comput Neurosci. 2024 Jan 5:17:1323182. doi: 10.3389/fncom.2023.1323182. eCollection 2023.

Authors

Marios G Krokidis¹, Georgios N Dimitrakopoulos¹, Aristidis G Vrahatis¹, Themis P Exarchos¹, Panagiotis Vlamos¹

Affiliation

¹ Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.

No abstract available

Keywords: deep learning algorithms; fold recognition; neurodegeneration; protein misfolding; structure prediction.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Regional Excellence (Research Activity in the Ionian University, for the study of protein folding in neurodegenerative diseases) (FOLDIT) MIS 5047144.