2nd International Conference on Chemo and Bioinformatics ICCBIKG 2023 (338-341)
АУТОР(И) / AUTHOR(S): Marios Krokidis, Aristidis Vrahatis, George Dimitrakopoulos, Konstantina Skolariki, Panagiotis Vlamos, Themis Exarchos
Е-АДРЕСА / E-MAIL: mkrokidis@ionio.gr
DOI: 10.46793/ICCBI23.338K
САЖЕТАК / ABSTRACT:
Proteins assume a suitable spatial structure to effectively execute their intracellular functions. The occurrence of protein misfolding, resulting from single point mutations or external influences, along with the consequent buildup of protein aggregates, can lead to diverse pathological processes like neurodegenerative disorders. Protein misfolding serves as a risk indicator for Alzheimer’s disease (AD), the prevailing cause of neurodegenerative dementia in the elderly, characterized by gradual cognitive impairment. Several structure prediction algorithms and computational approaches have been developed to address this challenge. The present work focuses on specific proteins related to AD and aims to verify their conformation through ColabFold which utilizes the MMseqs2 algorithm to quickly provide multiple sequence alignments. The predicted models were then compared to selected PDB structures, a superposition was created and the TM-score and RMSD metrics were assessed. In addition, a comprehensive look into the superposed structures was performed to observe any potent deviations between pairs of residues. These notable findings encompass precise direct sequence-to-structure patterns found in individual AD polypeptides motifs and well-folded domains.
КЉУЧНЕ РЕЧИ / KEYWORDS:
protein folding, Alzheimer’s disease, functional analysis, multiple sequence alignments, structure prediction
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