In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer’s Disease

Abstract

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold’s performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer’s disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

Keywords

• protein folding
• functional analysis
• Alzheimer’s disease
• presenilin 1
• fold recognition