Bioinformatics and Neuroinformatics MSc Program, Hellenic Open University, 26335 Patras, Greece.
Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Int J Mol Sci. 2023 Aug 31;24(17):13543. doi: 10.3390/ijms241713543.
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.
使用计算方法准确预测蛋白质结构仍然是分子生物学中的一个挑战。最近在人工智能算法方面的进展在解决这个问题方面产生了变革性的影响。尽管 AlphaFold 自发布以来性能有所提高，但它的功效仍然存在局限性。在这项研究中，选择了与阿尔茨海默病病理学相关的一组蛋白质进行建模，重点是早老素-1（PSN1）及其突变变体。使用 AlphaFold 的 ColabFold 实现评估了它们的结构预测，该实现利用 MMseqs2 来创建多重序列比对（MSAs）。选择了比 AlphaFold DB 中使用的更多的循环次数，并且没有使用模板。此外，还应用了 RoseTTAFold 的预测来解决这两个深度学习框架的结构如何与现实匹配的问题。将得到的构象与相应的实验结构进行比较，为这种方法在这组特定蛋白质中的预测能力提供了潜在的见解。此外，还对无序区域的预测、突变对 PSN1 的潜在影响等特征进行了全面检查。我们的研究结果包括与实验确定的域级模型高度精确的叠加，几乎没有或没有偏差。
