Tripathi Asmita, Mondal Rajkrishna, Mandal Malay, Lahiri Tapobrata, Pal Manoj Kumar
Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Prayagraj 211015, India.
Department of Biotechnology, Nagaland University, Kohima 797004, India.
Biomolecules. 2024 Dec 12;14(12):1588. doi: 10.3390/biom14121588.
Pathological significance of interaction of Synphilin-1 with mutated alpha-synuclein is well known to have serious consequences in causing the formation of inclusion bodies that are linked to Parkinson's disease (PD). Information extracted so far pointed out that specific mutations, A53T, A30P, and E46K, in alpha-synuclein promote such interactions. However, a detailed structural study of this interaction is pending due to the unavailability of the complete structures of the large protein Synphilin-1 of chain length 919 residues and the mutated alpha-synuclein having all the reported specific mutations so far. In this study, a semi-automatic pipeline-based meta-predictor, AlphaLarge, is created to predict high-fidelity structures of large proteins like Synphilin-1 given the limitations of the existing protocols. AlphaLarge recruits a novel augmented AlphaFold model that uses a divide and conquer based strategy on the foundation of a self-sourced template dataset to choose the best structure model through their standard validations. The structure models were re-validated by a Protein Mediated Interaction Analysis (PMIA) formalism that uses the existing structurally relevant information of these proteins. For the training dataset, the new method, AlphaLarge, performed reasonably better than AlphaFold. Also, the new residue- and domain-based structural details of interactions of resultant best structure models of Synphilin-1 and both wild and mutated alpha-synuclein are extracted using PMIA. This result paves the way for better screening of target specific drugs to control the progression of PD, in particular, and research on any kind of pathophysiology involving large proteins of unknown structures, in general.
已知Synphilin-1与突变的α-突触核蛋白相互作用的病理意义在导致与帕金森病（PD）相关的包涵体形成方面具有严重后果。迄今为止提取的信息指出，α-突触核蛋白中的特定突变A53T、A30P和E46K促进了这种相互作用。然而，由于链长为919个残基的大蛋白Synphilin-1以及具有迄今为止所有报道的特定突变的突变α-突触核蛋白的完整结构不可用，因此对这种相互作用的详细结构研究尚待进行。在本研究中，鉴于现有方案的局限性，创建了一种基于半自动流程的元预测器AlphaLarge，以预测像Synphilin-1这样的大蛋白的高保真结构。AlphaLarge引入了一种新颖的增强型AlphaFold模型，该模型在自源模板数据集的基础上采用分而治之的策略，通过标准验证来选择最佳结构模型。结构模型通过蛋白质介导的相互作用分析（PMIA）形式主义进行重新验证，该形式主义使用这些蛋白质的现有结构相关信息。对于训练数据集，新方法AlphaLarge的表现比AlphaFold合理地更好。此外，使用PMIA提取了Synphilin-1与野生型和突变型α-突触核蛋白的最终最佳结构模型相互作用的基于新残基和结构域的结构细节。这一结果为更好地筛选靶向特定药物以控制PD的进展，特别是为一般涉及未知结构大蛋白的任何病理生理学研究铺平了道路。
