SARS-CoV-2 Spike glycoprotein (full-length) prediction via E8 lattice topological optimization

Protein target This record contains the predicted 3D structure of the full-length SARS-CoV-2 spike (S) glycoprotein (UniProt P0DTC2, 1273 amino acids, Wuhan-Hu-1 isolate), generated using the E8 Navigator — a symmetry-based, non-data-driven protein folding method.MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT Biological relevance The spike protein is the primary surface antigen of SARS-CoV-2 and the main target for COVID-19 vaccines, therapeutic antibodies, and neutralizing immunity. It mediates host cell entry via ACE2 receptor binding, undergoes dramatic conformational changes during membrane fusion, and is heavily glycosylated. Accurate structural modeling of spike is essential for understanding receptor interaction, immune escape, variant effects, and vaccine/antibody design. Why this is a challenging folding problem The spike protein is structurally complex and difficult for conventional prediction methods: Large size (~1273 residues) with multiple domains (N-terminal domain, receptor-binding domain, S2 fusion subunit) Significant conformational plasticity (pre-fusion closed/open, post-fusion states) Extensive glycosylation that shields much of the surface and affects loop dynamics Long flexible linkers and antigenic loops often lead to low-confidence regions or conformational bias in MSA-dependent predictors AlphaFold models are generally accurate on structured domains but can assign lower confidence to flexible loops, glycosylation sites, and full-length dynamics These features make spike an ideal test case for alternative folding approaches. Method: E8 Navigator The structure was generated using the E8 Navigator, a topology-driven folding engine that maps the amino acid sequence onto the exceptional Lie group E8 lattice based on physicochemical properties. Folding is performed as symmetry-constrained optimization on the E8 manifold, guided by a holographic coherence metric (Ψ) and convergence to ultra-low error states — without multiple sequence alignments, neural networks, or patterns memorized from the PDB. Results The output PDB (P0DTC2_E8_prediction.pdb) converged with Ψ = 2.00 and very low final error (~0.0003 Å), producing a compact, multi-domain core with extended flexible regions and no major steric clashes. The model is provided for open inspection and comparison. Significance The SARS-CoV-2 spike is a canonical viral fusion glycoprotein whose structure and dynamics are central to pandemic response, vaccine efficacy, and therapeutic development. A coherent, clash-free prediction from a purely symmetry-based method — particularly in flexible, glycosylated, and conformationally dynamic regions — offers an orthogonal perspective to current AI-driven predictors. This work demonstrates the potential of lattice-based approaches for modeling complex viral surface proteins. The prediction is shared openly for visualization, alignment against experimental spike structures (e.g. PDB 6VXX, 6VSB, 7A94), community evaluation, and comparison with other folding methods. Files included P0DTC2_E8_prediction.pdb — full predicted structure蛋白质靶点 本记录包含全长严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）刺突（S）糖蛋白的预测三维结构，该蛋白的通用蛋白知识库（UniProt）编号为P0DTC2，包含1273个氨基酸，取自Wuhan-Hu-1毒株，通过E8 Navigator生成——这是一种基于对称性、无需数据驱动的蛋白质折叠方法。 MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT 生物学相关性 刺突蛋白是SARS-CoV-2的主要表面抗原，也是新型冠状病毒肺炎（COVID-19）疫苗、治疗性抗体以及中和免疫的核心靶点。它通过结合血管紧张素转换酶2（ACE2）受体介导宿主细胞入侵，在膜融合过程中会发生显著的构象变化，且存在大量糖基化修饰。对刺突蛋白进行精准的结构建模，对于理解受体相互作用、免疫逃逸、变异株影响以及疫苗/抗体设计至关重要。 为何这是一项具有挑战性的折叠任务 刺突蛋白结构复杂，传统预测方法难以有效处理： 1. 尺寸较大（约1273个残基），包含多个结构域：N端结构域、受体结合结构域以及S2融合亚基； 2. 具有显著的构象可塑性，涵盖融合前闭合/开放状态与融合后状态； 3. 广泛的糖基化修饰会遮蔽大部分蛋白表面，并影响环区动力学特性； 4. 较长的柔性连接肽与抗原性环区常会导致依赖多序列比对（MSA）的预测工具出现低置信度区域或构象偏差； 5. AlphaFold模型在结构化结构域上通常表现精准，但对柔性环区、糖基化位点以及全长蛋白动力学的置信度较低。 上述特性使得刺突蛋白成为替代折叠方法的理想测试案例。 方法：E8 Navigator 本结构通过E8 Navigator生成，这是一种拓扑驱动的折叠引擎，可基于理化特性将氨基酸序列映射到特殊李群E8晶格上。折叠过程通过在E8流形上进行对称性约束优化完成，以全息相干度量（Ψ）为导向，并收敛至超低误差状态——无需多序列比对、神经网络或从蛋白质数据库（PDB）中记忆的结构模式。 结果 输出的PDB文件（P0DTC2_E8_prediction.pdb）以Ψ=2.00收敛，最终误差极低（约0.0003 Å），生成了包含延伸柔性区域的紧凑多结构域核心，且无重大空间位阻冲突。本模型公开供用户查看与对比。 研究意义 SARS-CoV-2刺突蛋白是典型的病毒融合糖蛋白，其结构与动力学是疫情防控、疫苗效力评估以及治疗性药物开发的核心要点。通过纯对称性方法得到的连贯且无空间冲突的预测结果——尤其是在柔性、糖基化且构象动态的区域——为当前基于人工智能的预测工具提供了一种正交视角。本研究证明了基于晶格的方法在建模复杂病毒表面蛋白方面的潜力。 本预测结果公开共享，可用于可视化、与实验获得的刺突蛋白结构（例如PDB 6VXX、6VSB、7A94）进行比对、社区评估以及与其他折叠方法进行对比。 包含文件 P0DTC2_E8_prediction.pdb —— 完整的预测结构