DOI: 10.5281/zenodo.18488844
title: SARS-CoV-2 Spike glycoprotein (full-length) prediction via E8 lattice topological optimization

description: 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