DOI: 10.5281/zenodo.18485599
title: EGFR (Epidermal Growth Factor Receptor) full-length prediction via E8 lattice topological optimization

description: Protein target

This record contains the predicted 3D structure of full-length human EGFR (UniProt P00533, 1210 amino acids), generated using the E8 Navigator — a symmetry-based, non-data-driven protein folding method.

MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEV VLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALA VLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDF QNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGC TGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYV VTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFK NCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAF ENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKL FGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCN LLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVM GENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVV ALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGS GAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGI CLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAA RNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSY GVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPK FRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQ QGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTED SIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLN TVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRV APQSSEFIGA

Biological relevance

EGFR is one of the most important receptor tyrosine kinases in human cancer biology. It is frequently mutated or amplified in non-small cell lung cancer (NSCLC), particularly in adenocarcinoma, where activating mutations (e.g., exon 19 deletions, L858R) drive tumorigenesis. EGFR is the primary target of several approved tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, and osimertinib. The protein spans the plasma membrane with an extracellular ligand-binding region, a single transmembrane helix, and an intracellular kinase domain + C-terminal regulatory tail.

Why this is a challenging folding problem

EGFR is structurally difficult for conventional prediction methods:  



It is a transmembrane protein with a hydrophobic transmembrane helix (residues ~621–643) that must be correctly inserted and oriented — AlphaFold often fails to model realistic membrane topology  

The intracellular C-terminal tail (~950–1210) and juxtamembrane regions are intrinsically disordered and critical for signaling and drug resistance  

Long linkers between extracellular, transmembrane, and intracellular domains lead to unreliable inter-domain arrangements  

AlphaFold predictions (see AF-P00533-F1) assign high pLDDT to individual domains (especially the kinase) but very low confidence to disordered regions and the full-length assembly


 

These limitations make EGFR an excellent test case for alternative folding approaches.

Method: E8 Navigator

The structure was produced using the E8 Navigator, a topology-driven folding engine that maps the amino acid sequence onto the exceptional Lie group E8 lattice using 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 PDB-derived patterns.

Results

The output PDB (EGFR_E8_prediction.pdb) converged with Ψ ≈ 2.00 and very low final error, producing a structured extracellular region, transmembrane helix, kinase domain, and extended/disordered intracellular tail. The model shows no major steric clashes and is provided for direct comparison with the AlphaFold prediction.

Significance

EGFR is a canonical membrane receptor whose full-length architecture and dynamics are biologically and therapeutically critical. A coherent prediction from a purely symmetry-based method — particularly in the transmembrane and disordered regions — demonstrates an orthogonal capability to current AI predictors. This is especially relevant for lung cancer research, where understanding EGFR conformation affects drug binding and resistance mechanisms.

The prediction is shared openly for visualization, alignment against experimental kinase domain structures (e.g. PDB 1M17, 4HJO) or AlphaFold output, and community evaluation of lattice-based folding methods.

Files included



EGFR_E8_prediction.pdb — full predicted structure