De Novo Engineering of pH-Conditional, Cross-Species EGFR Binders via Higher-Dimensional ToposLang and Pharmacological Lattice Quantisation
Abstract
Overview This record contains the manuscript, computational framework artifacts, candidate sequence datasets, and wet-lab experimental protocols for "De Novo Engineering of pH-Conditional, Cross-Species EGFR Binders via Higher-Dimensional ToposLang and Pharmacological Lattice Quantisation". Targeted therapeutics against Epidermal Growth Factor Receptor (EGFR) are frequently constrained by severe on-target off-tumor toxicities and preclinical translation failures caused by cross-species sequence divergence. This work introduces a coordinate-free, topologically verified de novo protein design framework bridging Pharmacological Lattice Quantisation (PLQ) and ToposLang (7-Lang). The deposit includes 20 Pareto-optimal single-chain constructs (84-117 amino acids) presenting a histidine-switch electrostatic paratope directed against the conserved Domain III Cetuximab epitope. Key Features and Results pH-Conditional Switch Dynamic Range: Candidates exhibit potent binding in the acidic tumor microenvironment ($\Delta G_{\text{bind}} \le -14.81\text{ kcal/mol}$ at $pH = 6.5$) while remaining biochemically silent at physiological serum pH ($\Delta G_{\text{bind}} \ge +3.38\text{ kcal/mol}$ at $pH = 7.4$), establishing a thermodynamic switch delta of $\Delta\Delta G \ge +18.19\text{ kcal/mol}$. Cross-Species Equivalence: Binding thermodynamics are balanced between Human (UniProt: P00533) and Mouse (UniProt: Q01279) EGFR ($\vert{}\Delta\Delta G_{\text{species}}\vert{} \le 0.16\text{ kcal/mol}$). Folding Confidence and Sequence Novelty: The candidates exhibit high structural confidence ($pLDDT \ge 88.0$, $PAE < 4.0\text{ \AA}$) alongside low sequence identity ($\le 30.8\%$ to known reference binders), achieving $3/4$ and $4/4$ novelty scores by purging natural homology loops. Deterministic Assembly: Sequence generation is governed by exact rational Combinatorial Hodge Laplacians ($L_1 = B_1^T B_1 + B_2 B_2^T$), terminating deterministically upon Betti number annihilation ($\beta_1 = \dim(\ker L_1) = 0$). Theoretical Framework and Methodological Notes Discrete Topological Representation: Molecular interaction manifolds are formalized as Directed Acyclic Cell Complexes (DACCs) where boundary nilpotency ($\partial_{k-1} \circ \partial_k \equiv 0$) is statically enforced at compile time. Clifford Conformal Geometric Algebra ($\mathbb{R}^{4,1,0}$): Rigid docking transformations use coordinate-free multivector versor sandwich operators ($A' = M A \bar{M}$) instead of Cartesian rotation matrices. Compiler Optimization (-topos-symbolic-gnc): An MLIR dialect lowering pass simplifies blade expressions at compile time and packs non-zero multivector coefficients into 64-byte/128-byte SIMD cache lines for lock-free concurrency. Histidine Bifold Manifold: Intrinsic acid dissociation constants ($pK_a \approx 6.0\text{-}6.2$) are mapped into a discrete ternary state space $T \in \{-1, 0, +1\}$ to drive conditional salt-bridge formation at $pH = 6.5$ and severe desolvation penalties at $pH = 7.4$. File Manifest paper.pdf: Full preprint manuscript detailing the mathematical foundations, MLIR optimization passes, candidate profiling, and wet-lab protocols. submission.csv: Complete metadata and sequence entries for all 20 Pareto-optimal candidate constructs (ToposEGFR-C1-004 through ToposEGFR-C1-099), including sequence lengths, binding energies, $pLDDT$ scores, novelty tiers, and Hodge loop closure status. SOP_experimental_validation.md: Complete Standard Operating Procedure (SOP) for recombinant expression in E. coli SHuffle T7 Express, pET-22b(+) vector cloning, Ni-NTA/SEC purification, Surface Plasmon Resonance (SPR) kinetic assays, and flow cytometry on A431 cells. src/: Core source repositories for the ToposLang language engine and PLQ quantization framework. Technical and Execution Requirements Compiler Environment: ToposLang compiler suite (topos-lang) with MLIR/LLVM toolchain integration. Framework Dependencies: Pharmacological Lattice Quantization (PLQ) execution environment. Hardware Optimization: AVX-512 compatible x86_64 CPU recommended for SIMD multivector evaluation pipelines.
