DOI: 10.5281/zenodo.15817622
title: Synthetically Engineered SARS-CoV-2 Spike Mutant Designed for Biocompatibility and Drug Resistance

description: In this study, I generated a synthetic SARS-CoV-2 Spike protein based on the XBB.1.5 (XFG) lineage, introducing 19 biologically plausible mutations aimed at modeling immune escape, drug resistance, and structural fitness. These mutations were selected to preserve viral function while exploring evolutionary pathways that could impact receptor binding, antibody recognition, and vaccine efficacy.

Using computational tools such as SWISS-MODEL and PyMOL, I validated the structural plausibility of the mutant and confirmed its similarity to natural variants in conformational space. Subsequently, I performed a Codon Pair Bias Index (CPBI) analysis across over 3,000 natural SARS-CoV-2 genomes and compared them with synthetic construct that I created for this study.

Surprisingly, the synthetic spike showed no significant deviation from the natural population in terms of codon pair frequency or adaptation index. Its CPBI score fell within the expected distribution of natural isolates, suggesting that engineered sequences designed under natural codon constraints may not be distinguishable from evolved ones using current genomic signature-based methods .

This work highlights the limitations of existing synthetic signal detection approaches , particularly when synthetic constructs are optimized for host codon compatibility and structural fidelity.

Biological Plausibility Over Engineering Signatures

The synthetic Spike variant was constructed using mutation strategies that:



Mimic known natural variation

Maintain physicochemical properties

Avoid rare codons or unnatural codon pairs

Preserve structural integrity and folding energy


As a result, despite being computationally engineered, the mutant exhibits codon pair frequencies consistent with natural isolates , making it indistinguishable from naturally occurring strains using standard CPBI metrics.

Implications for Biosecurity Detection

This suggests a critical limitation in current genomic surveillance techniques :

A synthetic virus designed with care to match natural codon usage can evade detection by CPBI-based algorithms.

This has important implications for:



Biosecurity monitoring

Synthetic origin detection

Future design of pathogen engineering safeguards


Conclusion: Rejection of CPBI as Sole Indicator

I conclude that CPBI alone cannot reliably distinguish engineered from naturally evolving sequences when synthetic designs are guided by biological constraints and codon optimization principles.

While CPBI remains a powerful tool for identifying obviously engineered constructs , its utility diminishes when synthetic genes are designed to mimic natural variation .

This opens new questions around:



How to define "unnatural" mutation. 

The need for multi-layered detection frameworks


As the sole author and researcher of this study and its accompanying data, I declare that I have no competing interests. This work has been designed to be fully reproducible, and the dataset and related materials are available on Zenodo. For additional files, collaboration opportunities, or to further develop this research, I can be contacted via tahirhb.com or by email at tahirhb@hotmail.com

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description: Metric
Value


Number of Natural Isolates Analyzed
3,357


Number of Mutations Introduced
19


Mutation Strategy
Conservative + structurally tolerated


CPBI Score – Synthetic
7.8587


CPBI Score – Natural Mean ± Std
7.7490 ± 0.4997


Z-Score
0.2195


Outlier Status
Not an outlier
description: Ethical Statement

This study involves the computational design and structural modeling of a synthetic SARS-CoV-2 spike protein with biologically plausible mutations. The sequence described here is entirely in silico, meaning it exists only as a digital construct and has not been synthesized, expressed, or used to generate any infectious viral entity.

 

The purpose of this research is to explore the limitations of current biosurveillance tools in detecting engineered sequences that mimic natural evolutionary patterns. It is not intended for replication in live systems or for any application involving gain-of-function studies.

 

All analyses were conducted using publicly available datasets and open-source bioinformatics tools, including SWISS-MODEL, PyMOL, Nextclade, and CoVsurver. No laboratory-based molecular biology experiments were performed.

 

This work aligns with principles of responsible science and dual-use research of concern (DURC) awareness, emphasizing the importance of improving global pathogen detection frameworks rather than enabling misuse.

 

I recognize the potential sensitivity of this work and affirm that:

 

No live virus was generated

No transfection or expression experiments were conducted

No biological function was tested beyond computational modeling

Results are intended solely for improving detection of synthetic constructs in genomic surveillance

Tahir Bhatti

TahirHB.Com

tahirhb@hotmail.com