DOI: 10.5281/zenodo.15817674
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
description: Mutation Constellation Table:

     


SN
Mutation
Region/Domain
Functional Impact / Category
Notes


1
F2L
Signal peptide
Structural
Rare; unknown effect


2
S13F
Signal peptide
Immune evasion
Seen in Omicron sublineages


3
V16X
NTD
Structural
Ambiguous call


4
L18F
NTD supersite
Immune evasion
Reduces neutralizing antibody binding


5
T19I
NTD
Immune evasion
Omicron feature


6
R21T
NTD
Immune evasion
Rare


7
T22N
NTD
Immune evasion
Rare


8
L24del
NTD
Immune evasion
Omicron hallmark


9
P25del
NTD
Immune evasion
Omicron hallmark


10
P26del
NTD
Immune evasion
Omicron hallmark


11
A27del
NTD
Immune evasion
Omicron hallmark


12
S31P
NTD
Structural
Rare


13
T33I
NTD
Structural
Rare


14
S50L
NTD
Immune evasion
Sporadic


15
I68del
NTD
Immune evasion
Linked to antigenic shift


16
H69del
NTD
Immune evasion
Common in Omicron/Alpha


17
V70del
NTD
Immune evasion
Common in Omicron/Alpha


18
V90F
NTD
Structural
Rare


19
V127F
NTD
Structural
Rare


20
G142D
NTD supersite
Immune evasion
Omicron hallmark


21
Y144del
NTD supersite
Immune evasion
Omicron hallmark


22
F157S
NTD
Immune evasion
Omicron hallmark


23
R158G
NTD
Immune evasion
Omicron hallmark


24
K182R
NTD
Structural
Rare


25
R190S
NTD
Immune evasion
Omicron feature


26
F194L
NTD
Structural
Rare


27
N211G
NTD
Immune evasion
Omicron hallmark


28
L212del
NTD
Immune evasion
Omicron hallmark


29
V213del
NTD
Immune evasion
Omicron hallmark


30
L216F
NTD
Structural
Rare


31
L226F
NTD
Structural
Rare


32
H245N
NTD
Structural
Rare


33
A264D
NTD
Structural
Rare


34
I332V
RBD flank
Structural
Rare


35
G339H
RBD core
Immune evasion
Omicron hallmark


36
R346T
RBD
Immune evasion
Antibody escape (Class 3)


37
K356T
RBD
Structural
Rare


38
N370S
RBD
Structural
Found in BA.2.86


39
S371F
RBD
Immune evasion
Omicron hallmark


40
S373P
RBD
Immune evasion
Omicron hallmark


41
S375F
RBD
Immune evasion
Omicron hallmark


42
T376A
RBD
Structural
BA.2.86


43
S383P
RBD
Structural
Rare


44
R403X
RBD / interface
Structural
Truncation or stop codon, may be synthetic error


45
D405N
RBD
Immune evasion
BA.2.86


46
R408S
RBD
Immune evasion
BA.2.86


47
K417N
RBD
Immune evasion
Class 1 antibody escape


48
N440K
RBD
Immune evasion
Class 3 antibody escape


49
K444R
RBD
Immune evasion
Class 3 antibody escape


50
V445R
RBD
Immune evasion
Rare


51
G446S
RBD
Immune evasion
Class 3 antibody escape


52
N450D
RBD
Immune evasion
Rare


53
L452W
RBD
Immune evasion
Known L452 mutations drive immune escape


54
L455S
RBD
Structural
Rare


55
F456L
RBD
Structural
Rare


56
N460K
RBD
Immune evasion
BA.2.86


57
S477N
RBD
Receptor binding
Increased ACE2 affinity


58
T478K
RBD
Immune evasion
Omicron hallmark


59
N481K
RBD
Structural
Rare


60
V483del
RBD
Immune evasion
Rare


61
E484K
RBD
Immune evasion
Class 2 escape


62
F486P
RBD
Receptor binding
Synthetic; may drastically alter RBD conformation


63
N487D
RBD
Receptor binding
Rare; near ACE2 interface


64
Q493E
RBD
Receptor binding
Synthetic


65
F497P
RBD
Receptor binding
Synthetic


66
Q498R
RBD
Receptor binding
Increased ACE2 binding in Omicron


67
N501Y
RBD
Receptor binding
Enhanced ACE2 affinity


68
Y505H
RBD
Immune evasion
Rare


69
E554K
S2
Structural
Rare


70
A570V
S1/S2
Structural
Alpha variant feature


71
T572I
S1/S2
Structural
Rare


72
D614G
S1/S2
Fitness advantage
Universal in VOCs


73
P621S
S1/S2
Structural
Rare


74
H655Y
S1/S2
Structural
Omicron hallmark


75
N679K
Furin site
Fusion efficiency
Omicron hallmark


76
S680F
Furin site
Fusion efficiency
Rare


77
P681R
Furin site
Fusion efficiency
Delta hallmark


78
N764K
S2
Structural
Omicron hallmark


79
D796Y
S2
Immune evasion
Linked to resistance to fusion inhibitors


80
A903V
S2
Structural
Rare


81
S939F
S2
Structural
Rare


82
T941S
S2
Structural
Rare


83
Q954H
S2
Structural
Omicron hallmark


84
Q965H
S2
Structural
Rare


85
N969K
S2
Structural
Omicron hallmark


86
V1065L
S2
Structural
Rare


87
V1068I
S2
Structural
Rare


88
T1077I
S2
Structural
Rare


89
P1143L
S2
Structural
Rare


90
Y1155H
S2
Structural
Rare


91
L1197I
S2
Structural
Rare


92
T1231S
S2
Structural
Rare
description: Notable None-Spike Mutations:

     


SN
Gene
Mutation(s)
Functional Category
Notes


1
NSP1
S135R
Host shutoff modulation
Rare


2
NSP2
A31D
Unknown
Rare


3
NSP3
T24I, V238L, G489S, K1155R, T1275I, N1708S, S1717L, A1892T
Replication complex
Mixed


4
NSP4
L264F, T327I, T492I
Membrane rearrangement
Possible replication influence


5
NSP5
P132H
Protease
Common in VOCs


6
NSP6
V24F, S106del, G107del, F108del, R252K
Autophagy modulation
Omicron hallmark


7
NSP9
T35I
Unknown
Rare


8
NSP12 (RdRp)
P323L
Polymerase
Universally seen in VOCs


9
NSP13
R392C
Helicase
Rare


10
NSP14
I42V
Exonuclease
Rare


11
NSP15
T112I
Endoribonuclease
Rare


12
NS3
T223I
Ion channel
Rare


13
E
T9I
Envelope structure
Seen in Omicron


14
M
D3H, Q19E, T30A, A63T, A104V
Assembly
Rare ensemble


15
NS7b
F19L
Accessory
Rare


16
N
P13L, E31del, R32del, S33del, R203K, G204R, Q229K, T379I, S413R
Immune modulation, packaging
R203K/G204R widespread in VOCs