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Tumor Discovery Missense mutations in CXCR1: Impact on stability and function

these studies to gain insights into disease development and assist in the exploration of potential therapeutic strategies.
progression. 23,75 Using I-TASSER, we modeled the native Moreover, such computational methods can assist in the
and mutant CXCR1 protein structures (Figure 3B and D) clinical assessment of genetic variants, 70,76 enabling more
for MDS analysis to explore the structural consequences informed and effective treatment decisions.
of the mutation on the protein structure. This approach
allowed us to investigate the effect of the mutation on the 5. Conclusion
overall structure of the protein. The RMSD data obtained In silico investigations of the TM domains of CXCR1
from the simulations indicated that the mutant CXCR1 (TM1, TM3, and TM7) revealed N57D, R135C, and P302S
protein displayed a distinct and noticeable pattern of point mutations as potential carcinogenic variants. In
deviation throughout the entire simulation duration addition, MDS analysis indicated that these mutations
compared to the behavior observed for the native protein lead to changes in the structural stability of the protein,
(Figure 3A and C). This finding suggested that the mutation including increased flexibility and reduced compactness.
significantly destabilized the CXCR1 protein, leading The structural instability of the mutant protein could
to structural alterations that are different from those lead to an inability to interact with the CXCL8 ligand and
observed in the native protein. The RMSF data provided other associated proteins, which may have implications
additional evidence to corroborate the hypothesis that for cancer development. The molecular understanding
the mutation disrupts the stability of the protein structure attained through this study could serve as a foundation
(Figure 4A and B). The data revealed that the level of for in vitro and in vivo experiments investigating the effect
variation at the residue level was significantly greater in the of N57D, R135C, and P302S mutations on the interaction
mutant protein than in the wild-type protein, indicating that of CXCR1 with its ligand and its association with disease
the mutation significantly altered the protein conformation development.
and increased its flexibility. The SSE analysis indicated that
the mutant protein exhibited a distinct alteration in the Acknowledgments
protein conformation, transitioning from an α-helix to
a coil form, unlike the native protein (Figure 5A and B). None.
This finding suggested that the mutation-induced changes Funding
in the protein’s secondary structure, leading to a shift in
its overall conformation. The observed conformational This work was supported by the National Natural Science
changes in our analysis offer compelling evidence that Foundation of China (32270438, 32170498, 31970388),
the substitution of amino acids in the mutant protein National Key Research and Development Program of
resulted in substantial alterations to its overall structure. China (2021YFF0702000, 2018YFD0900602), 1.3.5 Project
These alterations resulted in mutant proteins that are less for Disciplines of Excellence by the West China Hospital,
stable, more flexible, and less tightly packed than the native Sichuan University (ZYJC21050), Science and Technology
protein. Taken together, our results strongly indicated that Department of Sichuan Province (2022YFH0116), Priority
the mutation had a significant and profound influence on Academic Program Development of Jiangsu Higher
both the structure and stability of the protein. Education Institutions (PAPD), and the National Clinical
Research Center for Geriatrics, West China Hospital,
Previous studies have demonstrated the complementary
nature of in silico approaches and wet laboratory experiments Sichuan University (Z2023JC003).
in understanding biological phenomena. The integration Conflict of interest
of computational methods with experimental techniques
has proven to be a powerful tool for predicting and The authors declare no conflict of interest.
validating hypotheses in a range of biological systems. 70,76 Author contributions
The combination of computational mutation predictions
with MDS analysis has been instrumental in identifying Conceptualization: Shah Kamal, Gohar Mushtaq,
disease-causing mutations. This approach has enabled Muhammad Nasir Iqbal
77
the identification of the most deleterious mutations from Formal analysis: Shah Kamal, Amanullah Amanullah,
a large pool of mutations, providing valuable insights into Qingqing Wang, Najeeb Ullah, Muhammad Nasir
the molecular basis of various diseases. Computational Iqbal
78
methods play a vital role in establishing the groundwork Writing – original draft: Shah Kamal, Gohar Mushtaq,
for genetic research aimed at comprehending the molecular Mohammad Amjad Kamal
foundations of diseases. These methods facilitate the Writing – review & editing: Gohar Mushtaq, Mohammad
79
detection of mutations that contribute to diseases and Amjad Kamal, Najeeb Ullah

Volume 3 Issue 1 (2024) 20 https://doi.org/10.36922/td.2512