Page 69 - TD-3-1

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

a reference, and time is considered as t = 0); and r is the mutations in CXCR1 could cause harm and disease, and
position of selected atoms in frame x after superposition these mutations were located across multiple domains of
with the reference frame (frame x being recorded at time the protein (Table 1).
tx). The procedure was repeated for each frame in the
simulation trajectory. 59 3.3. Prediction of the impact of mutation on CXCR1
protein stability
RMSF is useful for characterizing local changes in the
54
protein chain. The RMSF for residue i is: Three algorithms, mCSM, SDM, and DUET, were used
to predict the 299 mutations and their impact on protein
T stability. mCSM predicted that 19 mutations would
2
RMSF = 1/T ∑ < ( `(t)r i ) ri− (tref )) > (II)
i strongly destabilize protein stability, SDM predicted that
t = 1
183 mutations would reduce CXCR1 protein stability, and
Where T is the flight path time over which the RMSF DUET predicted that 240 amino acids would affect the
is calculated; tref is the reference time; ri is the position of stability of the CXCR1 protein (Table 2).
residue I; r is the position of the atoms in residue I after an
overlay with the reference; and the angle brackets indicate 3.4. Identification of potential carcinogenic
that the mean squared distance is taken over the selection mutations in CXCR1
of the atoms in residue I. 54 In CXCR1, 299 mutations were identified using the
2.8. Analysis of secondary structure changes in FATHMM server to determine their potential role
CXCR1 proteins in carcinogenesis. The analysis revealed five specific
carcinogenic mutations (N57D, R135C, R135H, R135L,
The Database of Secondary Structure in Proteins tool and P302S) (Table 3), and these mutations were located
utilizes hydrogen bonding and other secondary structure across different regions of CXCR1.
markers to categorize protein residues and examine
alterations in secondary structure patterns between 3.5. Conservation analysis
60
wild-type and mutant CXCR1 proteins. The resulting From the five identified carcinogenic mutations, three
secondary structure patterns were plotted for comparison. mutations, N57D, R135C, and P302S, were selected for
3. Results further analysis. This analysis utilized the ConSurf server
tool to examine the conservation and location of these three
This study demonstrated that missense mutations in the mutations. The results indicated high conservation levels
61
CXCR1 gene significantly affected the molecular structure, at these locations, with a score of 9 on the scale presented
stability, and function of the resulting protein. This finding in Figure 1A-C. Based on these findings, we subsequently
suggested that the altered protein could be dysfunctional investigated the impact of these three mutations on protein
and contribute to the development or progression of stability and function through simulated computational
disease. The study utilized advanced computational analysis.
methods to screen for protein stability, cancer-causing
mutations, and pathogenicity. 3.6. Selection of the mutant CXCR1 structure
The control model for the native protein was the 3D
3.1. Retrieving the dataset of missense mutations structure of CXCR1 (PDB ID 2LNL). The I-TASSER server
62
To explore the potential effects of CXCR1 gene mutations was used to locate template proteins with comparable folds
on protein function, we acquired a comprehensive list from the PDB database and construct 10,000 conformations
of 299 mutations (Table S1) from a publicly available (decoys) from a mutant sequence. I-TASSER utilized the
63
database. These mutations are distributed throughout SPICKER tool to cluster the generated conformations by
different coding regions of the gene, allowing for a performing pairwise structural alignments. This process
64
comprehensive assessment of the potential impact on grouped similar conformations, enabling a more organized
protein functionality. analysis of the protein structures. Consequently, five
separate clusters emerged, each having a large number of
3.2. Analysis of missense mutations models with identical structures. I-TASSER then selected
The importance of missense mutations in amino acids a representative model from each of these clusters to
can be used to assess the pathogenic implications of construct five final models that best represented the varied
64
these mutations. PMut, PROVEAN, and Meta-SNPs conformation predictions. The first model was selected
(PANTHER, PhD-SNP, SIFT, and SNAP) were used to for further investigation because it had the greatest
investigate the 299 missense mutations. We found that 53 confidence level (C-score). Ramachandran plots were
47

Volume 3 Issue 1 (2024) 5 https://doi.org/10.36922/td.2512