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Tumor Discovery Bioinformatics insights into CCL2 mutations

protein. The altered protein might not perform its normal 3.6. Structure validation of native and mutant CCL2
functions effectively, potentially contributing to disease proteins
development or progression. Advanced computational The reference model for the native protein was the
methods were employed to assess protein stability, identify 3D structure of CCL2 (PDB ID 2LIE). The I-TASSER
cancer-causing mutations, and predict pathogenicity. 72,73
server was used to identify the template proteins with
3.1. Collection of missense mutations similar folds in the PDB database and generate 10,000
conformations (decoys) based on a mutant sequence.
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To explore the potential effects of CCL2 mutation on protein I-TASSER used the SPICKER tool to group the generated
function, we collected a comprehensive dataset comprising conformations through pairwise structural alignments,
83 mutations from a publicly accessible database. These identifying models with structural similarities. This
mutations were located across various coding regions of process typically results in five clusters, each containing
the gene, enabling a thorough assessment of their potential multiple models with identical structures. I-TASSER
effects on protein functionality.
then selects a representative model from each cluster to
3.2. Missense mutation analysis generate five final models that best represent the diverse
conformations predicted. The model displaying the
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The deleterious effects of mutations can be anticipated highest confidence score (C-score) was selected for
by evaluating the importance of the affected peptides. To subsequent analysis. Ramachandran plots were used to
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analyze the 83 missense mutations in CCL2, computational assess the model’s quality by examining the distribution
tools such as Polyphen-2 and Meta-SNP (comprising of the protein backbone torsion angles (φ and ψ) and
Panther, PhD-SNP, SIFT, and SNAP) were employed identifying any structural issues. The Ramachandran
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(Table 1). The results revealed that 10 mutations were likely plot analysis revealed that 90% of native residues and 89%
disease-causing and distributed across various protein of mutant residues were positioned in the favored region
domains.
of the native CCL2 structure, indicating a high degree of
3.3. CCL2 mutations affect protein stability structural similarity (Figure 3).

Three algorithms, mCSM, SDM, and DUET, were used 3.7. MD simulation of CCL2
to predict the effects of 83 mutations on CCL2 protein
stability. The results are shown in Table 2. mCSM predicted To investigate the effects of the C59G mutation on the
that 40 mutations in CCL2 were likely highly destabilizing. CCL2 protein, a 100-ns MD simulation was performed.
The simulation tracked various structural parameters,
3.4. Identification of cancer-causing mutations in including secondary structure elements, to monitor
CCL2 the protein’s behavior and identify any changes in its
conformational dynamics.
Using the FATHMM server, 83 CCL2 mutations were
analyzed to assess their potential involvement in cancer The RMSD was computed for the backbone residues
development or progression. The analysis results suggest that of both the native and mutant CCL2 proteins to assess
the C59G mutation alters the binding dynamics between their deviation from their initial structures throughout the
CCL2 and CCR2, potentially reducing the risk of cancer and simulation and quantify the extent of structural variation.
enhancing the immune response to pathogen invasion. The RMSD values for the mutant CCL2 protein (C59G)
were substantially higher than those of the native protein
3.5. Conservation analysis of CCL2 mutations (Figure 3A), indicating a higher degree of structural
The C59G mutation, identified as a cancer-causing instability in the mutant structure. This suggests that the
mutation, was selected for further analysis. We utilized the C59G mutation introduced substantial conformational
ConSurf server to investigate the conservation and spatial changes, potentially affecting the protein’s stability and
distribution of the C59G mutation, which offered valuable function. MD simulations revealed that the native CCL2
insights into its potential functional implications. protein remained structurally stable throughout the
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ConSurf analysis revealed that the C59G mutation simulation and that the mutant proteins, particularly
occurred at a highly conserved position, obtaining the C59G, exhibited a longer stabilization period. The RMSD
maximum conservation score of 9 on the ConSurf scale values for the mutant proteins steadily increased between
(Figure 2A and B). These findings prompted further 15 and 30 ns, indicating a higher degree of structural
computational investigations into the potential effects of deviation from the initial conformation. In contrast, the
this mutation on the stability and function of the CCL2 RMSD values for the native C59C–CCL2 protein remained
protein. relatively stable, which suggests that the C59G mutation

Volume 3 Issue 4 (2024) 7 doi: 10.36922/td.3891