Page 67 - TD-3-1

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

including sequence information in FASTA format (UniProt proteins with known 3D structures. This information is
42
ID: P25024) from the National Centre for Biological used to generate substitution probability tables, which are
Information (NCBI). 31,32 The structural information of subsequently employed to estimate the predicted impact of
CXCR1 was obtained from the Protein Data Bank (PDB) a mutation on protein stability. 41
(http://www.rcsb.org/) with PDB ID: 2LNL. 33 The mCSM computational approach (http://structure.
2.2. Computer-assisted pathogenicity analysis of the bioc.cam.ac.uk/mcsm) 27 was specifically designed
mutations to predict the effects of missense mutations on both
protein stability and interactions with its corresponding
Computational techniques play a vital role in assessing the ligands. This prediction technique utilizes graph-based
22
effect of mutations on proteins and distinguishing between signatures to capture atom distance patterns, enabling
deleterious and benign single-nucleotide variations. This precise evaluations of the effect of mutations on protein
capability is crucial for accurately distinguishing the impact stability and interactions. In detail, mCSM encodes spatial
of genetic alterations on protein function, facilitating the relationships between atoms and provides insights into
identification of disease-causing mutations. Meta-SNPs the structural consequences of genetic variations at the
are web-based tools (http://snps.biofold.org/meta-snp/) atomic level. These signatures enable the mapping of the
43
that employ multiple predictive algorithms to assess the protein residue environment and the training of predictive
potentially detrimental effects of protein mutations. The models. In addition to evaluating the effect of mutations
23
tool integrates other prediction methods, such as SNAP, on protein stability, mCSM also takes into account possible
34
PhD-SNP, PANTHER, and SIFT, into the system, interactions with proteins and nucleic acids. Therefore,
36
35
making it one of the most powerful tools for predicting the mCSM computational approach can provide valuable
the impact of mutations on protein function. Meta- insights into how mutations might contribute to the
SNPs utilize a random forest algorithm consisting of 100 development of various diseases. 44
decision trees implemented through the WEKA library.
37
29
This algorithm was trained on the SV-2009 dataset using DUET (http://structure.bioc.cam.ac.uk/duet) is a
a rigorous 20-fold cross-validation methodology. The computational method that combines two prediction
23
output of this predictor is a probability score indicating approaches, mCSM and SDM, to accurately predict the
29
the likelihood of a mutation causing a disease, with scores effects of missense mutations on protein stability. The
<0.5 indicating a greater likelihood of the mutation being predictions generated by these methods are combined
pathogenic. 38 using support vector machines (SVMs) that have been
trained with sequential minimal optimization. By
45
The PROVEAN web server applies an alignment- integrating these methods, DUET can produce a consensus
created scoring methodology to determine whether an prediction, offering a reliable and optimized approach for
amino acid replacement affects a protein’s biological accurately predicting the effects of mutations on protein
function. In this approach, mutations with final scores stability. 29
25
<2.5 were considered harmful. The web server can be
accessed at (http://provean.jcvi.org/seq_submit.php). 39 2.4. Evaluation of cancer-related mutations through
the FATHMM server
2.3. Analysis of protein stability
We utilized the FATHMM server (http://fathmm.
Mutations in a protein can modify its stability and free biocompute.org.uk/cancer.html) to assess the relevance of
energy, wherein the difference in free energy (ΔΔG) between harmful mutations to cancer. This web-based tool provides
the mutated (ΔGm) and wild-type (ΔGw) proteins reflects predictions that enable differentiation between cancer-
the effect of mutation on protein stability. 28,40 A negative causing mutations and other nonpathogenic germline
ΔΔG value represents a destabilizing mutation, while a polymorphisms. We accurately assessed the potential
46
positive ΔΔG value indicates a stabilizing mutation. 28,40 role of deleterious mutations in cancer development,
Several computational approaches, including mCSM, enhancing our understanding of the genetic basis of cancer
SDM, and DUET, were used to determine the effect of and facilitating the identification of clinically significant
mutations on protein stability. 28 mutations. 26
SDM2 (http://marid.bioc.cam.ac.uk/sdm2) is a
computational method designed to predict the quantitative 2.5. Protein structure modeling of the mutation site
impact of mutations on protein stability. 28,41 SDM2 analyses To construct a mutant model of CXCR1 with targeted
the variation in potential amino acid substitutions within mutations (N57D, R135C, and P302S), the crystal structure
a specific structural environment tolerated by homologous of the native protein was downloaded from PDB, and the

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