Prostate cancer (PCa) is the second most commonly diagnosed cancer worldwide and the most frequent cancer among men in Mexico, with Sinaloa reporting the highest incidence (GLOBOCAN 2022). PCa arises from uncontrolled proliferation of malignant prostate cells, with multiple contributing factors including genetic alterations such as mutations in the tumor suppressor gene PTEN, a key regulator of the PI3K/AKT pathway. Through a systematic literature review and bioinformatics analysis (NCBI, PDB, UniProt, UCSF Chimera, WinCoot, Mutation3D, SNPs&GO, I-Mutant 2.0, PyMOL 3.0), 30 PTEN mutations associated with PCa were identified. Among these, D107Y, R130Y, and R335H were selected for in silico structural and functional analysis due to their predicted deleterious effects and significant structural changes. Structural modeling confirmed high-quality PTEN models, while Mutation3D and PyMOL analyses revealed that R130Y and R335H induce alterations in bond formation and tertiary structure, potentially affecting phosphatase activity and membrane anchoring. SNP-based predictions suggest that all three mutations may impair PTEN function, although experimental validation is required. These findings highlight the potential impact of specific PTEN mutations on protein stability and function in PCa and propose these variants as targets for future in vitro studies to explore their utility as molecular markers for diagnosis.
Research Article
In silico analysis of D107Y, R130Y, and R335H mutations in PTEN protein and their relationship with prostate cancer
https://doi.org/10.21203/rs.3.rs-8090201/v1
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Mutations
PTEN
Prostate Cancer
In Silico Analysis
Tumour Suppressor
Prostate cancer (PCa) is the second most commonly diagnosed cancer worldwide, with 1,467,854 new cases (14.2%) and approximately 397,430 deaths (7.3%) per year, according to GLOBOCAN 2022 (Bray et al., 2024). In Mexico, more than 26,565 new cases are registered annually, making PCa the most frequent cancer among men (27.7% of all male cancers) and causing around 7,358 deaths per year (Bray et al., 2024). In particular, Sinaloa reports the highest incidence of PCa cases nationwide (Ertay, 2023), representing a significant public health challenge.
PCa arises from abnormal prostate growth due to uncontrolled proliferation of malignant cells (Moradi, 2022). It mainly affects men over 65 years, as the disease progresses slowly and is often asymptomatic. When present, symptoms may include fatigue, anemia, skeletal complications such as pain, fractures, or spinal cord compression, and kidney failure caused by bilateral ureteral obstruction. Diagnosis is primarily based on prostate-specific antigen (PSA) testing and biopsies, while treatment options include androgen-blocking drugs, chemotherapy, radiotherapy, and prostatectomy (Morote et al., 2016; Pérez et al., 2020; Savón Moiran, 2019). Although its etiology is not fully understood, several factors have been implicated, including age, ethnicity, diet, inflammation, lifestyle, family history, and genetic alterations in genes such as ATM, BRCA1, BRCA2, RAS, HOXB13, and PTEN (Pérez et al., 2020; Sekhoacha et al., 2022; Pejčić et al., 2023).
PTEN is a tumor suppressor gene located at 10q23 that encodes a 403-amino-acid protein expressed in all tissues. It contains two main domains: a catalytic phosphatase domain (PTP) spanning amino acids 22–185, which removes phosphate groups, and a C2 domain (amino acids 190–351) that anchors the protein to membranes (Mirantes, 2015; Ortega-Molina & Serrano, 2013; Sotelo, 2011).
Structurally, the phosphatase domain has five central β-sheets flanked by α-helices and contains the characteristic HCXXGXXR motif found in protein tyrosine phosphatases (PTPs) and dual-specificity phosphatases (DSPs) (Mirantes, 2015; Persad et al., 2000; Sotelo, 2011). It can dephosphorylate phosphotyrosine, phosphothreonine, and the D3 position of PIP3 (phosphatidylinositol 3,4,5-triphosphate) (Mirantes, 2015; Persad et al., 2000). The C2 domain has a β-sandwich structure formed by two antiparallel β-sheets and two α-helices, enabling Ca²⁺-independent membrane binding (Castiblanco, 2006; Torrecillas Sánchez, 2010; Sotelo, 2011).
As a tumor suppressor, PTEN antagonizes the PI3K/AKT signaling pathway. It acts as a lipid phosphatase, converting PIP3 into PIP2 to regulate cell proliferation, differentiation, and apoptosis (Ho, 2020). PTEN is frequently mutated in human tumors, and these mutations can impair protein stability and function. The most commonly reported PTEN mutations—R130X, R233X, and R335X—occur in glioblastoma, endometrial cancer, and PCa (Yehia, 2020; Ertay, 2023). Studying PTEN alterations, its interactions, and their role in PCa is therefore crucial.
In silico studies provide valuable insights into PTEN’s structure and interactions, enabling predictions of functional consequences and guiding subsequent in vitro experiments (Calabrese, 2009).
The present study aimed to analyze the structural effects of reported PTEN mutations in silico and their potential relationship with PCa. Additionally, these mutations are proposed as targets for future in vitro studies, which may identify molecular markers for PCa diagnosis.
2.1 Literature and Database Search
A bibliographic review was conducted using the National Center for Biotechnology Information (NCBI), Protein Data Bank (PDB), and UniProt databases to identify the most frequently reported mutations in the PTEN gene and their pathological implications in prostate cancer (PCa). The following query was used: "(PTEN OR 'PTEN gene' OR 'tumor suppressor PTEN') AND ('prostate cancer') AND (mutation OR mutations)". From this search, thirty PTEN mutations associated with PCa were identified.
2.2 Structural Analysis of the PTEN Protein
The three-dimensional structure of the PTEN protein (PDB code: 1D5R) was analyzed to assess model quality, electron density distribution, and conformational stability using UCSF Chimera (Pettersen et al., 2004) and WinCoot (Emsley et al., 2010).
2.2.1 Model Evaluation with UCSF Chimera
UCSF Chimera (Huang et al., 2014) was used to visualize and refine the PTEN protein model, ensuring the presence and correct positioning of amino acids of interest within the reconstructed 3D structure. This tool facilitated the identification of structural regions with minimal free energy and potential relevance to functional stability.
2.2.2 Structural Validation with WinCoot
WinCoot (Emsley et al., 2010) was employed to validate the PTEN protein model through the generation of a Ramachandran plot, evaluating the stereochemical quality and conformational accuracy of amino acid residues. This analysis allowed visualization of the overall geometry and electronic interactions within the protein structure.
2.3 Association of PTEN Mutations with Cancer
The Mutation3D web server (https://mutation3d.org/) was used to identify potential clusters of amino acid substitutions within the PTEN structure associated with tumorigenesis. This platform integrates over 975,000 somatic mutations from 6,811 cancer sequencing studies, referencing data from PDB, UniProt, ModBase, COSMIC, and PFAM (Roy et al., 2024).
2.3.1 Structural Mapping with Mutation3D
Mutation3D (Meyer et al., 2016) predicts the likelihood that amino acid substitutions contribute to cancer development based on spatial arrangement and functional relevance. The PTEN structure (PDB code 1D5R) and the thirty identified mutations were analyzed to visualize their distribution, identity, and similarity among different reported models. Mutations associated with PCa were examined for their spatial clustering and potential structural effects.
2.4 Computational Prediction of Mutation Effects
The software SNPs&GO (Capriotti et al., 2013), I-Mutant 2.0 (Capriotti et al., 2005), and PyMOL 3.0 (Schrödinger, 2024) were employed to predict the functional and structural consequences of each mutation. Protein sequences in FASTA format were retrieved from the NCBI reference (NC_000010.11). Analyses were performed at 25°C and pH 7, as standardized in I-Mutant 2.0. From the thirty mutations analyzed, only those predicted to induce structural alterations in PyMOL with a reliability index > 5 and classified as deleterious by both SNPs&GO and I-Mutant 2.0 were selected for further analysis.
2.4.1 Structural Stability Analysis with PyMOL
The PTEN structure (1D5R) was visualized in PyMOL 3.0 to perform in silico mutagenesis. Ligand and interaction tools were used to evaluate inter- and intramolecular effects of amino acid substitutions. PyMOL’s three-dimensional visualization capabilities facilitated the observation of conformational changes and the generation of dynamic molecular representations simulating biological interactions (Schrödinger, 2024).
2.4.2 Prediction of Deleterious Effects via SNP-Based Tools
SNPs&GO predicts disease-related mutations with an accuracy of 82% and a Matthews correlation coefficient (MCC) of 0.63, integrating information from protein sequence, profile, and function (Li, 2017). I-Mutant 2.0 predicts the impact of amino acid substitutions on protein stability with 80% accuracy when 3D structure data are available, and 77% when based solely on sequence (Calabrese, 2009). These complementary predictions allowed the identification of PTEN variants potentially contributing to functional impairment and prostate cancer progression.
3.1 Selection of PTEN Mutations for Computational Analysis
We conducted a systematic search in NCBI using the formula [(((PTEN gene) OR (tumor suppressor) AND (prostate cancer))) AND (mutation)], identifying 277 articles. Selection criteria included publication year (2000 onwards), species (human), and availability of full text. These studies reported a descriptive panel of mutations previously observed in prostate cancer (PCa), encompassing genomic characteristics of the disease, potential biomarkers, and the roles of tumor suppressor genes in cellular functions and signaling pathways mainly related to cell growth. Based on this literature, we compiled 30 PTEN mutations reported in association with PCa (Table 1), of which 29 correspond to amino acid substitutions and one to a frameshift mutation. Among all the mutations analyzed, D107Y, R130Y, and R335H were selected for predictive analysis because they exhibited the greatest structural changes in PyMOL 3.0 (Fig. 1), resulting from the original amino acid substitutions (Table 2). These variants also showed a reliability index greater than 5 in both SNPs&GO and I-Mutant 2.0, supporting the prediction of a deleterious effect associated with decreased protein structural stability.
3.2 Structural Modeling and Validation of the PTEN Protein
UCSF Chimera was used to visualize the PTEN crystal structure (PDB code 1D5R) and confirmed that the amino acids of interest—D107, R130, and R335—are present. Missing residues were incorporated to reconstruct the complete protein, generating a new three-dimensional model (Fig. 2). The quality of this model was then evaluated using a Ramachandran plot (Fig. 3). Analysis revealed that 85.15% of the phi and psi angles fall within favored regions, indicating that the reconstructed PTEN model has good structural quality.
3.3 Structural Mapping of PTEN Mutations
The selected PTEN mutations (D107Y, R130Y, and R335H) were analyzed using Mutation3D, a software that maps amino acid substitutions onto the protein's three-dimensional structure. Mutation3D identifies spatial clusters of mutations and their locations relative to functional domains, providing insight into potential structural impacts without directly predicting pathological phenotypes. In our analysis, these mutations did not form clusters associated with prostate cancer or exhibit a common structural pattern linked to the disease (Fig. 4). The spatial arrangement of the substitutions, combined with data from multiple mutation databases, suggests that no shared etiology can be inferred at the proteome level. Nevertheless, these mutations may still have independent effects on protein structure or function.
3.4 Structural and Functional Prediction of PTEN Mutations
3.4.1 Structural-Based Prediction of Protein Stability
Only the R130Y and R335H mutations showed modifications in their tertiary structure, as observed in PyMOL 3.0 (Fig. 1). Changes in bond formation were detected: R130Y exhibited the addition of chemical groups, while R335H showed potential interactions with other amino acids and molecules.
3.4.2 SNP-Based Prediction of Deleterious Effects
SNPs&GO and I-Mutant 2.0 analyses indicated that D107Y, R130Y, and R335H are predicted to be deleterious. The reliability indices (RI) were 10 in SNPs&GO and 4, 7, and 4 in I-Mutant 2.0, respectively. These results suggest that the three selected mutations may have a potential impact on PTEN function, although experimental validation would be required to confirm pathogenic effects.
The PTEN gene functions as a tumor suppressor and regulates the PI3K-PKB/AKT pathway, which controls cell proliferation and apoptosis (Yehia et al., 2020). In this study, 30 PTEN mutations reported in PCa were analyzed using in silico approaches. Among these, D107Y, R130Y, and R335H were identified as the variants most likely to affect protein stability and function, suggesting a potential role in PCa development.
Structural modeling in PyMOL 3.0 indicated that R130Y may disrupt interactions in the phosphatase domain by replacing a basic, positively charged arginine with a polar, uncharged tyrosine, potentially reducing PIP3 dephosphorylation and altering PI3K-PKB/AKT pathway activity (Baynes et al., 2007; Pinzón et al., 2009; Alfaro, 2019). R335H could modify bond formation in the C2 domain due to the imidazole side chain of histidine, possibly affecting membrane anchoring and phosphatase activity (Das et al., 2003). D107Y, while not showing major tertiary changes in modeling, involves a residue important for nucleic acid synthesis, suggesting a potential functional impact.
These potential effects are influenced by the physicochemical properties of the residues. Tyrosine (positions 107 and 130) is involved in metabolic regulation and receptor signaling; arginine (positions 130 and 335) contributes to GABA synthesis and has been associated with tumor size reduction (Katchalski, 2006); and histidine (position 335) participates in tissue repair, blood cell formation, anti-inflammatory responses, histamine synthesis, and pH regulation (Cervantes De La Cruz et al., 2017). Although these properties are residue-specific, they provide insight into how amino acid substitutions could impact PTEN function.
It is important to emphasize that these proposed effects are predictions based on computational modeling and the physicochemical characteristics of the amino acids, not experimentally confirmed outcomes. Therefore, the actual impact of D107Y, R130Y, and R335H on PTEN activity and prostate cancer progression should be interpreted with caution. Experimental studies are necessary to validate these findings and determine the functional consequences of these mutations in cellular and physiological contexts.
In this study, three PTEN mutations (D107Y, R130Y, and R335H) were identified as potentially affecting protein stability and function in prostate cancer based on in silico analyses. Structural modeling and consideration of amino acid properties suggest that these substitutions could alter PTEN interactions and activity, with possible implications for the PI3K-PKB/AKT pathway. However, these effects remain predictions, and further experimental studies are required to confirm their impact on PTEN function and prostate cancer progression.
6. DECLARATION OF CONFLICTS OF INTEREST
The authors declare that there is no conflict of interest.
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Table 1. Most frequently reported mutations in PTEN and their respective effect were analyzed in each of the bioinformatics tools used.
|
Position |
Wild residue |
Mutated residue |
SNP&GO |
I Mutant2.0 |
PyMOL |
||
|
Prediction |
RI |
Stability |
RI |
Prediction |
|||
|
107 |
D |
Y |
Disease |
10 |
Decreases |
4 |
Deleterious mutation |
|
130 |
R |
Y |
Disease |
10 |
Decreases |
7 |
Deleterious mutation, modification in bond formation |
|
G |
Disease |
10 |
Decreases |
8 |
Modification in bond formation |
||
|
A |
Disease |
10 |
Decreases |
9 |
No change |
||
|
P |
Disease |
10 |
Decreases |
7 |
Deleterious mutation, modification in bond formation |
||
|
S |
Disease |
10 |
Decreases |
8 |
No change |
||
|
T |
Disease |
10 |
Decreases |
8 |
No change |
||
|
C |
Disease |
10 |
Decreases |
7 |
No change |
||
|
H |
Disease |
10 |
Decreases |
9 |
Deleterious mutation, modification in bond formation |
||
|
K |
Disease |
10 |
Decreases |
8 |
Adding a link |
||
|
Q |
Disease |
10 |
Decreases |
9 |
No change |
||
|
E |
Disease |
10 |
Decreases |
8 |
No change |
||
|
N |
Disease |
10 |
Decreases |
9 |
No change |
||
|
D |
Disease |
10 |
Decreases |
9 |
Modification in bond formation and ligand angles |
||
|
132 |
G |
D |
Disease |
10 |
Decreases |
9 |
No structural change, only in ligand angles. |
|
147 |
K |
G |
Disease |
9 |
Decreases |
5 |
No change |
|
H |
Disease |
9 |
Decreases |
8 |
Modification in bond formation and ligand angles |
||
|
A |
Disease |
9 |
Decreases |
4 |
Deleterious mutation, modification in bond formation |
||
|
173 |
R |
C |
Disease |
10 |
Decreases |
6 |
Modification in bond formation and ligand angles |
|
233 |
R |
A |
Disease |
10 |
Decreases |
8 |
No change |
|
G |
Disease |
10 |
Decreases |
6 |
No change |
||
|
S |
Disease |
10 |
Decreases |
6 |
Modification in bond formation and ligand angles |
||
|
H |
Disease |
9 |
Decreases |
7 |
Deleterious mutation, modification in bond formation |
||
|
K |
Disease |
9 |
Decreases |
7 |
Deleterious mutation, modification in bond formation |
||
|
Q |
Disease |
10 |
Decreases |
8 |
Modification in bond formation and ligand angles |
||
|
252 |
D |
Y |
Disease |
10 |
Decreases |
8 |
Deleterious mutation, modification in bond formation |
|
L295fs |
L |
Frame Shift |
- |
- |
- |
- |
- |
|
335 |
R |
K |
Disease |
9 |
Decreases |
6 |
Modification in bond formation and ligand angles |
|
Q |
Disease |
7 |
Decreases |
4 |
Modification in bond formation and ligand angles |
||
|
H |
Disease |
9 |
Decreases |
4 |
Deleterious mutation, modification in bond formation |
||
*RI = Reliability index.
Table 2. Amino acid structural change prediction in PTEN mutations using MolView tool.
|
Mutation |
Type of change |
Wild type |
Mutant |
|
D107Y |
Acid for aromatic |
|
|
|
R130Y |
Basic for aromatic |
|
|
|
R335H |
Change of basic amino acids |
|
|
The authors declare no competing interests.