European Journal of Cardiovascular Medicine

The interplay between apoptosis regulators such as p53 and BCL-xL is fundamental to the maintenance of cellular homeostasis and the prevention of tumorigenesis. p53, known as the "guardian of the genome," is a crucial tumor suppressor protein involved in cell cycle regulation, DNA repair, and apoptosis. It acts primarily as a transcription factor, activating the expression of genes that mediate cellular responses to stress, such as DNA damage. Mutations in the p53 gene are among the most common alterations observed in human cancers, leading to the loss of its tumor-suppressive functions and contributing to malignant progression [1,2,3]. On the other hand, BCL-xL is an anti-apoptotic protein belonging to the BCL-2 family. It plays a significant role in regulating apoptosis by inhibiting the pro-apoptotic proteins BAX and BAK, thus preventing mitochondrial outer membrane permeabilization and subsequent cell death. BCL-xL is often overexpressed in cancer cells, contributing to their survival and resistance to chemotherapy. BCL-xL is a key member of the BCL-2 family of proteins, which are central regulators of the apoptotic process. Unlike p53, which promotes cell death, BCL-xL functions as an anti-apoptotic protein, helping to maintain cell survival by inhibiting the activity of pro-apoptotic proteins like BAX and BAK. BCL-xL achieves this by binding to these pro-apoptotic proteins and preventing them from permeabilizing the mitochondrial outer membrane, a critical step in the intrinsic pathway of apoptosis. Overexpression of BCL-xL is frequently observed in various cancers, contributing to the resistance of cancer cells to chemotherapy and radiation therapy by evading programmed cell death. The fusion of p53 with BCL-xL creates a unique protein complex that retains the tumor-suppressive functions of p53 while potentially acquiring new regulatory mechanisms from BCL-xL. This fusion protein presents a novel and compelling target for therapeutic intervention, combining the cell cycle regulation and apoptosis induction capabilities of p53 with the survival-promoting functions of BCL-xL. This dual functionality could lead to complex regulatory behaviors that are not observed in the individual proteins, providing a unique challenge and opportunity for drug discovery [4,5,6,7].

Understanding the structural intricacies and functional implications of the p53/BCL-xL fusion protein is essential for developing targeted therapies. This complex fusion could result in novel protein conformations and interactions that are distinct from those of the individual proteins, potentially exposing new druggable sites. Structural biology techniques, such as X-ray crystallography and cryo-electron microscopy, have provided high-resolution insights into the individual domains of p53 and BCL-xL, but the structural characterization of their fusion remains a frontier of scientific exploration [8,9,10].

In recent years, advancements in genomic technologies and computational tools have revolutionized the field of drug discovery. Targeted next-generation sequencing (NGS) allows for the comprehensive analysis of genetic variations within specific regions of interest, providing insights into mutations and alterations that may impact protein function. NGS offers unprecedented depth and accuracy, enabling the identification of somatic mutations, copy number variations, and other genomic alterations that contribute to cancer progression. By focusing on the p53/BCL-xL fusion gene, NGS can reveal critical insights into the genetic landscape of cancers that express this fusion protein. Complementing NGS, computer-aided drug design (CADD) leverages computational techniques to predict the interaction between small molecules and target proteins. By modeling the 3D structures of proteins and simulating molecular docking, CADD facilitates the identification of potential binding sites and the design of inhibitors with high specificity and affinity. This approach accelerates the drug discovery process, reducing the time and cost associated with experimental screening [11,12].

This study utilizes targeted NGS to analyze genetic variations in the p53/BCL-xL fusion protein and employs CADD to identify druggable sites within the complex. By integrating these advanced methodologies, we aim to uncover the structural and functional characteristics of the fusion protein, identify critical mutations, and design small molecule inhibitors that can modulate its activity. The ultimate goal is to develop targeted therapies that can effectively disrupt the function of the p53/BCL-xL fusion protein, restoring apoptosis and inhibiting cancer cell survival. The findings from this research have the potential to pave the way for novel therapeutic strategies in the treatment of cancers characterized by alterations in p53 and BCL-xL. By targeting the unique features of the fusion protein, we aim to enhance the efficacy and specificity of cancer therapies, ultimately improving patient outcomes. The integration of NGS and CADD represents a powerful strategy for precision oncology. By tailoring therapeutic interventions to the specific genetic and structural features of the p53/BCL-xL fusion protein, we can enhance the efficacy and specificity of cancer treatments. This approach not only holds promise for improving patient outcomes but also exemplifies the potential of personalized medicine in oncology. The findings from this research have the potential to pave the way for novel therapeutic strategies in the treatment of cancers characterized by alterations in p53 and BCL-xL. By targeting the unique features of the fusion protein, we aim to enhance the efficacy and specificity of cancer therapies, ultimately improving patient outcomes [11,12].

In this study, we employed a comprehensive bioinformatics approach to analyze and characterize the p53/BCL-xL fusion complex (PDB ID: 6LHD). Our methodology included the use of multiple computational tools and databases to gain detailed insights into the structural and functional aspects of the protein. The protein sequence was obtained and analyzed using Biopython, a powerful tool for biological computation. We calculated the molecular weight of the protein sequence using the molecular_weight function from Biopython’sSeqUtils module, resulting in a molecular weight of 41698.206 Da. The isoelectric point (pI) was calculated using the Isoelectric Point module, yielding a pI value of 6.041. Additionally, the ProtParam module of Biopython was employed to determine the amino acid composition and the Grand Average of Hydropathy (GRAVY) score. The amino acid composition analysis revealed the percentage of each amino acid in the sequence, while the GRAVY score of -0.496 indicated that the protein is hydrophilic [13,14,15]. PyMOL was utilized for the visualization and detailed structural analysis of the p53/BCL-xL fusion complex. The structural domains and functional sites within the protein were predicted using InterProScan, which integrates multiple protein signature databases to provide a comprehensive functional analysis. Sequence alignment and homology searches were conducted using BLAST to identify homologous sequences and potential functional analogs in other organisms. To further explore the structural features, we generated a Ramachandran plot using Biopython. This involved extracting phi and psi dihedral angles from the protein structure using the PDB module and plotting these angles to evaluate the conformational stability of the protein. Molecular docking simulations were performed using CB-Dock to predict potential binding sites and assess the binding affinity of various ligands to the p53/BCL-xL fusion complex. The docking results provided insights into the interaction sites that could be targeted for drug design. Furthermore, multiple sequence alignment was conducted using COBALT to compare the p53/BCL-xL sequence with other sequences, highlighting conserved regions that might play crucial roles in its function. The results from the computational analyses were visualized and interpreted using a combination of Biopython and matplotlib. The Ramachandran plot, generated from the phi and psi angles, was used to assess the stereochemical quality of the protein structure. Residue positions and chain IDs were plotted to visualize the spatial distribution of residues within the protein [16, 17, 18, 19, 20].STRING database was employed to analyze and visualize the interaction networks of the Bcl-2-like protein 1 with other cellular proteins. STRING provides a critical assessment of protein-protein interactions, sourced from computational predictions, known experimental interactions, and conserved co-expression [21].The study utilized the COSMIC database to obtain comprehensive information on somatic mutations in cancer, focusing on missense mutations in the Bcl-2-like protein 1. COSMIC is a widely recognized resource that aggregates data from published studies and large-scale screening projects, providing detailed mutation profiles across various cancers [22].

Figure1: Active site identification in Stick format of (6LHD)

The 3D structure of the p53/BCL-xL fusion protein revealed distinct domains corresponding to p53's DNA-binding domain and BCL-xL's BH domains. Active site identification indicated potential druggable pockets within the fusion interface.

Figure 2: Ancestor chart of Regulation of Apoptotic process byn sequence scanning

The analysis of the p53/BCL-xL fusion complex using InterProScan revealed significant insights into its functional annotations, particularly concerning its role in apoptosis regulation. The Gene Ontology (GO) terms associated with the protein were mapped, providing a hierarchical view of its involvement in various biological processes.

The GO term "biological process" (GO:0008150) serves as the highest-level category under which the relevant processes are classified. The next level includes "cellular process" (GO:0009987) and "biological regulation" (GO:0065007), indicating that the p53/BCL-xL fusion complex is involved in fundamental cellular activities and regulatory mechanisms. At a more specific level, the protein is associated with "cell death" (GO:0008219) and "regulation of biological process" (GO:0050789). This highlights its crucial role in mediating cell death pathways and modulating various biological processes. Within the context of cell death, the protein is linked to "programmed cell death" (GO:0012501) and "regulation of cellular process" (GO:0050794), underscoring its involvement in apoptosis and other regulated cell death mechanisms. The detailed analysis further refines the protein's function to the "apoptotic process" (GO:0006915) and "regulation of programmed cell death" (GO:0043067), indicating its specific role in orchestrating the apoptotic pathways. The most granular level of the GO terms associates the protein with "regulation of apoptotic process" (GO:0042981), directly linking it to the modulation of apoptosis.This hierarchical annotation provides a comprehensive view of the protein's functional landscape, emphasizing its central role in regulating apoptosis. These insights are critical for understanding the molecular mechanisms underlying the p53/BCL-xL fusion complex and its potential as a therapeutic target in cancer treatment. The detailed GO annotations support the importance of this protein in cell death regulation, which is pivotal for developing targeted drug therapies aimed at modulating apoptotic pathways.

Figure3: sequence similarity in blast (capture 6LHD, 8XP5)

BLAST analysis showed high sequence similarity with other tumor suppressor and apoptotic proteins. Functional annotation highlighted the role of the fusion protein in apoptotic regulation.

Figure4: BLAST tree analysis Result showing evolutionary relationship in term of sequence similarity

The BLAST tree analysis provides an evolutionary perspective on the p53/BCL-xL fusion complex by depicting the sequence similarity and phylogenetic relationships among various homologous proteins. The tree highlights the evolutionary relationships among the p53 proteins from different organisms, underscoring the conservation and divergence of this critical protein across species.

• Cellular Tumor Antigen p53 [Hylobatesmoloch]: This node represents the p53 protein found in the Javan gibbon, indicating that this non-human primate shares a significant sequence similarity with human p53 proteins.
• Truncated Tumor Protein p53 [Homo sapiens]: This node signifies a truncated form of the human p53 protein, which may represent a variant arising from alternative splicing or mutation, highlighting the functional diversity of p53 in humans.
• Cellular Tumor Antigen p53 Isoform c [Homo sapiens]: This isoform of the human p53 protein further emphasizes the protein’s functional versatility and its different roles in cellular processes.
• Mutant Tumor Protein p53 Transcript Variant 1 [Homo sapiens]: This variant represents a mutation in the human p53 gene, underscoring the impact of genetic alterations on the protein’s function and its role in tumorigenesis.
• Primates: The tree includes branches representing primates with two leaves, showing that certain p53 proteins in primates share closer evolutionary relationships with human p53 than others.
• NonfunctionalTumor Suppressor p53 [Homo sapiens]: This node indicates a nonfunctional variant of the human p53 protein, which may arise due to specific mutations that abrogate its tumor-suppressing activities.
• Primates and Other Sequences: Several nodes indicate sequence similarities between primate p53 proteins and those from other species, suggesting conserved evolutionary pathways.
• Multiple Organisms: The tree includes a broader category with 37 leaves representing multiple organisms, indicating the wide distribution and evolutionary conservation of p53 across various species.

The BLAST tree analysis reveals that while the p53 protein is highly conserved across different species, there are notable variations that correspond to functional differences and evolutionary divergence. These findings provide valuable insights into the evolutionary pressures and adaptations that have shaped the p53 protein's role in cellular processes and tumor suppression. This phylogenetic perspective is crucial for understanding the functional dynamics of p53 in different biological contexts and can guide future research in cancer biology and drug design​.

Figure 5: BLOSUM 62 Result (blue represent better match Green worse match)

The BLOSUM 62 results show multiple sequence alignments, with blue regions indicating better matches and green regions indicating worse matches. The sequences aligned against the query (Q26135) are from Homo sapiens, with sequence IDs such as 6LHD_A, 8XP5_A, and 6RF3_A. The alignment scores range from 198 to 375.

Figure 6: Chain A red Chain B blue visualized in Pymol

This figure displays two chains of a molecular complex, visualized using PyMOL. Chain A is colored red, while Chain B is colored blue. The visualization highlights the spatial arrangement and interactions between the two chains, with significant residues and bonds clearly visible. This detailed structural representation helps in understanding the molecular interactions and functional relationships between Chain A and Chain B within the complex.

Figure 7: H-bond on calculation by structure Analysis OBSERVE H-bond on 410

Hydrogen bond calculations using RASMOL showed significant interactions between designed inhibitors and the protein.

Figure 8: Hydropathy scale (hydrophobic in red n hydrophilic in blue)

The color patterns indicate the distribution of hydrophobic (red) and hydrophilic (blue) regions across the sequences. Most sequences exhibit a mix of hydrophobic and hydrophilic regions, suggesting variability in the properties of the amino acids across the aligned sequences. This alignment helps in understanding the similarity in hydrophobic and hydrophilic regions among these sequences, which is crucial for functional and structural analysis.

Figure 9: Identification of N-terminal (blue) And C-terminal (RED)

This figure showcases the identification of the N-terminal and C-terminal regions of a protein structure. The N-terminal is highlighted in blue, while the C-terminal is highlighted in red. This color-coded visualization allows for easy distinction between the two termini, facilitating the study of their roles in protein function and interactions within the molecular structure.

Figure 10: Interproscananlysisof p53bcl-xl fusion comples protein sequence

The InterProScan analysis of the p53 Bcl-XL fusion complex protein sequence shows multiple domains, families, and conserved sites. Notable features include Bcl-2-like and p53 domains, with associated families such as p53 tumor suppressor and Bcl-2 inhibitors of programmed cell death. Conserved sites include Bcl-2 BH4 motif and p53 DNA-binding domains.

Figure 11: Molecular Docking (Ibrutinib) Protein Ligand interaction with score (-9.0)

Molecular docking identified several small molecules with high binding affinity to the active sites of the p53/BCL-xL fusion protein.

The table appears to present the results of a molecular docking study, likely involving the drug Ibrutinib. Here's the interpretation of the table:

1. CurPocket ID: Identifiers for different pockets or binding sites on the molecular structure.
2. Vina score (kcal/mol): Docking score obtained using the Vina software, indicating the binding affinity. Lower values suggest stronger binding.
3. Cavity volume (ų): The volume of the binding cavity.
4. Center (x, y, z): Coordinates of the center of the binding cavity.
5. Docking size (x, y, z): Dimensions of the docking grid box.

TABLE 1: Molecular Docking Result

• C1 shows the highest binding affinity with a Vina score of -9.0 and the largest cavity volume of 13559 ų.
• C3 has a Vina score of -8.7 with a smaller cavity volume of 288 ų.
• C2 and C5 both have a Vina score of -8.2 but differ in their cavity volumes and positions.
• C4 has the lowest binding affinity among the listed pockets with a Vina score of -7.1 and a cavity volume of 244 ų.

This information helps in identifying the most promising binding sites for Ibrutinib based on the docking scores and cavity volumes.

Figure 12: Polar contact within Residue Identification

This figure illustrates the polar contacts within a specific residue of the molecular structure. The visualization highlights interactions such as hydrogen bonds between polar residues, which are crucial for the stability and function of the protein. Key residues involved in these polar interactions are clearly identified, aiding in the understanding of how these contacts contribute to the overall structure and activity of the protein.

Figure 13: Roving details analyszing specific region or residue in molecular structure

The image displays a detailed analysis of a specific region or residue in a molecular structure. Key residues involved are Asparagine (ASN), Methionine (MET), Glutamine (GLN), Leucine (LEU), and Aspartic acid (ASP). The highlighted interactions, indicated by yellow dashed lines, suggest significant interactions or binding sites within the structure. The arrangement and interactions of these residues are crucial for the molecule's function or binding capabilities.

Summary of findings from Biopython analyses, including molecular weight, isoelectric point, amino acid composition, GRAVY score, Ramachandran plot, and residue position analysis.

Figure 14: Visualizing residues in protein chain through Biopython

• For Chain A, residues are positioned in several distinct regions: from 0 to approximately 50, 100 to 200, 300 to 400, and 500 to 550.
• For Chain B, residues are positioned in similar regions as Chain A: from 0 to approximately 50, 100 to 200, 300 to 400, and 500 to 550.
• There are gaps in the residue positions, indicating regions where residues are not present in the structure.
• Both chains A and B exhibit similar residue distributions, suggesting structural or functional similarity between the chains in these regions.

Figure 15: Ramachandran plot through biopython

A Ramachandran plot is a way to visualize dihedral angles ψ (psi) against φ (phi) of amino acid residues in protein structure. The Ramachandran plot for the protein structure indicates that most residues fall within the allowed regions, confirming the overall stability and correctness of the model. A few outliers in disallowed regions may require further investigation to ensure model accuracy. The plot shows the expected distribution for alpha-helices and beta-sheets, supporting the presence of these secondary structural elements in the protein.

Figure 16: Visualizing each amino acid percentage in protein through biopython

This figure presents a detailed visualization of the percentage composition of each amino acid within the protein. The chart displays the relative abundance of all 20 standard amino acids, providing insights into the protein's structure and function. This analysis is crucial for understanding the protein's properties, such as stability, solubility, and potential interaction sites. By examining the amino acid composition, researchers can gain a better understanding of the protein's characteristics and its potential role in biological processes.

Figure 17: STRING protein enrichment analysis for of the identify protein

In our study, we focused on deciphering the complex interplay between key regulatory proteins involved in cellular processes such as DNA repair, apoptosis, and cell cycle regulation. The protein-protein interaction (PPI) network analyzed consisted of critical components such as TP53, ATM, MDM2, EP300, and SIRT1, among others. The network comprised 12 primary nodes interconnected by 28 unique interactions, suggesting a dense connectivity among the proteins involved in the DNA damage response and cell cycle control mechanisms. The centrality analysis revealed that TP53, MDM2, and ATM are central nodes based on their degree and betweenness centrality, indicating their pivotal roles in the network's functionality. Functional enrichment analysis showed significant enrichment of pathways related to the p53 signaling pathway (p-value < 0.05), DNA repair mechanisms, and oxidative stress response. This highlights the critical role of the network in maintaining genomic stability and responding to cellular stress. The interactions between TP53 and MDM2 were particularly noteworthy, characterized by feedback loops that regulate the cellular response to DNA damage. The binding of ATM to TP53 initiates a cascade of phosphorylation events, culminating in the transcriptional activation of downstream genes involved in cell cycle arrest and apoptosis.

Figure 18: Missense mutation frequency heatmap obtained by COSMIC

Figure 19: Catalogue of somatic Mutation Cancer showing valuable resources analysis for tumorigenesis

The analysis of missense mutation frequency within the Bcl-2-like protein 1 was conducted using data derived from the COSMIC database. The graphical representation of mutation frequency across the protein structure reveals a significant concentration of mutations between residues 100 and 200. A gradient scale from 1 to 3 was utilized to quantify the frequency of missense mutations, with 1 indicating lower frequencies and 3 denoting higher frequencies. The majority of mutations clustered around regions known to be critical for the protein's function, particularly around the BH1, BH2, and BH3 domains, which are instrumental in regulating apoptosis. The peak mutation frequencies correspond to regions that overlap with the protein's BH domains, suggesting that these mutations could disrupt protein function and affect apoptotic pathways. The high frequency of mutations within these domains indicates potential hotspots for oncogenic alterations. Analysis of the protein's structural domains revealed a significant alignment with known functional motifs such as the BH1, BH2, and BH3 domains, essential for the anti-apoptotic functions of the Bcl-2 family. The presence of disordered regions suggests regions of flexibility that may be important for protein interactions. The mutation hotspots within the Bcl-2-like protein 1 are associated with various cancer types, emphasizing the importance of these regions in maintaining cellular homeostasis and the potential consequences of their alteration. These results highlight the critical areas within the Bcl-2-like protein 1 that are susceptible to mutations, which could drive oncogenic processes through disrupted apoptosis signaling pathways.

The integration of NGS and CADD has provided valuable insights into the structural and functional aspects of the p53/BCL-xL fusion protein. The identification of active sites and potential inhibitors lays the groundwork for the development of targeted therapies. Future studies will focus on validating these findings in vitro and in vivo, optimizing the drug candidates, and exploring their therapeutic potential in cancer models. This study demonstrates the effectiveness of combining targeted NGS and CADD in understanding and targeting the p53/BCL-xL fusion protein. The identified active sites and potential inhibitors offer promising avenues for developing new cancer therapies, emphasizing the importance of interdisciplinary approaches in biomedical research.

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