Background:
Cancer is a leading cause of death globally, with around 20 million new cases each year. The disease is mainly caused by genetic mutations that lead to uncontrolled cell growth. Common types of cancer include lung, breast, prostate, colorectal, and skin cancers. Diagnostic methods usually involve imaging tests, biopsies, and lab tests, while treatment options range from surgery and chemotherapy to immunotherapy. Preventative strategies focus on avoiding carcinogens and detecting the disease early through regular check-ups.
Oncogenes are key drivers in cancer, as they promote the growth and spread of cancer cells. Targeting these oncogenes can help in stopping cancer progression. Peptides, short chains of amino acids, offer a promising approach to inhibit these oncogenes. In cancer research, peptides can block specific proteins responsible for cancer progression. This study uses computational biology to design peptides that target and disrupt oncogenes, aiming to develop new therapies that inhibit cancer cell growth and metastasis.
A particular focus of this research is the MYC oncogene, which is responsible for uncontrolled cell division in cancer. MYC binds to DNA through its partner protein MAX, facilitating gene transcription that promotes cancer growth. This interaction is implicated in 60-70% of human cancers. The research hypothesizes that using advanced computational modeling and RF diffusion techniques can develop inhibitors to disrupt the MYC-DNA interaction. By designing peptides that target this interaction, the study aims to provide new strategies for cancer treatment.
Objectives:
Design Peptide Inhibitors: The main objective is to use advanced computational tools to design peptides that block the interaction between the MYC oncogene and DNA. Tools such as RF diffusion, AlphaFold, PyMOL, and RCSB Browser will be used to predict peptide binding sites and their ability to inhibit the MYC-DNA complex.
Disrupt MYC Oncogene Activity: The study will explore how peptides can target specific regions of the MYC-DNA interaction to inhibit oncogene function. This includes analyzing the structural and functional roles of peptides in preventing MYC from promoting cancer cell growth.
Evaluate Peptide Positioning and Binding: Accurately determining the positions of MYC, DNA, and peptides is essential to ensure effective peptide binding. The research will assess spatial arrangements and key interaction hotspots within the MYC-DNA complex.
Therapeutic Potential: Finally, the study will evaluate the therapeutic potential of the peptides by analyzing their ability to interfere with the MYC-DNA interaction, aiming to offer new avenues for cancer treatment.
Materials & Methods:
This research utilized several computational tools and databases to design and analyze peptides capable of inhibiting the MYC-DNA interaction. The following tools were used:
Google Colab & RF Diffusion: Python scripts were run on Google Colab to process protein structures. RF Diffusion was used to predict binding affinity and stability of the peptides, focusing on peptides with IPAE scores below 10, which indicate accurate predictions of spatial interactions.
AlphaFold: This tool was used to predict the 3D structure of MYC and its interaction with peptides. AlphaFold helped identify binding regions and interaction sites by analyzing MYC complexes with the designed peptides.
RCSB Browser & PyMOL: High-resolution structures of MYC and DNA were obtained from the RCSB PDB, and PyMOL was used for visualizing the peptide interactions with MYC and DNA. Key hotspots in the MYC-DNA complex were identified to validate peptide binding.
Results:
Identification of Peptide Candidates: The study identified 30 peptides that showed potential to inhibit the MYC-DNA interaction. Their IPAE values ranged from 6.151 to 9.981, all below the threshold of 10, indicating accurate predicted interactions.
Peptide Binding and Stability: The low IPAE scores suggest strong binding affinity and stability of these peptides to the MYC-DNA complex, disrupting the MYC oncogene's role in cancer progression. Structural analysis confirmed the peptides' ability to interfere with MYC-DNA interactions by targeting key hotspot sites.
Visualization and Hotspot Identification: PyMOL analysis revealed how peptides interact with MYC and DNA, pinpointing specific hotspots critical for binding. These findings demonstrate the effectiveness of the computational tools in identifying peptides with the potential to disrupt MYC-driven oncogenesis.
Discussion:
The research successfully identified 30 peptides with the potential to disrupt MYC-DNA interactions. The use of advanced computational methods, including RF diffusion and AlphaFold, provided precise predictions of binding interactions and hotspot sites. The low IPAE values of the designed peptides highlight their potential effectiveness as inhibitors of MYC-driven cancer progression.
This study supports the hypothesis that combining computational biology tools can generate promising inhibitors of oncogenes like MYC. By targeting the MYC-DNA interaction, these peptides may offer new therapeutic options for cancer treatment. Further experimental validation and in vivo studies are needed to confirm their efficacy and potential clinical applications.
Conclusion:
This study identified 30 peptide candidates capable of disrupting the MYC-DNA interaction, leveraging advanced computational tools like RF diffusion, AlphaFold, and PyMOL. The peptides show promise for targeting key interaction sites in the MYC-DNA complex, potentially inhibiting MYC-driven cancer progression. The research provides a foundation for future studies, with the ultimate goal of developing peptide-based therapies for cancer treatment.
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