Computational Drug Discovery

Exploration of Bacterial and Fungal Natural Products Targeting ERα for Therapeutic Intervention in Breast Cancer

📅 Duration: June 2024 – August 2025
🏢 Affiliation: BioLab Bangladesh, Bioinformatics Laboratory
👨‍🔬 Collaborator: Noimul Hasan Siddiquee, Department of Microbiology, Noakhali Science and Technology University

📝 Project Synopsis:

As the team lead on this computational biology project, we identified two promising lead compounds targeting estrogen receptor alpha (ERα) from over 36,500+ natural products in the NPAtlas database. Using the Desmond software suite, we conducted comprehensive analyses including molecular docking, ADMET profiling, post-docking MM-GBSA binding energy calculations, Density Functional Theory (DFT) studies, and extensive molecular dynamics simulations. These simulations evaluated parameters such as RMSD, RMSF, hydrogen bonding, solvent-accessible surface area (SASA), radius of gyration, principal component analysis (PCA), dynamic cross-correlation matrix (DCCM), and protein-ligand contacts to confirm the stability and inhibitory potential of the compounds. The study aims to discover novel ERα inhibitors to advance breast cancer treatment.

🧪 Skills Applied:

• Molecular Dynamics Simulation

• Molecular Docking

• ADMET Analysis

• MM-GBSA Binding Energy Calculations

• Density Functional Theory (DFT)

• Computer-Aided Design (CAD)

• Cheminformatics

📚 Publication (Under Review): Manuscript submitted to PlosOne and currently under peer review.  

Computational Identification of Taxus baccata-Derived EZH2 Inhibitors to Overcome Melanoma Therapy Resistance

📅 Duration: January 2025 – December 2025
🏢 Affiliation: CHIRAL Bangladesh
👨‍🔬Collaborator: Md. Jubayer Hossain, Department of Microbiology, Jagannath University 

📝 Project Synopsis:

This computational research aims to identify potent phytochemical inhibitors of the oncogenic enzyme EZH2 (Enhancer of Zeste Homolog 2) derived from Taxus baccata to counter therapy-resistant melanoma. Given the emerging role of EZH2 in melanoma progression and treatment resistance, the study utilizes in silico methodologies to explore natural compound libraries for effective alternatives.

The project applies molecular docking, ADMET profiling, Density Functional Theory (DFT), and Molecular Dynamics (MD) simulations (via YASARA) to predict and validate the binding affinities, pharmacokinetic properties, and dynamic behaviour of promising EZH2 inhibitors. The findings are expected to contribute to novel drug development strategies against melanoma, especially in drug-resistant cases.

🧪 Skills Applied:

• Molecular Docking

• ADMET Prediction

• Molecular Dynamics Simulation using YASARA

• Density Functional Theory (DFT)

• In Silico Drug Design

• Computational Biology & Pharmacoinformatics

📚 Publication (In Progress): Manuscript submitted to Scientific Reports and currently under peer review.  

Nest-generation mRNA vaccine design against Yellow fever Virus (YFV) using reverse vaccinology and immuno-informatics 

📅 Duration: June 2025 – November 2025
🏢 Affiliation: BioLab Bangladesh, Bioinformatics Laboratory
👨‍🔬 Collaborator: Ahmad Abdullah Mahdeen, Department of Microbiology, Notre Dame University Bangladesh, and Noimul Hasan Siddiquee, Department of Microbiology, Noakhali Science and Technology University.

📝 Project Synopsis:

As the project lead on this immunoinformatics-based vaccine design project, we targeted the Yellow Fever virus’s Envelope (E) and Non-Structural Protein 1 (NS1) across multiple global strains. The workflow involved retrieving protein sequences from NCBI/UniProt, predicting CTL, HTL, and B-cell epitopes using IEDB tools, and screening them for antigenicity, allergenicity, and toxicity. Selected epitopes were assembled into a multi-epitope vaccine construct with suitable linkers and adjuvants. Physicochemical properties and solubility were analyzed via ProtParam and SOLpro, followed by secondary and tertiary structure modeling, refinement, and validation. Disulfide engineering was applied to enhance stability. The construct was docked with immune receptors (TLR-2, TLR-4) using ClusPro, with binding affinities assessed via MM-GBSA. A 200ns molecular dynamics simulation evaluated RMSD, RMSF, SASA, radius of gyration, and hydrogen bonding stability using the Desmond software suite. The sequence was codon-optimized, and immune simulations using C-ImmSim predicted strong B- and T-cell responses, indicating the potential efficacy of the vaccine. 

🧪 Skills Applied:

• Immunoinformatics & Epitope Prediction

• Protein Structure Modeling & Refinement

• Molecular Docking & MM-GBSA Binding Energy Calculations

• Molecular Dynamics Simulation (Desmond)

• Disulfide Engineering & Stability Analysis

• Codon Optimization & In Silico Cloning

• In Silico Immune Response Simulation

📚 Publication (In Progress): Manuscript submitted to Immunologic Research.