Previous Projects | Abdullah Al Marzan

Previous Projects

1. SARS-CoV-2 Mutational and Immunoinformatic Profiling

The identification of deleterious mutations in different variants of SARS-CoV-2 and their roles in the morbidity of COVID-19 patients has yet to be thoroughly investigated. To unravel the spectrum of mutations and their effects within SARS-CoV-2 genomes, we analyzed 5,724 complete genomes from deceased COVID-19 patients sourced from the GISAID database. This analysis was conducted using the Nextstrain platform, applying a generalized time-reversible model for evolutionary phylogeny. These genomes were compared to the reference strain (hCoV-19/Wuhan/WIV04/2019) using MAFFT v7.470. Our findings revealed that SARS-CoV-2 genomes from deceased individuals belonged to 21 Nextstrain clades, with clade 20I (Alpha variant) being the most predominant, followed by clade 20H (Beta variant) and clade 20J (Gamma variant). The majority of SARS-CoV-2 genomes from deceased patients (33.4%) were sequenced in North America, while the lowest percentage (0.98%) came from Africa.

Role: Research Assistant
Institute: Department of Biochemistry and Molecular Biology, SUST
Duration: April 2021 – January 2023

This project focused on mutation profiling and immune landscape mapping of SARS-CoV-2 variants, including Delta and Omicron. Using a dataset of over 200,000 GISAID sequences, I applied tools like NextClade, PredictSNP, and DynaMut to identify deleterious mutations in the spike protein, including those associated with immune escape and protein destabilization.
Machine learning models were trained to classify high-risk mutations, integrating immunoinformatics tools (IEDB, NetCTLpan) to assess T-cell and B-cell epitope variability. This resulted in several impactful publications:
Scientific Reports (2023): Analysis of shared mutations across deceased patients globally
Frontiers in Pharmacology (2023): Variant-specific mutation mapping for vaccine strategy
Advances in Biomarker Sciences and Technology (2023): Multi-omics biomarker analysis in cancer–diabetes overlap
Frontiers in Immunology (2022): Natural immune enhancers during COVID-19
I performed data preprocessing, statistical modeling in R and SPSS, and manuscript figures using Adobe Illustrator.

2. Wastewater-Based SARS-CoV-2 Surveillance & Environmental Virology

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes.

Role: Research Assistant
Institute: Department of Civil & Environmental Engineering, SUST and COVID-19 Testing Lab at NSTU
Duration: November 2020 – June 2021

This interdisciplinary project applied environmental sampling and molecular diagnostics to detect SARS-CoV-2 RNA in wastewater from communities with limited sanitation infrastructure. As part of the project:
I collected and processed wastewater samples.
Extracted RNA and performed qRT-PCR.
Developed a monitoring framework using R for spatial and temporal trends.
This work led to the following publications:
Environmental Pollution (2022): Surveillance protocol for low-resource settings
Current Opinion in Environmental Science & Health (2023): Integration of wastewater data with clinical diagnostics
medRxiv (2021): Pilot studies on decentralized wastewater monitoring
These efforts contributed directly to early warning models for COVID-19 outbreaks and showcased my ability to work across microbiology, public health, and data science domains.

3. Molecular Docking and MD Simulation for SARS-CoV-2 RBD Inhibitors

Compared to the Wuhan-Hu-1 reference strain NC 045512.2, genome-wide annotations showed 16,954 mutations in the SARS-CoV-2 genome. We determined that the Omicron variant had 6,307 mutations (retrieved sequence:1947), including 67.8% unique mutations, more than any other variant evaluated in this study. The spike protein of the Omicron variant harboured 876 mutations, including 443 deleterious mutations. Among these deleterious mutations, 187 were common and 256 were unique non-synonymous mutations. In contrast, after analysing 1,884 sequences of the Delta variant, we discovered 4,468 mutations, of which 66% were unique, and not previously reported in other variants. Mutations affecting spike proteins are mostly found in RBD regions for Omicron, whereas most of the Delta variant mutations drawn to focus on amino acid regions ranging from 911 to 924 in the context of epitope prediction (B cell & T cell) and mutational stability impact analysis protruding that Omicron is more flexible.

Role: Research Assistant (Remote)
Institute: Red-Green Research Center
Duration: February 2020 – December 2021

Using structure-based drug discovery techniques, this project aimed to design small molecules and RNA aptamers that could bind to the SARS-CoV-2 receptor-binding domain (RBD) with high affinity. I used YASARA, GROMACS, and Desmond for MD simulations and docking validation.
Curated ligand libraries.
Modeled RBD-ligand interactions.
Ran Linux-based simulation pipelines for conformational stability.

4. Microbial Bioremediation Using Cyanobacteria

Microcoleus sp. is a versatile microorganism widely available in the environment and easily culturable. Hence, there is a progressing demand for wastewater treatment using this novel biosorption medium. The design of such a treatment method may be defined as an optimisation problem of algal dose and hydraulic retention time for attaining an adequate removal efficiency of heavy metals (Cr6+, Ni2+, and Zn2+) and nutrients (PO43- and NO3-). Batch experiments on synthetic wastewater were conducted for algal doses varying from 0.5 to 25 g/L and hydraulic retention times from 1 to 7 days. Significant removal efficiencies of greater than 90% were observed for the heavy metals, 75% of PO43- removal, and no removal of NO3- was found under continuous daylight. The single-factor ANOVA test confirms the statistical significance of the varying parameters on the pollutant removal efficiency. Langmuir and Freundlich's adsorption isotherms indicate satisfactory adsorption of the contaminants.

Role: Research Assistant
Institute: Department of Civil & Environmental Engineering, SUST
Duration: November 2020 – March 2021

In this project, I cultivated blue-green algae (Microcoleus sp.) for use in biosorptive removal of heavy metals from industrial wastewater. I optimized culture conditions and conducted batch absorption assays under varying pH and retention times.
Analytical methods such as AAS and spectrophotometry were employed to calculate removal efficiencies. Data was modeled using Freundlich and Langmuir isotherms. This work contributed to:
AQUA – Water Infrastructure, Ecosystems and Society (2023): Novel application of Microcoleus in bioremediation

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