Mohammad Asif Khan
Visiting Professor (Former)
Prof. Khan is a computational biologist and bioinformatics expert whose work integrates large-scale biological data, molecular modeling, and machine learning to address complex challenges in pathogen biology, immune system dynamics, vaccine and drug development, and biomarker discovery. Trained entirely at the National University of Singapore (BSc, MSc, PhD), he has developed novel bioinformatics tools, databases, and methodologies, holds three granted patents, and has authored over 75 scientific publications. He established Türkiye's first dedicated Data Science Center and high-performance computational server for biological and biomedical research at Bezmialem University through a prestigious leadership grant.
In addition to his research achievements, Dr. Khan is deeply committed to education, mentoring, and scientific service, contributing as a reviewer and as an organizer of numerous national and international bioinformatics events.
Research Area: Bioinformatics, Data Science
Field of expertise
ViralInformatics, ChemInformatics-based drug design, Disease Biomarker Informatics, Platform development for biology, VaccineInformatics, ImmunoInformatics, ClinicalInformatics, Data warehousingf or biology
Ongoing work
Dr. Khan's current research focuses on leveraging computational and bioinformatics tools to uncover insights across multiple areas of biology:
Data Warehousing for Biology: Designing systems to organize, integrate, and analyze large and diverse biological datasets.
VaccineInformatics: Identifying and prioritizing candidate vaccine targets from pathogen genome data.
ImmunoInformatics: Studying immune receptor interactions (HLA, PRRs, TCRs) with pathogen ligands to understand host defense mechanisms.
VenomInformatics: Creating curated venom data repositories and applying computational tools to study toxin structure and function.
ChemInformatics-Based Drug Design: Developing computational pipelines for identifying inhibitors targeting disease-relevant proteins.
Disease Biomarker Informatics: Using machine learning to classify gene expression profiles and reveal biomarkers that influence disease outcomes.
Specialties: Biological data warehousing, molecular evolution, gene/protein structure–function modeling, immunomics, vaccine design, computer-aided drug discovery, genome annotation, and venom biology.
About the team
The khan lab consists of the following members:
Elif Naz Bingol
Achievements as a team
At the Khan Lab, we have developed a comprehensive suite of advanced bioinformatics tools to decode viral diversity and guide diagnostics, therapeutics, and vaccine design. The ViVA (Viral Variome Analyser) ecosystem serves as the centerpiece of our research, integrating modules such as SEVANT for visualizing viral protein sequence diversity, DiMA for quantitative motif analysis, MoSwA for detecting motif switches, and CROSTA for cross-species transmissibility studies. Our lab also created target-focused tools including IDTA for identifying druggable sites, ViTA for vaccine target discovery, and DiTA for diagnostics development. The ViM (ViVA Integration Module) provides a seamless web-based interface to access all modules. Additional tools from the lab, such as UNIQmin, Kmerslicer, and g-FLUA2H, further enable researchers to study pathogen diversity and monitor zoonotic and human viral dynamics. Together, these innovations exemplify the Khan Lab's commitment to leveraging computational biology for actionable insights into viral evolution and infectious disease control.
Projects
The development of a Viral Variome Analyzer (ViVA) with application to surveillance, diagnostics, drug design and vaccine target discovery.
Novel Coronavirus (nCoV): Acute Respiratory Infection Clinical Characterisation Data Tool Design.
Investigating possible inhibitors against Factor H in relation to glioblastoma.
Large-scale analysis of B-cell epitopes on Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein – implications for cross-reactivity of neutralizing antibodies.
Mapping and characterising sequences shared between coronaviruses and human proteomes: structural, functional and immunological implications.
Lab alumni
- Rashid Mukaila
- Faruk Üstünel
- Omer Aysar
- Esra Büşra Işık
- Ebru Sarsılmaz
- Adnaane Bawa
- Ayesha Fatima
- Emre Herdan
- Esin Özkan
- Eyyub Selim Ünlü
- Gizem Yılmaz
- Gökçen Şahin
- Hasiba Karimi
- Hilal Hekimoğlu
- Levent Faruk Soysal
- Li Chuin Chong
- Melike Karakaya
- Melda Mermer
- Moosa Farhan
- Muhammad Farhan Sjaugi
- Shan Tharanga
- Muhammed Miran Öncel
- Muhammet Çelik
- Omer Avşar
- Pendy Tok
- Rumeysa Meriç
- Shazia Naqshbandi
- Zarife Aslan
Completed grants/projects
2020-2023 International Fellowship for Outstanding Researchers 118C314 Sponsor: Scientific and Technological Research Council of Turkey (TUBITAK)
2020-2023 Title: Advanced Studies Institutes (ASI); International Research Experiences for Students (IRES) National Science Foundation (NSF), USA
2022-2023 Title: Cross-Disciplinary Training for Future Computational Biologists in Asia. Sponsor: Asi@Connect (http://www.tein.asia). Partners: USA, UK, Malaysia, Turkey, Thailand, Indonesia, Bangladesh, India, and Singapore.
2022-2023 Title: Building a high bandwidth distributed High Performance Computing. Sponsor: Asi@Connect (http://www.tein.asia). Partners: Australia, Korea, Pakistan, Malaysia and Turkey
2021 Title: Educational workflow on computer aided drug design: investigating possible inhibitors against Factor H as a case study. Sponsor: National Supercomputing Centre (NSCC), Singapore
2019-2020 Cheminformatics-based, high-throughput virtual screening for candidate inhibitory peptides against the spike protein of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With Dr Serdar Uysal
2019-2020 Bilgisayar yardımlı geliştirilen Fakt.r H inhibit.rlerinin glioblastoma hayvan modellerinin tedavisinde kullanımının değerlendirilmesi. Sponsor: Bezmialem Vakif University with Rümeyza Kazancıoğlu
Selected Publications
Işık EB, Brazas MD, Schwartz R, Gaeta B, Palagi PM, van Gelder CWG, Suravajhala P, Singh H, Morgan SL, Zahroh H, Ling M, Satagopam VP, McGrath A, Nakai K, Tan TW, Gao G, Mulder N, Schönbach C, Zheng Y, De Las Rivas J, Khan AM. Grand challenges in bioinformatics education and training. Nat Biotechnol. 2023 Aug;41(8):1171-1174. doi: 10.1038/s41587-023-01891-9. [Public link: https://rdcu.be/djhh8]. [*Corresponding author].
Raman HS, Tan S, August JT, Khan AM*. Dynamics of Influenza A virus (H5N1) protein sequence diversity. PeerJ. 2020, e7954 [*Corresponding author].
Suwinski P, Poh YM, Ling MHT, Ong CK, Khan AM* and Ong HS*. Advancing personalized medicine through the application of whole exome sequencing and big data analytics. Frontiers in Genetics. 2019, 10, 49. [*Co-Corresponding author].
Khan AM*, Hu Y, Miotto O, Thevasagayam NM, Sukumaran R, Raman HSA, Brusic V, Tan TW and August, JT. Analysis of Viral Diversity for Vaccine Target Discovery. BMC Med. Genomics. 2017. 10(4), 78. [*Corresponding author] [Award = Best Paper, Gold @ International Conference on Bioinformatics (InCoB) 2017]
Lim WC and Khan AM*. Mapping HLA-A2, -A3, and -B7 supertype-restricted T-cell epitopes in ebolavirus proteome. BMC Genomics. 2018, 19 (1), 42. [*Corresponding author; Award = Best Paper, Gold @ International Conference on Bioinformatics (InCoB) 2017]
