Duangrudee Tanramluk

Asst. Prof. Duangrudee Tanramluk, Ph.D.


Dr. Duangrudee Tanramluk
Education: Ph.D. (Biochemistry), University of Cambridge, 2010
Email: duangrudee.tan at mahidol.ac.th
Phone: 02-441-9003-7 Ext. 1211
Research Interests: Drug Design, Bioinformatics
Homepage: duangrudee.com

Academic Tree: Computational Biology


            My research experiences, ranging from quantum chemical calculations, protein X-ray crystallography, and software development, have shaped my vision and expertise on how to design tools to solve small molecule drug discovery problems. I choose scientific problems that have big impact on our scientific knowledge and the entire society. To fulfill this objective, I have identified 2 major protein groups, which are the kinases and DHFR. Kinases have been implicated in a lot of signaling pathways and human diseases, such as cancer and inflammation. DHFR can be the target protein for several antimicrobial drugs due to its high inhibitor selectivity nature.

          Our group have come up with algorithms that allow for binding affinity prediction to facilitate rational drug design from protein structure coordinates. A lot of drug researchers relies on combinatorial and high-throughput screening of chemical compounds instead of working towards chemical properties in a rational and interpretable way and leads to lead optimization failure. Based on trends of large amount of ligand interactomic distances, shape, and charge complementary in the homologous pockets from structure ensemble, we can now dissect the binding affinity in a human interpretable way for both aforementioned enzyme groups.

          My key findings are around the Manoraa.org systems to assist drug discovery by linking ligand to target proteins, baseline expression, SNPs and pathways as featured in Structure (Tanramluk, et al., 2022) and Nucleic Acids Research (Tanramluk, et al., 2016). By linking these information in this Manoraa ligand design hub, researchers can perform in silico target discovery and ligand design before doing wet lab experiments. Our group have also developed MANORAA algorithms to analyze features obtained from a set of homologous protein crystal structures.  Therefore, we can generate influential distance equations and  molecular anchors to guide inhibitor design. We can compare the shape of the pocket to observe position specific interactions and to display chemical interactions in the pocket of the proteins (Tanramluk, et al., 2009). Several visualization techniques are employed to create three-dimensional pictures from various perspectives. The effects from parameters therein, such as the entities that usually participate in hydrogen bond formation, the parts of the pocket that expand or contract upon binding to inhibitors, can be displayed to guide scientists while formulating an inhibitor design strategy.

          Taken together, I gear toward novel computational methods to make drug discovery faster, cheaper, and more effective.

Selected Publications

  1. Tanramluk D*, Pakotiprapha D, Phoochaijaroen S, Chantravisut P, Thampradid S, Vanichtanankul J, Narupiyakul L, Akavipat R, Yuvaniyama J. MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances. Structure. 2022;30(1):181-9.e5.
  2. Tanramluk D*, Narupiyakul L, Akavipat R, Gong S, Charoensawan V. MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein-ligand fragment interaction, pathways and SNPs. Nucleic Acids Res. 2016;44(W1):W514-21.
  3. Wikan N, Khongwichit S, Phuklia W, Ubol S, Thonsakulprasert T, Thannagith M, Tanramluk, D, Paemanee A, Kittisenachai S, Roytrakul S, Smith, DR. Comprehensive proteomic analysis of white blood cells from chikungunya fever patients of different severities. J Transl Med. 2014;12:96.
  4. Tanramluk D*, Akavipat R, Charoensawan V. Toward mobile 3D visualization for structural biologists. Mol Biosyst. 2013;9(12):2956-60.
  5. Tanramluk D, Schreyer A, Pitt WR, Blundell TL. On the origins of enzyme inhibitor selectivity and promiscuity: a case study of protein kinase binding to staurosporine. Chem Biol Drug Des. 2009;74(1):16-24.
  6. Gong S, Worth CL, Bickerton GR, Lee S, Tanramluk D, Blundell TL. Structural and functional restraints in the evolution of protein families and superfamilies. Biochem Soc Trans. 2009;37(Pt 4):727-33.
  7. Lee S, Brown A, Pitt WR, Higueruelo AP, Gong S, Bickerton GR, Schreyer A, Tanramluk D, Baylay A, Blundell TL. Structural interactomics: informatics approaches to aid the interpretation of genetic variation and the development of novel therapeutics. Mol Biosyst. 2009;5(12):1456-72.


Laboratory Activity 

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SDG 3: Good Health and well being

Drug Design Server research

SDG 4: Quality Education

MANORAA taught in Metaverse

SDG 12: Resposible Consumption and Production

Computational Drug Design is Green chemistry, which can reduce chemical waste from high-throughput drug candidate synthesis.

SDG 17: Partnership for the Goal

Collaboration between MB and Department of Biochemistry and Department of Engineering