The identification of binding pockets is an essential component of the drug discovery process. We have developed a computational pocket detection method that combines ideas from fragment-based drug discovery with molecular dynamics (MD) simulations to account for the effects of protein flexibility and solvation. In this method, which we call ligand-mapping molecular dynamics (LMMD), small organic molecules called fragments are incorporated into the protein’s solvent box to probe for binding pockets during MD simulations (Figure 1). LMMD was first used with benzene probes to design a ligand to target a recalcitrant cryptic pocket (Tan et al., 2012). The method has since been employed in various studies to interrogate a range of proteins, providing crucial insights that have led to the identification of druggable cryptic pockets and discovery of novel protein–protein interaction inhibitors, thus underlining its usefulness for drug discovery.
Through the incorporation of multiple types of fragment probes with different functional groups (Figure 2), LMMD has now been extended to the simultaneous identification of hydrophobic, polar, charged, and cryptic binding sites (Tan and Verma, 2021). We are currently developing an arsenal of LMMD-based approaches, which are expected to improve the accuracy of binding site prediction, provide new insights into the structure and dynamics of drug targets, and guide the design of therapeutics and chemical tools for biological applications. We are keen to apply our methods to chemical biology and drug discovery projects and welcome collaborations with groups working in these areas.
Structure-based design of next-generation therapeutics
Aptamers are single-stranded DNA or RNA molecules with unique tertiary structures that enable them to bind to a variety of target molecules with high affinity and specificity. They hold great potential as therapeutics because of their high binding specificity and low immunogenicity. So far, one aptamer has been approved for therapeutic application while several others have advanced to clinical trials. We are currently working with researchers in A*STAR to apply structure-based computational approaches, such as LMMD and docking, to the rational design of therapeutic aptamers.
Furthermore, we are involved in the structure-based design of a certain class of therapeutics called constrained or stapled peptides. The formation of a covalent "staple" between two appropriately positioned amino acid residues in the peptide constrains its conformation, which helps to enhance binding potency, protease resistance, and to a certain degree, cell permeability. Our computational modelling efforts have led to the development of the first bioactive stapled non-helical peptides with collaborators in the University of Cambridge (Figure 3). These studies show that peptides can also be stabilised in extended conformations by stapling, thus opening up a whole new spectrum of protein–protein interactions for therapeutic targeting.
Molecular mechanisms of diseases
We collaborate closely with IMCB's Dr Adrian Teo to study the molecular mechanisms of diabetes-causing mutations. We have specifically studied mutations that cause two forms of monogenic diabetes, neonatal diabetes mellitus and maturity onset diabetes of the young (MODY). These mutations occur in insulin and HNF1A respectively. Through the use of MD simulations, the detrimental effect of the mutations on protein structure and function were elucidated. We plan to extend our study to type 2 diabetes (T2D)-linked coding variants, which will hopefully help to shed light on the complex molecular pathogenesis of T2D, identify new drug targets for personalised therapy, and aid the development of precision medicine approaches with improved clinical outcomes.
Dr Tan Yaw Sing graduated with a B.Sc. (Hons) from the National University of Singapore in 2009, with a double major in Chemistry and Life Sciences. Funded by the A*STAR Graduate Scholarship, he proceeded to pursue his postgraduate studies at the University of Cambridge, where he obtained his PhD in Chemistry in 2014. In the same year, he joined Chandra Verma's group at the Bioinformatics Institute (BII) as a postdoctoral research fellow. He now leads his own group in BII after he was promoted to the position of Assistant Principal Investigator in April 2021.
Dr Tan's research interests span the diverse fields of computational chemical biology, computer-aided drug design, and computational structural biology. He hopes to transform current strategies for drug discovery and chemical biology by improving the accuracy of binding site prediction, providing new insights into the structure and dynamics of drug targets, and guiding the rational design of therapeutics and chemical tools.
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