Structure-based Ligand Discovery and Design

The broad goal of my lab is to develop computational methods to study protein-ligand interactions. The developed methods are used in basic research, to help understand and regulate biological processes, in particular, structure-function mechanisms of GPCRs, transporters, and kinases. Meanwhile, these methods were also employed in applied research, to help evaluate toxicity of chemicals including food ingredients, discover new drug leads, and engineer industrial enzymes.

COMPUTATIONAL METHOD DEVELOPMENT

We are developing computational methods to effectively and accurately model protein-ligand interactions. Two examples are given below on chemical toxicity prediction and enzyme engineering.

Chemical Toxicity Prediction

We constructed an in-silico platform for chemical toxicity prediction (Figure 1). Using ToxCast data of 12 nuclear receptors, we systematically analysed how multiple crystal protein structures can be used in screening protocols to distinguish actives from inactives. The methods used for data fusion are consensus docking scores from multiple conformations at a single functional state (agonist-bound or antagonist-bound), multiple functional states, and multiple pockets (orthosteric and allosteric sites). The results suggested that such a consensus approach could have a greater discriminating power between active and inactive molecules with respect to the standard single-structure based approach. Currently we are improving this structure-based approach with the aid of AI.

Figure 1. The in-silico platform for chemical toxicity prediction, applied to nuclear receptors.

Structure-AI method for Enzyme Engineering

We are collaborating with BII, ISCE2 and IHPC colleagues, developing “an Integrative Computational/Experimental Workflow for Enzyme Development”, to improve the speed of enzyme engineering and to build a panel of enzymes with activity against industrially relevant panel of substrates. My lab has developed a computational method that integrates machine learning and deep learning models with conventional bioinformatics for enzyme engineering. This new method has shown > 90% accuracy when it was used to predict better mutants of naturally occurring galactose oxidase (GAOx) in terms of industry-related substrate activity, and to predict new substrates for selected GAOx out of industry-related chemical databases, with improved speed of evolution and faster biocatalyst development that are critical for manufacturing processes and of high industrial importance (Figure 2). In collaboration with Dr. Wen Shan Yew’s lab at NUS, we also successfully applied the computational method to the engineering of NphB produces the cannabinoid cannabigerolic acid (CBGA), the key precursor to many cannabinoids.

Figure 2.  The computational method that integrates machine learning models with conventional bioinformatics for enzyme engineering, applied to galactose oxidase.

GPCR & Kinase: Structure-function Mechanisms

GPCRs play important roles in cell signaling pathways. Their dysfunction causes many human developmental and metabolic disorders, as well as certain cancers. In collaboration with Dr. Cheng Zhang from the University of Pittsburgh, we contributed to the determination of crystal structures of GPCRs including a lipid GPCR CRTH2 as the receptor for prostaglandin D2, the C5a receptor (C5aR) that can induce strong inflammatory events in response to the anaphylatoxin C5a peptide, and N-formyl peptide receptor 2 (FPR2) that is a high affinity receptor for the arachidonic acid metabolites. We further studied C5aR, CRTH2, and GPR84 with MD simulations, revealing a distinctive lipid recognition mechanism for CRTH2, a cooperative two-site binding mechanism for C5aR, and the molecular basis for the high selectivity of GPR84 for MCFAs as well as its potential routes of ligand binding and dissociation. Currently we are working towards the identification of novel orthosteric and allosteric modulators and better understanding of structure-function mechanisms, for other lipid GPCRs (Figure 3).

• Supekar S, Tay DWP, Yeo WL, Tam KWE, Koo YS, See JY, Miyajima JMT, Maurer-Stroh S, Ang EL, Lim YH*, Fan H*. A Machine Learning-Guided Approach to Navigate the Substrate Activity Scope of Galactose Oxidase: Application in the Conversion of Pharmaceutically Relevant Bulky Secondary Alcohols. ACS Catalysis. 2024. 14, 23, 17233–17243.


• Lim TYM, Jaladanki CK, Wong YH, Yogarajah T*, Fan H*, Chu JJH*. Tanomastat exerts multi-targeted inhibitory effects on viral capsid dissociation and RNA replication in human enteroviruses. EBioMedicine. . 2024, 107, 105277.


• Zhang X, Wang Y, Supekar S, Cao X, Zhou J, Dang J, Chen S, Jenkins L, Marsango S, Li X, Liu G, Milligan G*, Feng M*, Fan H*, Gong W*, Zhang C*. Pro-phagocytic function and structural basis of GPR84 signaling. Nature Communications. 2023, 14, 5706.


• Cheng L, Deepak RNVK, Wang G, Meng Z, Tao L, Xie M, Chi W, Zhang Y, Yang M, Liao Y, Chen R, Liang Y, Zhang J, Huang Y, Wang W, Guo Z, Wang Y, Lin J, Fan H*, Chen L*. Hepatic mitochondrial NAD+ transporter SLC25A47 activates AMPKα mediating lipid metabolism and tumorigenesis. Hepatology. 2023, 78, 1828-1842.


• Lim KJH, Hartono YD, Xue B, Kho MB, Fan H*, Yew WS*. Structure-Guided Engineering of Prenyltransferase NphB for High-Yield and Regioselective Cannabinoid Production. ACS Catalysis. 2022, 12, 4628–4639.


• Liu H, Deepak RNVK, Shiriaeva A, Gati C, Batyuk A, Hu H, Weierstall U, Liu W, Wang L, Cherezov V*, Fan H*, Zhang C*. Molecular basis for lipid recognition by the prostaglandin D2 receptor CRTH2. PNAS 2021, 118(32):e2102813118.


• Yap J, Deepak RNVK, Tian Z, Ng WH, Goh KC, Foo A, Tee ZH, Mohanam MP, Sim YR, Degirmenci U, Lam P, Chen ZZ, Fan H*, Hu J*. The Stability of R-spine Defines RAF Inhibitor Resistance: A Comprehensive Analysis of Oncogenic BRAF Mutants with In-frame Insertion of αC-β4 loop. Science Advances 2021, 7:eabg0390.


• Low G, Du WN, Gocha T, Oguz G, Zhang X, Chen MW, Masirevic S, Yim DGR, Tan IBH, Ramasamy A, Fan H*, DasGupta R*. Molecular docking-aided identification of small molecule inhibitors targeting β-catenin-TCF4 interaction. iScience 2021, 24(6):102544.


• Goh JJN, Behn J, Chong CS, Zhong G, Maurer-Stroh S, Fan H*, Loo LH*. Structure-based virtual screening of CYP1A1 inhibitors: towards rapid tier-one assessment of potential developmental toxicants. Archives of Toxicology 2021, 95(9):3031-3048.


• Jaladanki CK, He Y, Zhao LN, Maurer-Stroh S, Loo LH, Song HW*, Fan H*. Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids.  Archives of Toxicology 2021, 95(1):355-374.


• Dumitru AC, Deepak RNVK, Liu H, Koehler M, Zhang C*, Fan H*, Alsteens D*. Submolecular probing of the complement C5a receptor–ligand binding reveals a cooperative two-site binding mechanism. Communications Biology 2020, 3(1):786.


• Xiao QP, Wang L, Supekar S, She T, Liu H, Ye F, Huang JZ, Fan H*, Wei ZY*, Zhang C*. Structure of human steroid 5α-reductase 2 with the anti-androgen drug finasteride. Nature Communications 2020, 11(1):5430.

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