Obesity has become one of the most stubborn public health challenges of the modern era, and the search for drugs that can blunt the body’s absorption of dietary fat remains a major focus of pharmaceutical research. Now, a team of Indonesian scientists has taken a significant step toward making that search faster and more reliable. In a study published in the journal Molecular Diversity, researchers led by Ilma Fauziah Ma’ruf of the Research Center for Pharmaceutical Ingredients and Traditional Medicine at the National Research and Innovation Agency (BRIN) have validated a bacterial enzyme, known as lipase ITB 2.1, as a robust structural and functional model for screening potential anti-obesity drugs. The work combines evolutionary analysis, computational modelling, molecular docking, long-timescale molecular dynamics simulations and laboratory enzyme assays into a single, carefully cross-checked pipeline.
Lipases are the enzymes responsible for breaking down dietary triglycerides into absorbable fatty acids and monoacylglycerides. Because of this central role in fat digestion, they have long been recognised as prime therapeutic targets for obesity and related metabolic disorders. The best-known anti-obesity drug on the market, orlistat, works precisely by inhibiting pancreatic lipase in the gut, preventing a portion of dietary fat from ever entering the bloodstream. However, screening new lipase inhibitors requires a dependable enzyme model that behaves predictably, is easy to produce and accurately reflects the biology of human digestive lipases. Lipase ITB 2.1, originally isolated from an Indonesian hot spring bacterium, may now fill that role.
The team began with a deep evolutionary dive into lipase sequences drawn from bacterial, fungal and mammalian sources. Multiple sequence alignment revealed that the catalytic serine residue, which sits at the so-called nucleophilic elbow of the enzyme and performs the decisive chemical step of fat cleavage, is strongly conserved across all three domains of life. This conservation is the molecular linchpin that makes a bacterial enzyme a meaningful proxy for its human counterparts. Interestingly, the analysis also uncovered rare substitutions in the canonical pentapeptide motif surrounding the catalytic serine. While most lipases carry the characteristic GXSXG motif, certain Bacillus and Geobacillus species display AXSXG variants, hinting at adaptive structural variation among thermostable bacterial enzymes.
Phylogenetic and distance-based analyses then mapped out the evolutionary relationships among these enzymes. The resulting trees showed clear clustering by lineage, yet they also revealed unexpected overlaps between mammalian, fungal and bacterial lipases that the authors interpret as signs of functional convergence. In other words, evolution appears to have arrived at similar catalytic solutions multiple times through different genetic routes. For drug developers, this convergence is good news: a compound designed to block the conserved active site of one lipase has a reasonable chance of affecting others, including the pancreatic lipase that matters most for obesity therapy.
Structural modelling brought the sequence data into three dimensions. Superimposition of predicted and experimentally determined structures confirmed that all the analysed enzymes share the classic alpha/beta-hydrolase fold, the architectural scaffold that underpins virtually all lipase catalysis. But the models also highlighted one of the most important and least appreciated features of lipase biology: the lid domain. This mobile structural element swings open and closed over the active site, regulating access for substrates and inhibitors alike. The researchers found that these lid-dependent conformational changes are preserved across the enzyme family, and that the position of the lid dramatically alters what a drug molecule can actually ‘see’ when it approaches the binding pocket.
That insight proved decisive in the docking experiments. Using AutoDock Vina 1.2.0, the team docked orlistat into both open-lid and closed-lid conformations of several lipases, including lipase ITB 2.1, human pancreatic lipase and monoacylglycerol lipase. The results were striking: orlistat bound far more strongly to the open-lid state than to the closed state across the board, confirming that active-site accessibility governed by lid dynamics is a critical determinant of inhibitor potency. Among all the enzymes tested, lipase ITB 2.1 exhibited the most favourable binding affinity for orlistat, a result that immediately flagged the bacterial enzyme as a sensitive and responsive screening target.
Docking alone can be misleading, however, because it captures a single frozen snapshot rather than the dynamic reality of protein-ligand interaction in a watery, thermally jostling cellular environment. To address this, the researchers ran molecular dynamics simulations of 100 nanoseconds for each enzyme-orlistat complex using the GROMACS engine with the CHARMM36 protein force field and CGenFF ligand parameters. The simulations confirmed that all the complexes were stable over the simulated timescale, with the ligand remaining lodged in its binding pocket throughout. Monoacylglycerol lipase and pancreatic lipase displayed the highest overall structural stability, but lipase ITB 2.1 maintained consistent compactness and reliable ligand retention, exactly the qualities one wants in a reproducible screening model. Analyses of root-mean-square deviation, radius of gyration and interaction fingerprints together painted a picture of a firm, durable drug-enzyme engagement.
The crucial test came in the laboratory, where computational prediction met wet-lab reality. Enzymatic inhibition assays measured how effectively orlistat shut down lipase ITB 2.1’s catalytic activity. The kinetics followed classic Michaelis-Menten behaviour, with a maximum velocity of 1428 micromoles per minute and a Michaelis constant of 81 micromoles. Most tellingly, the enzyme was completely inhibited at an orlistat concentration of just 56.25 micromoles, demonstrating exquisite sensitivity to the drug. These experimental values vindicated the docking scores and simulation stability data, closing the loop on a validation chain that stretched from raw sequence all the way to measured biochemistry. Very few candidate screening enzymes are put through such a comprehensive gauntlet before being adopted by the research community.
The implications extend well beyond one enzyme and one drug. A validated, thermally robust, structurally well-characterised lipase model gives medicinal chemists a dependable platform for virtual screening of large compound libraries, for rational drug design and for rapid experimental confirmation of computationally predicted inhibitors. Because lipase ITB 2.1 responds to orlistat with clear, quantifiable kinetics and retains a conserved catalytic architecture shared with human pancreatic lipase, candidate molecules that inhibit it are strong candidates for inhibiting the therapeutic target as well. The integrated workflow demonstrated in this study, spanning sequence analysis, AlphaFold-era structural prediction, docking, 100-nanosecond dynamics and enzymology, also offers a template that other laboratories can replicate for validating model enzymes in entirely different drug-discovery contexts.
There are, of course, caveats and next steps. Lipase ITB 2.1 is a bacterial enzyme, and while its active-site chemistry is conserved, differences in lid architecture, surface properties and physiological context mean that hits identified against it will still require confirmation against human pancreatic lipase and, ultimately, in animal and clinical models. The study’s authors also note that the open-versus-closed lid comparison underscores the need to account for protein flexibility in any screening campaign, a lesson increasingly recognised across computational drug discovery. Nevertheless, by demonstrating that a single enzyme model can pass structural, evolutionary, computational and experimental validation in one coherent framework, the Indonesian team has given the fight against obesity a new and practical weapon: a trustworthy molecular stand-in against which the next generation of fat-blocking drugs can be tested quickly, cheaply and with confidence.
Subject of Research: Structure- and function-based validation of the bacterial enzyme lipase ITB 2.1 as a model for anti-obesity drug screening
Article Title: Lipase ITB 2.1: a structure- and function-validated model for anti-obesity drug screening
Article References: Ma’ruf, I. F., Ernawati, T., Hermawan, F., Akhmaloka, A., Warganegara, F. M., Widhiastuty, M. P., Restiawaty, E., Wiraswati, H. L., Wibisana, A., Mozef, T., Hidayati, N. A., Haryati, T., & Simatupang, D. F. (2026). Lipase ITB 2.1: a structure- and function-validated model for anti-obesity drug screening. Molecular Diversity. https://doi.org/10.1007/s11030-026-11731-y
Image Credits: AI Generated
DOI: 10.1007/s11030-026-11731-y
Keywords: lipase ITB 2.1, obesity, orlistat, lipase inhibition, molecular docking, molecular dynamics, alpha/beta-hydrolase fold, lid domain, pancreatic lipase, drug screening, enzyme kinetics, Indonesia
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Daisy Hatcher. (September 12, 2026). Scientists Validate a Bacterial Lipase Model That Could Speed Up Anti-Obesity Drug Discovery. Scienmag. https://scienmag.com/scientists-validate-a-bacterial-lipase-model-that-could-speed-up-anti-obesity-drug-discovery/
Daisy Hatcher. “Scientists Validate a Bacterial Lipase Model That Could Speed Up Anti-Obesity Drug Discovery.” Scienmag, 12 September 2026, https://scienmag.com/scientists-validate-a-bacterial-lipase-model-that-could-speed-up-anti-obesity-drug-discovery/. Accessed 12 September 2026.
Daisy Hatcher. “Scientists Validate a Bacterial Lipase Model That Could Speed Up Anti-Obesity Drug Discovery.” Scienmag. September 12, 2026. https://scienmag.com/scientists-validate-a-bacterial-lipase-model-that-could-speed-up-anti-obesity-drug-discovery/
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Tags: alpha/beta-hydrolase foldanti-obesity drug discoverybacterial enzymes in pharmaceutical researchbacterial lipase enzyme validationcomputational modelling of fat digestion enzymesdrug screeningenzyme assays for metabolic disorder researchenzyme kineticshigh-throughput screening for anti-obesity drugsIndonesialid domainlipase inhibitionlipase ITB 2.1lipase ITB 2.1 structural modelmolecular dockingmolecular docking in drug screeningmolecular dynamicsmolecular dynamics simulations for enzyme analysisobesityobesity treatment targetsorlistatpancreatic lipasepancreatic lipase inhibition mechanismstraditional medicine and pharmaceutical ingredients
