Poulami Chatterjee
Physical Science Research Scientist, Chemistry
All Publications
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Prediction and characterization of lipid-interacting proteins.
Methods in enzymology
2026; 727: 253-289
Abstract
Lipids are essential to all life forms. These molecules serve diverse purposes that range from cell membrane formation to energy storage and inter-cellular signaling. Lipids can be natively synthesized or sourced from the environment, often through the action of proteins engaging with specific lipid molecules. Characterizing lipid-interacting proteins is a key frontier in therapeutic science, as dysfunction in lipid metabolism is implicated in a range of human diseases. A substantial bottleneck that precludes the identification and characterization of lipid-interacting proteins pertains to the nature of the lipid substrates: they are not genetically encoded, their hydrophobic nature results in non-specific interactions, they exist in complex cellular environments, and they are structurally diverse. Regardless, the identification, characterization, and specific targeting of proteins that maintain proper lipid homeostasis is important for efforts to restore dysregulated metabolism. In this chapter, we outline bioinformatic and experimental approaches employed by our research group and others to study lipids and the proteins that directly bind them. The chapter covers methods for proteome-wide computational screening to reveal lipid binding proteins, characterization of total lipid composition in mammalian and bacterial cells, and the use of analytical and biophysical methods to study target protein-lipid interactions.
View details for DOI 10.1016/bs.mie.2025.11.013
View details for PubMedID 41765594
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A cofactor-promiscuous HMGR from the Lyme disease pathogen illuminates diversity in bacterial isoprenoid biosynthesis.
bioRxiv : the preprint server for biology
2026
Abstract
The Lyme disease pathogen Borrelia burgdorferi contains a highly reduced genome lacking many primary metabolic pathways. However, B. burgdorferi retains the mevalonate pathway that synthesizes isopentenyl pyrophosphate (IPP), the precursor to the peptidoglycan carrier lipid. While the mevalonate pathway and the enzyme that catalyzes its rate-limiting step (3-hydroxy-3-methyl glutaryl coenzyme A reductase, HMGR) are well studied in vertebrates, little is known about the pathway in B. burgdorferi and many pathogenic bacteria. In this work, we reveal that HMGR is a critical metabolic enzyme in B. burgdorferi. We demonstrate that loss of HMGR causes morphological defects and muted de novo synthesis of peptidoglycan; these defects are ameliorated by exogenous mevalonate and IPP. Biochemical characterization unveiled the HMGR as a highly unusual cofactor-promiscuous oxidoreductase that functions with both nicotinamide cofactors. Bioinformatics and biochemical characterization uncovered examples of similarly promiscuous HMGRs and revealed a previously unrecognized evolutionary link to cofactor choice. Moreover, structures of the enzyme reveal a highly divergent active site architecture. Together, these findings firmly establish HMGR as an opportunity target for the development of antibacterials for a diderm pathogen while highlighting cofactor promiscuity as an evolutionary acquired feature in HMGRs.
View details for DOI 10.64898/2026.07.06.735745
View details for PubMedID 42465499
View details for PubMedCentralID PMC13370337
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A lipid compendium of a metabolically compromised bacterium provides insights into lipid acquisition, biosynthesis, and metabolism.
bioRxiv : the preprint server for biology
2026
Abstract
The Lyme disease agent Borrelia burgdorferi belongs to a class of metabolically compromised bacteria that cannot survive without host-derived lipids. Survival of the agent in tick and vertebrate hosts requires substantial nutrient acquisition and potential cell envelope remodeling. While prior studies identified cholesterol, cholesterol glycolipids, and phosphatidylcholines as membrane lipids in B. burgdorferi, the identity of many other membrane lipids, their origin, and their physiological relevance remain unknown. Here, we used a suite of untargeted and targeted high-resolution mass spectrometry methods to reveal a complex lipid profile of the pathogen and to identify the origin of its lipids. The analysis detected more than 500 lipids in B. burgdorferi, the majority of which are sourced from the environment. However, the bacterium selectively accumulates certain lipids while excluding others, suggesting discriminatory uptake. These include cholesteryl esters and triglycerides that are organized in foci within the pathogen. Intriguingly, the pathogen also synthesizes predominantly eukaryotic lipids such as the lysosomal bis(monoacylglycerol)phosphate and the plant glycolipid sulfoquinovosyl diacylglycerol (SQDG). The biosynthesis of the latter is carried out by enzymes that exhibit structural homology to plant oxidoreductases and galactosyltransferases, yet their closest orthologs are found in bacteria. This hints that the capability of SQDG synthesis is more widespread in spirochaetes and other bacteria. Together, the comprehensive lipid profiling we report here uncovers novel aspects of the physiology of the metabolically challenged B. burgdorferi and highlights lipid acquisition and synthesis pathways as potentially critical for pathogen survival.
View details for DOI 10.64898/2026.05.22.727245
View details for PubMedID 42239376
View details for PubMedCentralID PMC13228262
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A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins.
Journal of chemical information and modeling
2025
Abstract
Lipids are essential metabolites that play critical roles in multiple cellular pathways. Like many primary metabolites, mutations that disrupt lipid synthesis can be lethal. Proteins involved in lipid synthesis, trafficking, and modification, are targets for therapeutic intervention in infectious disease and metabolic disorders. The ability to rapidly detect these proteins can accelerate their evaluation as targets for deranged lipid pathologies. However, it remains challenging to identify lipid binding motifs in proteins because the rules that govern protein engagement with specific lipids are poorly understood. As such, new bioinformatic tools that reveal conserved features in lipid binding proteins are necessary. Here, we present Structure-based Lipid-interacting Pocket Predictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. SLiPP uses a Random Forest classifier and operates at scale to predict lipid binding pockets with an accuracy of 96.8% and an F1 score of 86.9% when testing against a set of 8,380 pockets embedded within proteins. Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. SLiPP is fast and does not require substantial computational resources. Use of the algorithm to detect lipid binding proteins in various proteomes produced hits annotated or verified as bona fide lipid binding proteins. Additionally, SLiPP identified many new putative lipid binders in well studied proteomes. Because of its ability to identify novel lipid binding proteins, SLiPP can spur the discovery of new and "targetable" lipid-sensitive pathways.
View details for DOI 10.1021/acs.jcim.5c01076
View details for PubMedID 40906828
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Unveiling of a messenger: Gut microbes make a neuroactive signal.
Cell
2024; 187 (12): 2903-2904
Abstract
Gut microbes are known to impact host physiology in several ways. However, key molecular players in host-commensal interactions remain to be uncovered. In this issue of Cell, McCurry et al. reveal that gut bacteria perform 21-dehydroxylation to convert abundant biliary corticoids to neurosteroids using readily available H2 in their environment.
View details for DOI 10.1016/j.cell.2024.05.014
View details for PubMedID 38848674
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Rapid proteome-wide prediction of lipid-interacting proteins through ligand-guided structural genomics.
bioRxiv : the preprint server for biology
2024
Abstract
Lipids are primary metabolites that play essential roles in multiple cellular pathways. Alterations in lipid metabolism and transport are associated with infectious diseases and cancers. As such, proteins involved in lipid synthesis, trafficking, and modification, are targets for therapeutic intervention. The ability to rapidly detect these proteins can accelerate their biochemical and structural characterization. However, it remains challenging to identify lipid binding motifs in proteins due to a lack of conservation at the amino acids level. Therefore, new bioinformatic tools that can detect conserved features in lipid binding sites are necessary. Here, we present Structure-based Lipid-interacting Pocket Predictor (SLiPP), a structural bioinformatics algorithm that uses machine learning to detect protein cavities capable of binding to lipids in experimental and AlphaFold-predicted protein structures. SLiPP, which can be used at proteome-wide scales, predicts lipid binding pockets with an accuracy of 96.8% and a F1 score of 86.9%. Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. Use of the algorithm to detect lipid binding proteins in the proteomes of various bacteria, yeast, and human have produced hits annotated or verified as lipid binding proteins, and many other uncharacterized proteins whose functions are not discernable from sequence alone. Because of its ability to identify novel lipid binding proteins, SLiPP can spur the discovery of new lipid metabolic and trafficking pathways that can be targeted for therapeutic development.
View details for DOI 10.1101/2024.01.26.577452
View details for PubMedID 38352308
View details for PubMedCentralID PMC10862712