Academic Appointments

Contact

  • Academic
    poulami@stanford.edu
    Department: Chemistry Position: Physical Science Research Scientist

Additional Info

  • Mail Code: 5080

All Publications

  • Prediction and characterization of lipid-interacting proteins. Methods in enzymology Alfonso, S., Decosto, C. M., Chatterjee, P., Precord, T., Dassama, L. M. 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

  • A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins. Journal of chemical information and modeling Chou, J. C., Chatterjee, P., Decosto, C. M., Dassama, L. M. 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

  • Unveiling of a messenger: Gut microbes make a neuroactive signal. Cell Chatterjee, P., Dassama, L. M. 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

  • Rapid proteome-wide prediction of lipid-interacting proteins through ligand-guided structural genomics. bioRxiv : the preprint server for biology Chou, J. C., Decosto, C. M., Chatterjee, P., Dassama, L. M. 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